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+lambda0.02/hyperprior-featurecoding-8bit-individual/cls_in1ka/n04347754-0.001405_viaduct _ viaduct_0.8445672.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/hyperprior-featurecoding-8bit-individual/cls_in1ka/n07695742-0.003226_ant _ ant_0.64413536.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/hyperprior-featurecoding-8bit-individual/cls_in1ka/n04208210-0.007213_doormat _ doormat_0.81736016.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/hyperprior-featurecoding-8bit-individual/cls_in1ka/n04179913-0.006024_guacamole _ custard apple_0.5550306.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/hyperprior-featurecoding-8bit-individual/cls_in1ka/n07718472-0.001330_spatula _ spatula_0.5388231.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/hyperprior-featurecoding-8bit-individual/cls_in1ka/n04376876-0.004615_lighter _ lighter_0.69176143.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/hyperprior-featurecoding-8bit-individual/cls_in1ka/n04606251-0.003483_cliff _ cliff_0.9972174.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/hyperprior-featurecoding-8bit-individual/cls_in1ka/n04355338-0.000791_envelope _ envelope_0.9969598.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/hyperprior-featurecoding-8bit-individual/cls_in1ka/n04456115-0.002573_hair dryer _ envelope_0.34823084.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/hyperprior-featurecoding-8bit-individual/cls_in1ka/n07583066-0.003935_maraca _ maraca_0.5370013.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/hyperprior-featurecoding-8bit-individual/cls_in1kval/n02951358-ILSVRC2012_val_00000347.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/hyperprior-featurecoding-8bit-individual/cls_in1ka/n09229709-0.002628_stethoscope _ stethoscope_0.9726458.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/hyperprior-featurecoding-8bit-individual/cls_in1ka/n04532670-0.000670_spider web _ spider web_0.70711935.zst filter=lfs diff=lfs merge=lfs -text +lambda0.02/hyperprior-featurecoding-8bit-individual/cls_in1ka/n04509417-0.000350_sundial _ sundial_0.99936897.zst filter=lfs diff=lfs merge=lfs -text \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..5bd43701e6421cac4673c5c817ca44b4edc06202 --- /dev/null +++ b/README.md @@ -0,0 +1,7 @@ +--- +license: mit +tags: + - large-model-feature-coding +language: + - en +--- diff --git a/lambda0.001/elic-featurecoding-8bit-individual/cls_in1ka/dtufc_elic-featurecoding_dinov3-total_individual.log b/lambda0.001/elic-featurecoding-8bit-individual/cls_in1ka/dtufc_elic-featurecoding_dinov3-total_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..c39a75fb4e3081041a57f072b5838d8ad1ddc156 --- /dev/null +++ b/lambda0.001/elic-featurecoding-8bit-individual/cls_in1ka/dtufc_elic-featurecoding_dinov3-total_individual.log @@ -0,0 +1,9344 @@ +Experiment: dtufc_elic-featurecoding_dinov3-total_individual +Log file: output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/dtufc_elic-featurecoding_dinov3-total_individual.log +DTUFCCodecConfig: + arch: elic-featurecoding + handler: dinov3-total + checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.001_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.001_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 286 +Loaded elic-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.9' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.19' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.29' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.39' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json +Loaded per-key mappings: model=dinov3-total + Keys: ['layer.9', 'layer.19', 'layer.29', 'layer.39'] +---------------- -------------------------------------------------------------------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +Checkpoint codec_weights/elic_hybrid/elic2022-official_lambda0.001_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-DINOv3/imagenet1k-a +Output output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka +---------------- -------------------------------------------------------------------------------------------------------------------- +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01498041-0.006639_koala _ American bullfrog_0.6658246.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01498041-0.006639_koala _ American bullfrog_0.6658246.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,332B, BPFP=0.0234 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 44,436B, BPFP=0.0843 +⌛️ [2/4] FRONTEND: Frontend time: 2.877s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.544s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09594801 13.72008815 + layer.39.0 58.94484178 15960.91642371 + ------------------------------------------------------------------------------------- + TOTAL 29.52039490 7987.31825593 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 56768 +BPFP 0.0539 bits/point +EBPFP 0.0539 equivalent bits/point +MSE 7987.318256 +---------------------- -------------------------------------------------------- +Time: 5.493s Load: 0.072s, Pack+Encode: 2.877s, Decode+Unpack: 2.544s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7987.3183 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01498041-0.006639_koala _ American bullfrog_0.6658246.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n01498041-0.006639_koala _ American bullfrog_0.6658246.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01531178-0.000765_fox squirrel _ ant_0.9486805.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01531178-0.000765_fox squirrel _ ant_0.9486805.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,080B, BPFP=0.0229 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 28,284B, BPFP=0.0537 +⌛️ [2/4] FRONTEND: Frontend time: 2.583s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.454s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09773727 13.19220534 + layer.39.0 17.17825445 16165.17395530 + ------------------------------------------------------------------------------------- + TOTAL 8.63799586 8089.18308032 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 40364 +BPFP 0.0383 bits/point +EBPFP 0.0383 equivalent bits/point +MSE 8089.183080 +---------------------- -------------------------------------------------------- +Time: 5.105s Load: 0.069s, Pack+Encode: 2.583s, Decode+Unpack: 2.454s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8089.1831 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01531178-0.000765_fox squirrel _ ant_0.9486805.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n01531178-0.000765_fox squirrel _ ant_0.9486805.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01534433-0.004573_stingray _ stingray_0.97124094.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01534433-0.004573_stingray _ stingray_0.97124094.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 11,864B, BPFP=0.0225 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 28,136B, BPFP=0.0534 +⌛️ [2/4] FRONTEND: Frontend time: 2.555s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.453s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09515371 14.02985871 + layer.39.0 6.87362484 15133.43051506 + ------------------------------------------------------------------------------------- + TOTAL 3.48438928 7573.73018689 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 40000 +BPFP 0.0380 bits/point +EBPFP 0.0380 equivalent bits/point +MSE 7573.730187 +---------------------- -------------------------------------------------------- +Time: 5.061s Load: 0.052s, Pack+Encode: 2.555s, Decode+Unpack: 2.453s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7573.7302 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01534433-0.004573_stingray _ stingray_0.97124094.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n01534433-0.004573_stingray _ stingray_0.97124094.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01558993-0.000522_bow _ bow_0.9033333.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01558993-0.000522_bow _ bow_0.9033333.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 14,832B, BPFP=0.0282 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 27,972B, BPFP=0.0531 +⌛️ [2/4] FRONTEND: Frontend time: 2.563s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.470s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09874929 13.39521741 + layer.39.0 7.31778236 15243.88435374 + ------------------------------------------------------------------------------------- + TOTAL 3.70826583 7628.63978557 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 42804 +BPFP 0.0406 bits/point +EBPFP 0.0406 equivalent bits/point +MSE 7628.639786 +---------------------- -------------------------------------------------------- +Time: 5.084s Load: 0.051s, Pack+Encode: 2.563s, Decode+Unpack: 2.470s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7628.6398 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01558993-0.000522_bow _ bow_0.9033333.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n01558993-0.000522_bow _ bow_0.9033333.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01580077-0.004583_African bush elephant _ African bush elephant_0.59939015.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01580077-0.004583_African bush elephant _ African bush elephant_0.59939015.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 14,184B, BPFP=0.0269 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 35,192B, BPFP=0.0668 +⌛️ [2/4] FRONTEND: Frontend time: 2.566s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.463s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10720986 13.48746602 + layer.39.0 24.46209533 16482.91156463 + ------------------------------------------------------------------------------------- + TOTAL 12.28465260 8248.19951533 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 49376 +BPFP 0.0469 bits/point +EBPFP 0.0469 equivalent bits/point +MSE 8248.199515 +---------------------- -------------------------------------------------------- +Time: 5.082s Load: 0.052s, Pack+Encode: 2.566s, Decode+Unpack: 2.463s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8248.1995 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01580077-0.004583_African bush elephant _ African bush elephant_0.59939015.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n01580077-0.004583_African bush elephant _ African bush elephant_0.59939015.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01614925-0.049724_white-headed capuchin _ white-headed capuchin_0.9309422.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01614925-0.049724_white-headed capuchin _ white-headed capuchin_0.9309422.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 15,436B, BPFP=0.0293 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 25,472B, BPFP=0.0483 +⌛️ [2/4] FRONTEND: Frontend time: 2.566s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.476s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09739119 13.57643666 + layer.39.0 8.81423010 16728.30709427 + ------------------------------------------------------------------------------------- + TOTAL 4.45581065 8370.94176546 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 40908 +BPFP 0.0388 bits/point +EBPFP 0.0388 equivalent bits/point +MSE 8370.941765 +---------------------- -------------------------------------------------------- +Time: 5.113s Load: 0.071s, Pack+Encode: 2.566s, Decode+Unpack: 2.476s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8370.9418 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01614925-0.049724_white-headed capuchin _ white-headed capuchin_0.9309422.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n01614925-0.049724_white-headed capuchin _ white-headed capuchin_0.9309422.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01631663-0.004606_American bullfrog _ American bullfrog_0.8789855.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01631663-0.004606_American bullfrog _ American bullfrog_0.8789855.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,216B, BPFP=0.0232 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 28,512B, BPFP=0.0541 +⌛️ [2/4] FRONTEND: Frontend time: 2.566s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.464s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09716670 13.76039104 + layer.39.0 20.45897868 15421.03012634 + ------------------------------------------------------------------------------------- + TOTAL 10.27807269 7717.39525869 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 40728 +BPFP 0.0387 bits/point +EBPFP 0.0387 equivalent bits/point +MSE 7717.395259 +---------------------- -------------------------------------------------------- +Time: 5.090s Load: 0.061s, Pack+Encode: 2.566s, Decode+Unpack: 2.464s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7717.3953 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01631663-0.004606_American bullfrog _ American bullfrog_0.8789855.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n01631663-0.004606_American bullfrog _ American bullfrog_0.8789855.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01669191-0.029754_sandal _ sandal_0.38198605.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01669191-0.029754_sandal _ sandal_0.38198605.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.058s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 17,488B, BPFP=0.0332 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 33,304B, BPFP=0.0632 +⌛️ [2/4] FRONTEND: Frontend time: 2.565s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.469s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10877632 13.41715447 + layer.39.0 13.16500205 15066.54033042 + ------------------------------------------------------------------------------------- + TOTAL 6.63688918 7539.97874245 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50792 +BPFP 0.0482 bits/point +EBPFP 0.0482 equivalent bits/point +MSE 7539.978742 +---------------------- -------------------------------------------------------- +Time: 5.093s Load: 0.058s, Pack+Encode: 2.565s, Decode+Unpack: 2.469s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7539.9787 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01669191-0.029754_sandal _ sandal_0.38198605.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n01669191-0.029754_sandal _ sandal_0.38198605.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770081-0.000571_syringe _ syringe_0.7369336.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770081-0.000571_syringe _ syringe_0.7369336.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 11,824B, BPFP=0.0224 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 41,912B, BPFP=0.0796 +⌛️ [2/4] FRONTEND: Frontend time: 2.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.500s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09508557 13.81114952 + layer.39.0 60.03878538 17850.98347911 + ------------------------------------------------------------------------------------- + TOTAL 30.06693547 8932.39731431 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 53736 +BPFP 0.0510 bits/point +EBPFP 0.0510 equivalent bits/point +MSE 8932.397314 +---------------------- -------------------------------------------------------- +Time: 5.130s Load: 0.050s, Pack+Encode: 2.581s, Decode+Unpack: 2.500s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8932.3973 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770081-0.000571_syringe _ syringe_0.7369336.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n01770081-0.000571_syringe _ syringe_0.7369336.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770393-0.000908_harvestman _ harvestman_0.8782497.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770393-0.000908_harvestman _ harvestman_0.8782497.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.079s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,440B, BPFP=0.0236 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 23,368B, BPFP=0.0444 +⌛️ [2/4] FRONTEND: Frontend time: 2.558s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.491s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11350316 13.40585900 + layer.39.0 19.73148992 16157.40330418 + ------------------------------------------------------------------------------------- + TOTAL 9.92249654 8085.40458159 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 35808 +BPFP 0.0340 bits/point +EBPFP 0.0340 equivalent bits/point +MSE 8085.404582 +---------------------- -------------------------------------------------------- +Time: 5.128s Load: 0.079s, Pack+Encode: 2.558s, Decode+Unpack: 2.491s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8085.4046 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770393-0.000908_harvestman _ harvestman_0.8782497.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n01770393-0.000908_harvestman _ harvestman_0.8782497.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01784675-0.027853_syringe _ syringe_0.9584382.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01784675-0.027853_syringe _ syringe_0.9584382.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 16,036B, BPFP=0.0304 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 71,884B, BPFP=0.1364 +⌛️ [2/4] FRONTEND: Frontend time: 2.574s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.488s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11002613 13.48714145 + layer.39.0 26.08665877 15410.38872692 + ------------------------------------------------------------------------------------- + TOTAL 13.09834245 7711.93793419 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 87920 +BPFP 0.0834 bits/point +EBPFP 0.0834 equivalent bits/point +MSE 7711.937934 +---------------------- -------------------------------------------------------- +Time: 5.132s Load: 0.070s, Pack+Encode: 2.574s, Decode+Unpack: 2.488s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7711.9379 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01784675-0.027853_syringe _ syringe_0.9584382.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n01784675-0.027853_syringe _ syringe_0.9584382.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01819313-0.053742_koala _ koala_0.98647016.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01819313-0.053742_koala _ koala_0.98647016.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.081s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 14,968B, BPFP=0.0284 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 45,648B, BPFP=0.0866 +⌛️ [2/4] FRONTEND: Frontend time: 2.577s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.491s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14565475 13.81129187 + layer.39.0 25.01023445 21152.27793975 + ------------------------------------------------------------------------------------- + TOTAL 12.57794460 10583.04461581 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 60616 +BPFP 0.0575 bits/point +EBPFP 0.0575 equivalent bits/point +MSE 10583.044616 +---------------------- -------------------------------------------------------- +Time: 5.150s Load: 0.081s, Pack+Encode: 2.577s, Decode+Unpack: 2.491s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10583.0446 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01819313-0.053742_koala _ koala_0.98647016.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n01819313-0.053742_koala _ koala_0.98647016.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01820546-0.012522_toucan _ toucan_0.63882655.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01820546-0.012522_toucan _ toucan_0.63882655.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 11,708B, BPFP=0.0222 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,544B, BPFP=0.0618 +⌛️ [2/4] FRONTEND: Frontend time: 2.586s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.494s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09696376 13.72625596 + layer.39.0 16.65489097 15296.00000000 + ------------------------------------------------------------------------------------- + TOTAL 8.37592737 7654.86312798 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 44252 +BPFP 0.0420 bits/point +EBPFP 0.0420 equivalent bits/point +MSE 7654.863128 +---------------------- -------------------------------------------------------- +Time: 5.130s Load: 0.050s, Pack+Encode: 2.586s, Decode+Unpack: 2.494s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7654.8631 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01820546-0.012522_toucan _ toucan_0.63882655.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n01820546-0.012522_toucan _ toucan_0.63882655.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01833805-0.013248_toucan _ lorikeet_0.77773976.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01833805-0.013248_toucan _ lorikeet_0.77773976.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.057s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 11,308B, BPFP=0.0215 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 20,596B, BPFP=0.0391 +⌛️ [2/4] FRONTEND: Frontend time: 2.561s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.492s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09866240 13.43281269 + layer.39.0 7.67772963 14311.34693878 + ------------------------------------------------------------------------------------- + TOTAL 3.88819601 7162.38987573 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 31904 +BPFP 0.0303 bits/point +EBPFP 0.0303 equivalent bits/point +MSE 7162.389876 +---------------------- -------------------------------------------------------- +Time: 5.109s Load: 0.057s, Pack+Encode: 2.561s, Decode+Unpack: 2.492s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7162.3899 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01833805-0.013248_toucan _ lorikeet_0.77773976.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n01833805-0.013248_toucan _ lorikeet_0.77773976.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01843383-0.013605_white-headed capuchin _ white-headed capuchin_0.9984768.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01843383-0.013605_white-headed capuchin _ white-headed capuchin_0.9984768.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 14,580B, BPFP=0.0277 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 36,528B, BPFP=0.0693 +⌛️ [2/4] FRONTEND: Frontend time: 2.596s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.480s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11910487 13.60692742 + layer.39.0 9.20068692 14563.32167153 + ------------------------------------------------------------------------------------- + TOTAL 4.65989589 7288.46429948 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51108 +BPFP 0.0485 bits/point +EBPFP 0.0485 equivalent bits/point +MSE 7288.464299 +---------------------- -------------------------------------------------------- +Time: 5.128s Load: 0.052s, Pack+Encode: 2.596s, Decode+Unpack: 2.480s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7288.4643 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01843383-0.013605_white-headed capuchin _ white-headed capuchin_0.9984768.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n01843383-0.013605_white-headed capuchin _ white-headed capuchin_0.9984768.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01924916-0.000644_jay _ jay_0.82223135.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01924916-0.000644_jay _ jay_0.82223135.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 14,920B, BPFP=0.0283 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,340B, BPFP=0.0576 +⌛️ [2/4] FRONTEND: Frontend time: 2.592s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.504s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11488669 13.45061289 + layer.39.0 141.08750911 16361.75315841 + ------------------------------------------------------------------------------------- + TOTAL 70.60119790 8187.60188565 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 45260 +BPFP 0.0430 bits/point +EBPFP 0.0430 equivalent bits/point +MSE 8187.601886 +---------------------- -------------------------------------------------------- +Time: 5.147s Load: 0.050s, Pack+Encode: 2.592s, Decode+Unpack: 2.504s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8187.6019 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01924916-0.000644_jay _ jay_0.82223135.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n01924916-0.000644_jay _ jay_0.82223135.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01944390-0.002567_American robin _ American robin_0.5629079.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01944390-0.002567_American robin _ American robin_0.5629079.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.081s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,480B, BPFP=0.0237 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 20,288B, BPFP=0.0385 +⌛️ [2/4] FRONTEND: Frontend time: 2.576s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.489s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10732387 13.20689459 + layer.39.0 16.74672581 15424.84159378 + ------------------------------------------------------------------------------------- + TOTAL 8.42702484 7719.02424418 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 32768 +BPFP 0.0311 bits/point +EBPFP 0.0311 equivalent bits/point +MSE 7719.024244 +---------------------- -------------------------------------------------------- +Time: 5.146s Load: 0.081s, Pack+Encode: 2.576s, Decode+Unpack: 2.489s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7719.0242 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01944390-0.002567_American robin _ American robin_0.5629079.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n01944390-0.002567_American robin _ American robin_0.5629079.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01985128-0.001579_centipede _ centipede_0.85936093.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01985128-0.001579_centipede _ centipede_0.85936093.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,992B, BPFP=0.0266 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 38,028B, BPFP=0.0722 +⌛️ [2/4] FRONTEND: Frontend time: 2.587s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.484s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09645609 13.47962221 + layer.39.0 23.47999613 16076.60058309 + ------------------------------------------------------------------------------------- + TOTAL 11.78822611 8045.04010265 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52020 +BPFP 0.0494 bits/point +EBPFP 0.0494 equivalent bits/point +MSE 8045.040103 +---------------------- -------------------------------------------------------- +Time: 5.141s Load: 0.070s, Pack+Encode: 2.587s, Decode+Unpack: 2.484s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8045.0401 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01985128-0.001579_centipede _ centipede_0.85936093.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n01985128-0.001579_centipede _ centipede_0.85936093.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02037110-0.003503_snowmobile _ snowmobile_0.5200631.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02037110-0.003503_snowmobile _ snowmobile_0.5200631.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.081s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,184B, BPFP=0.0250 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,436B, BPFP=0.0578 +⌛️ [2/4] FRONTEND: Frontend time: 2.601s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.501s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09471867 13.70445555 + layer.39.0 17.04498261 14287.14091351 + ------------------------------------------------------------------------------------- + TOTAL 8.56985064 7150.42268453 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 43620 +BPFP 0.0414 bits/point +EBPFP 0.0414 equivalent bits/point +MSE 7150.422685 +---------------------- -------------------------------------------------------- +Time: 5.183s Load: 0.081s, Pack+Encode: 2.601s, Decode+Unpack: 2.501s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7150.4227 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02037110-0.003503_snowmobile _ snowmobile_0.5200631.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n02037110-0.003503_snowmobile _ snowmobile_0.5200631.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02123394-0.015363_marmot _ marmot_0.82052565.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02123394-0.015363_marmot _ marmot_0.82052565.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 11,940B, BPFP=0.0227 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,296B, BPFP=0.0594 +⌛️ [2/4] FRONTEND: Frontend time: 2.593s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.484s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10209646 13.41972068 + layer.39.0 11.38238543 14101.29446064 + ------------------------------------------------------------------------------------- + TOTAL 5.74224095 7057.35709066 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 43236 +BPFP 0.0410 bits/point +EBPFP 0.0410 equivalent bits/point +MSE 7057.357091 +---------------------- -------------------------------------------------------- +Time: 5.147s Load: 0.070s, Pack+Encode: 2.593s, Decode+Unpack: 2.484s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7057.3571 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02123394-0.015363_marmot _ marmot_0.82052565.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n02123394-0.015363_marmot _ marmot_0.82052565.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02165456-0.000157_corn _ corn_0.9868978.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02165456-0.000157_corn _ corn_0.9868978.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,728B, BPFP=0.0242 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 33,800B, BPFP=0.0642 +⌛️ [2/4] FRONTEND: Frontend time: 2.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.477s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10346756 13.35081181 + layer.39.0 776.17699223 16657.47133139 + ------------------------------------------------------------------------------------- + TOTAL 388.14022989 8335.41107160 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 46528 +BPFP 0.0442 bits/point +EBPFP 0.0442 equivalent bits/point +MSE 8335.411072 +---------------------- -------------------------------------------------------- +Time: 5.110s Load: 0.052s, Pack+Encode: 2.581s, Decode+Unpack: 2.477s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8335.4111 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02165456-0.000157_corn _ corn_0.9868978.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n02165456-0.000157_corn _ corn_0.9868978.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02219486-0.000060_cliff _ cliff_0.99684334.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02219486-0.000060_cliff _ cliff_0.99684334.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.057s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,244B, BPFP=0.0251 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,072B, BPFP=0.0590 +⌛️ [2/4] FRONTEND: Frontend time: 2.594s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.484s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09584527 13.49059691 + layer.39.0 31.94620460 16477.29251701 + ------------------------------------------------------------------------------------- + TOTAL 16.02102494 8245.39155696 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 44316 +BPFP 0.0421 bits/point +EBPFP 0.0421 equivalent bits/point +MSE 8245.391557 +---------------------- -------------------------------------------------------- +Time: 5.135s Load: 0.057s, Pack+Encode: 2.594s, Decode+Unpack: 2.484s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8245.3916 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02219486-0.000060_cliff _ cliff_0.99684334.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n02219486-0.000060_cliff _ cliff_0.99684334.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02226429-0.000085_stick insect _ stick insect_0.99900275.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02226429-0.000085_stick insect _ stick insect_0.99900275.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.081s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,100B, BPFP=0.0230 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 53,552B, BPFP=0.1016 +⌛️ [2/4] FRONTEND: Frontend time: 2.595s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.492s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09547379 13.52819789 + layer.39.0 19.16722850 11884.56268222 + ------------------------------------------------------------------------------------- + TOTAL 9.63135114 5949.04544005 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 65652 +BPFP 0.0623 bits/point +EBPFP 0.0623 equivalent bits/point +MSE 5949.045440 +---------------------- -------------------------------------------------------- +Time: 5.169s Load: 0.081s, Pack+Encode: 2.595s, Decode+Unpack: 2.492s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 5949.0454 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02226429-0.000085_stick insect _ stick insect_0.99900275.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n02226429-0.000085_stick insect _ stick insect_0.99900275.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02231487-0.000080_manhole cover _ manhole cover_0.7554716.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02231487-0.000080_manhole cover _ manhole cover_0.7554716.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 11,696B, BPFP=0.0222 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 33,180B, BPFP=0.0630 +⌛️ [2/4] FRONTEND: Frontend time: 2.610s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.520s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09512618 13.61792775 + layer.39.0 210.79875790 18195.39552964 + ------------------------------------------------------------------------------------- + TOTAL 105.44694204 9104.50672870 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 44876 +BPFP 0.0426 bits/point +EBPFP 0.0426 equivalent bits/point +MSE 9104.506729 +---------------------- -------------------------------------------------------- +Time: 5.182s Load: 0.052s, Pack+Encode: 2.610s, Decode+Unpack: 2.520s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9104.5067 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02231487-0.000080_manhole cover _ manhole cover_0.7554716.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n02231487-0.000080_manhole cover _ manhole cover_0.7554716.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02233338-0.002901_manhole cover _ manhole cover_0.69458175.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02233338-0.002901_manhole cover _ manhole cover_0.69458175.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 11,600B, BPFP=0.0220 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 41,668B, BPFP=0.0791 +⌛️ [2/4] FRONTEND: Frontend time: 2.598s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.490s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09539769 13.71523191 + layer.39.0 58.97704841 17279.68707483 + ------------------------------------------------------------------------------------- + TOTAL 29.53622305 8646.70115337 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 53268 +BPFP 0.0506 bits/point +EBPFP 0.0506 equivalent bits/point +MSE 8646.701153 +---------------------- -------------------------------------------------------- +Time: 5.157s Load: 0.069s, Pack+Encode: 2.598s, Decode+Unpack: 2.490s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8646.7012 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02233338-0.002901_manhole cover _ manhole cover_0.69458175.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n02233338-0.002901_manhole cover _ manhole cover_0.69458175.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02236044-0.000522_sundial _ sundial_0.96381366.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02236044-0.000522_sundial _ sundial_0.96381366.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,468B, BPFP=0.0237 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,452B, BPFP=0.0578 +⌛️ [2/4] FRONTEND: Frontend time: 2.584s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.493s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09795647 13.28743395 + layer.39.0 53.12385356 15490.91933916 + ------------------------------------------------------------------------------------- + TOTAL 26.61090502 7752.10338656 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 42920 +BPFP 0.0407 bits/point +EBPFP 0.0407 equivalent bits/point +MSE 7752.103387 +---------------------- -------------------------------------------------------- +Time: 5.147s Load: 0.070s, Pack+Encode: 2.584s, Decode+Unpack: 2.493s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7752.1034 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02236044-0.000522_sundial _ sundial_0.96381366.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n02236044-0.000522_sundial _ sundial_0.96381366.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02259212-0.000032_chain _ chain_0.6590295.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02259212-0.000032_chain _ chain_0.6590295.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 11,716B, BPFP=0.0222 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 49,532B, BPFP=0.0940 +⌛️ [2/4] FRONTEND: Frontend time: 2.600s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.507s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09523673 13.46380265 + layer.39.0 80.66082058 16102.17395530 + ------------------------------------------------------------------------------------- + TOTAL 40.37802865 8057.81887897 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 61248 +BPFP 0.0581 bits/point +EBPFP 0.0581 equivalent bits/point +MSE 8057.818879 +---------------------- -------------------------------------------------------- +Time: 5.187s Load: 0.080s, Pack+Encode: 2.600s, Decode+Unpack: 2.507s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8057.8189 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02259212-0.000032_chain _ chain_0.6590295.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n02259212-0.000032_chain _ chain_0.6590295.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02279972-0.000576_apron _ apron_0.7661352.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02279972-0.000576_apron _ apron_0.7661352.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,260B, BPFP=0.0385 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 54,532B, BPFP=0.1035 +⌛️ [2/4] FRONTEND: Frontend time: 2.613s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.487s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12772729 13.81996420 + layer.39.0 1038.59135083 19654.15549077 + ------------------------------------------------------------------------------------- + TOTAL 519.35953906 9833.98772748 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 74792 +BPFP 0.0710 bits/point +EBPFP 0.0710 equivalent bits/point +MSE 9833.987727 +---------------------- -------------------------------------------------------- +Time: 5.152s Load: 0.052s, Pack+Encode: 2.613s, Decode+Unpack: 2.487s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9833.9877 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02279972-0.000576_apron _ apron_0.7661352.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n02279972-0.000576_apron _ apron_0.7661352.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02280649-0.001599_custard apple _ leafhopper_0.9128452.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02280649-0.001599_custard apple _ leafhopper_0.9128452.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,056B, BPFP=0.0229 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 56,676B, BPFP=0.1076 +⌛️ [2/4] FRONTEND: Frontend time: 2.594s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.498s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09488542 13.88461473 + layer.39.0 1031.59973275 15821.31681244 + ------------------------------------------------------------------------------------- + TOTAL 515.84730909 7917.60071358 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 68732 +BPFP 0.0652 bits/point +EBPFP 0.0652 equivalent bits/point +MSE 7917.600714 +---------------------- -------------------------------------------------------- +Time: 5.172s Load: 0.080s, Pack+Encode: 2.594s, Decode+Unpack: 2.498s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7917.6007 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02280649-0.001599_custard apple _ leafhopper_0.9128452.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n02280649-0.001599_custard apple _ leafhopper_0.9128452.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02317335-0.033681_flatworm _ flatworm_0.6070924.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02317335-0.033681_flatworm _ flatworm_0.6070924.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.076s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,676B, BPFP=0.0241 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 42,680B, BPFP=0.0810 +⌛️ [2/4] FRONTEND: Frontend time: 2.582s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.519s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09575805 13.69216169 + layer.39.0 62.35741238 15712.66666667 + ------------------------------------------------------------------------------------- + TOTAL 31.22658522 7863.17941418 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 55356 +BPFP 0.0525 bits/point +EBPFP 0.0525 equivalent bits/point +MSE 7863.179414 +---------------------- -------------------------------------------------------- +Time: 5.178s Load: 0.076s, Pack+Encode: 2.582s, Decode+Unpack: 2.519s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7863.1794 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02317335-0.033681_flatworm _ flatworm_0.6070924.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n02317335-0.033681_flatworm _ flatworm_0.6070924.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02325366-0.000350_flatworm _ flatworm_0.87634724.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02325366-0.000350_flatworm _ flatworm_0.87634724.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.082s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,816B, BPFP=0.0243 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 28,220B, BPFP=0.0536 +⌛️ [2/4] FRONTEND: Frontend time: 2.568s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.489s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09712043 13.44304619 + layer.39.0 30.59439155 15811.38289602 + ------------------------------------------------------------------------------------- + TOTAL 15.34575599 7912.41297110 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 41036 +BPFP 0.0389 bits/point +EBPFP 0.0389 equivalent bits/point +MSE 7912.412971 +---------------------- -------------------------------------------------------- +Time: 5.140s Load: 0.082s, Pack+Encode: 2.568s, Decode+Unpack: 2.489s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7912.4130 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02325366-0.000350_flatworm _ flatworm_0.87634724.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n02325366-0.000350_flatworm _ flatworm_0.87634724.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02346627-0.011107_fountain _ skunk_0.28641737.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02346627-0.011107_fountain _ skunk_0.28641737.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 14,520B, BPFP=0.0276 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,528B, BPFP=0.0579 +⌛️ [2/4] FRONTEND: Frontend time: 2.561s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.484s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09705289 13.65833030 + layer.39.0 9.52721088 16379.12147716 + ------------------------------------------------------------------------------------- + TOTAL 4.81213189 8196.38990373 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 45048 +BPFP 0.0428 bits/point +EBPFP 0.0428 equivalent bits/point +MSE 8196.389904 +---------------------- -------------------------------------------------------- +Time: 5.114s Load: 0.069s, Pack+Encode: 2.561s, Decode+Unpack: 2.484s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8196.3899 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02346627-0.011107_fountain _ skunk_0.28641737.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n02346627-0.011107_fountain _ skunk_0.28641737.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02445715-0.036432_shovel _ tarantula_0.26785344.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02445715-0.036432_shovel _ tarantula_0.26785344.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.082s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 14,244B, BPFP=0.0270 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 26,992B, BPFP=0.0512 +⌛️ [2/4] FRONTEND: Frontend time: 2.562s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.501s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09708806 13.22743277 + layer.39.0 8.00606437 14596.39844509 + ------------------------------------------------------------------------------------- + TOTAL 4.05157622 7304.81293893 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 41236 +BPFP 0.0391 bits/point +EBPFP 0.0391 equivalent bits/point +MSE 7304.812939 +---------------------- -------------------------------------------------------- +Time: 5.145s Load: 0.082s, Pack+Encode: 2.562s, Decode+Unpack: 2.501s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7304.8129 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02445715-0.036432_shovel _ tarantula_0.26785344.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n02445715-0.036432_shovel _ tarantula_0.26785344.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02454379-0.082010_koala _ koala_0.7052893.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02454379-0.082010_koala _ koala_0.7052893.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 18,924B, BPFP=0.0359 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 27,392B, BPFP=0.0520 +⌛️ [2/4] FRONTEND: Frontend time: 2.565s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.486s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14585212 14.00232325 + layer.39.0 44.19989826 15302.59863946 + ------------------------------------------------------------------------------------- + TOTAL 22.17287519 7658.30048135 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 46316 +BPFP 0.0440 bits/point +EBPFP 0.0440 equivalent bits/point +MSE 7658.300481 +---------------------- -------------------------------------------------------- +Time: 5.121s Load: 0.070s, Pack+Encode: 2.565s, Decode+Unpack: 2.486s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7658.3005 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02454379-0.082010_koala _ koala_0.7052893.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n02454379-0.082010_koala _ koala_0.7052893.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02782093-0.007542_American alligator _ American alligator_0.49872985.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02782093-0.007542_American alligator _ American alligator_0.49872985.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.079s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 11,752B, BPFP=0.0223 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,880B, BPFP=0.0605 +⌛️ [2/4] FRONTEND: Frontend time: 2.571s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.503s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09848133 13.51232898 + layer.39.0 9.18780844 14792.40330418 + ------------------------------------------------------------------------------------- + TOTAL 4.64314488 7402.95781658 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 43632 +BPFP 0.0414 bits/point +EBPFP 0.0414 equivalent bits/point +MSE 7402.957817 +---------------------- -------------------------------------------------------- +Time: 5.153s Load: 0.079s, Pack+Encode: 2.571s, Decode+Unpack: 2.503s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7402.9578 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02782093-0.007542_American alligator _ American alligator_0.49872985.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n02782093-0.007542_American alligator _ American alligator_0.49872985.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02787622-0.004599_marimba _ accordion_0.25991488.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02787622-0.004599_marimba _ accordion_0.25991488.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 14,876B, BPFP=0.0282 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 51,860B, BPFP=0.0984 +⌛️ [2/4] FRONTEND: Frontend time: 2.571s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.507s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12856446 13.48780198 + layer.39.0 1004.59450923 16909.69290573 + ------------------------------------------------------------------------------------- + TOTAL 502.36153685 8461.59035386 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 66736 +BPFP 0.0633 bits/point +EBPFP 0.0633 equivalent bits/point +MSE 8461.590354 +---------------------- -------------------------------------------------------- +Time: 5.158s Load: 0.080s, Pack+Encode: 2.571s, Decode+Unpack: 2.507s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8461.5904 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02787622-0.004599_marimba _ accordion_0.25991488.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n02787622-0.004599_marimba _ accordion_0.25991488.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02793495-0.000130_flatworm _ stingray_0.5528234.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02793495-0.000130_flatworm _ stingray_0.5528234.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,424B, BPFP=0.0255 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 29,740B, BPFP=0.0564 +⌛️ [2/4] FRONTEND: Frontend time: 2.601s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.483s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09706621 13.54336925 + layer.39.0 8.05872662 14453.33236152 + ------------------------------------------------------------------------------------- + TOTAL 4.07789641 7233.43786538 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 43164 +BPFP 0.0410 bits/point +EBPFP 0.0410 equivalent bits/point +MSE 7233.437865 +---------------------- -------------------------------------------------------- +Time: 5.135s Load: 0.052s, Pack+Encode: 2.601s, Decode+Unpack: 2.483s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7233.4379 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02793495-0.000130_flatworm _ stingray_0.5528234.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n02793495-0.000130_flatworm _ stingray_0.5528234.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02797295-0.002775_hair dryer _ box turtle_0.2407761.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02797295-0.002775_hair dryer _ box turtle_0.2407761.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.081s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,444B, BPFP=0.0255 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 56,816B, BPFP=0.1078 +⌛️ [2/4] FRONTEND: Frontend time: 2.585s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.479s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11161610 13.67862457 + layer.39.0 373.09438776 16512.30126336 + ------------------------------------------------------------------------------------- + TOTAL 186.60300193 8262.98994397 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 70260 +BPFP 0.0667 bits/point +EBPFP 0.0667 equivalent bits/point +MSE 8262.989944 +---------------------- -------------------------------------------------------- +Time: 5.144s Load: 0.081s, Pack+Encode: 2.585s, Decode+Unpack: 2.479s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8262.9899 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02797295-0.002775_hair dryer _ box turtle_0.2407761.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n02797295-0.002775_hair dryer _ box turtle_0.2407761.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02814860-0.006340_fountain _ fountain_0.7891514.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02814860-0.006340_fountain _ fountain_0.7891514.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,756B, BPFP=0.0242 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 26,664B, BPFP=0.0506 +⌛️ [2/4] FRONTEND: Frontend time: 2.566s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.488s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.04615183 13.89740494 + layer.39.0 7.48662090 14790.04275996 + ------------------------------------------------------------------------------------- + TOTAL 7.76638637 7401.97008245 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 39420 +BPFP 0.0374 bits/point +EBPFP 0.0374 equivalent bits/point +MSE 7401.970082 +---------------------- -------------------------------------------------------- +Time: 5.125s Load: 0.072s, Pack+Encode: 2.566s, Decode+Unpack: 2.488s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7401.9701 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02814860-0.006340_fountain _ fountain_0.7891514.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n02814860-0.006340_fountain _ fountain_0.7891514.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02879718-0.003578_maraca _ maraca_0.6809677.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02879718-0.003578_maraca _ maraca_0.6809677.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.081s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,820B, BPFP=0.0243 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 43,120B, BPFP=0.0818 +⌛️ [2/4] FRONTEND: Frontend time: 2.574s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.497s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10989876 13.68593408 + layer.39.0 33.03751367 16903.82701652 + ------------------------------------------------------------------------------------- + TOTAL 16.57370621 8458.75647530 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 55940 +BPFP 0.0531 bits/point +EBPFP 0.0531 equivalent bits/point +MSE 8458.756475 +---------------------- -------------------------------------------------------- +Time: 5.152s Load: 0.081s, Pack+Encode: 2.574s, Decode+Unpack: 2.497s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8458.7565 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02879718-0.003578_maraca _ maraca_0.6809677.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n02879718-0.003578_maraca _ maraca_0.6809677.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02883205-0.000262_syringe _ syringe_0.7098205.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02883205-0.000262_syringe _ syringe_0.7098205.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,740B, BPFP=0.0242 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,604B, BPFP=0.0619 +⌛️ [2/4] FRONTEND: Frontend time: 2.567s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.487s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09610580 13.73510007 + layer.39.0 8.14318931 15892.15063168 + ------------------------------------------------------------------------------------- + TOTAL 4.11964755 7952.94286587 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 45344 +BPFP 0.0430 bits/point +EBPFP 0.0430 equivalent bits/point +MSE 7952.942866 +---------------------- -------------------------------------------------------- +Time: 5.124s Load: 0.070s, Pack+Encode: 2.567s, Decode+Unpack: 2.487s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7952.9429 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02883205-0.000262_syringe _ syringe_0.7098205.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n02883205-0.000262_syringe _ syringe_0.7098205.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02906734-0.000163_stethoscope _ stethoscope_0.9290994.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02906734-0.000163_stethoscope _ stethoscope_0.9290994.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 14,548B, BPFP=0.0276 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 51,628B, BPFP=0.0980 +⌛️ [2/4] FRONTEND: Frontend time: 2.576s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.483s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12024398 13.47563434 + layer.39.0 47.23105336 15828.01554908 + ------------------------------------------------------------------------------------- + TOTAL 23.67564867 7920.74559171 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 66176 +BPFP 0.0628 bits/point +EBPFP 0.0628 equivalent bits/point +MSE 7920.745592 +---------------------- -------------------------------------------------------- +Time: 5.129s Load: 0.070s, Pack+Encode: 2.576s, Decode+Unpack: 2.483s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7920.7456 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02906734-0.000163_stethoscope _ stethoscope_0.9290994.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n02906734-0.000163_stethoscope _ stethoscope_0.9290994.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02948072-0.002303_digital clock _ digital clock_0.928374.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02948072-0.002303_digital clock _ digital clock_0.928374.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,516B, BPFP=0.0238 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 48,576B, BPFP=0.0922 +⌛️ [2/4] FRONTEND: Frontend time: 2.582s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.486s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09670976 14.10508818 + layer.39.0 81.62974520 17047.07871720 + ------------------------------------------------------------------------------------- + TOTAL 40.86322748 8530.59190269 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 61092 +BPFP 0.0580 bits/point +EBPFP 0.0580 equivalent bits/point +MSE 8530.591903 +---------------------- -------------------------------------------------------- +Time: 5.138s Load: 0.069s, Pack+Encode: 2.582s, Decode+Unpack: 2.486s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8530.5919 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02948072-0.002303_digital clock _ digital clock_0.928374.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n02948072-0.002303_digital clock _ digital clock_0.928374.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02999410-0.000148_chest _ chest_0.9948565.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02999410-0.000148_chest _ chest_0.9948565.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.081s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,648B, BPFP=0.0240 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 33,868B, BPFP=0.0643 +⌛️ [2/4] FRONTEND: Frontend time: 2.574s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.491s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10256943 13.67822598 + layer.39.0 13.72598738 15765.36151603 + ------------------------------------------------------------------------------------- + TOTAL 6.91427841 7889.51987101 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 46516 +BPFP 0.0441 bits/point +EBPFP 0.0441 equivalent bits/point +MSE 7889.519871 +---------------------- -------------------------------------------------------- +Time: 5.145s Load: 0.081s, Pack+Encode: 2.574s, Decode+Unpack: 2.491s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7889.5199 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02999410-0.000148_chest _ chest_0.9948565.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n02999410-0.000148_chest _ chest_0.9948565.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03026506-0.001828_basketball _ basketball_0.6904969.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03026506-0.001828_basketball _ basketball_0.6904969.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,640B, BPFP=0.0240 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 44,328B, BPFP=0.0841 +⌛️ [2/4] FRONTEND: Frontend time: 2.584s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.494s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09484169 13.77625520 + layer.39.0 87.31533194 16624.07774538 + ------------------------------------------------------------------------------------- + TOTAL 43.70508681 8318.92700029 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 56968 +BPFP 0.0541 bits/point +EBPFP 0.0541 equivalent bits/point +MSE 8318.927000 +---------------------- -------------------------------------------------------- +Time: 5.158s Load: 0.080s, Pack+Encode: 2.584s, Decode+Unpack: 2.494s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8318.9270 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03026506-0.001828_basketball _ basketball_0.6904969.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n03026506-0.001828_basketball _ basketball_0.6904969.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03124043-0.001154_bow tie _ bow tie_0.9622156.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03124043-0.001154_bow tie _ bow tie_0.9622156.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,660B, BPFP=0.0240 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 43,148B, BPFP=0.0819 +⌛️ [2/4] FRONTEND: Frontend time: 2.609s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.484s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09893820 13.40102838 + layer.39.0 13.24554141 16493.85811467 + ------------------------------------------------------------------------------------- + TOTAL 6.67223981 8253.62957153 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 55808 +BPFP 0.0530 bits/point +EBPFP 0.0530 equivalent bits/point +MSE 8253.629572 +---------------------- -------------------------------------------------------- +Time: 5.144s Load: 0.052s, Pack+Encode: 2.609s, Decode+Unpack: 2.484s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8253.6296 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03124043-0.001154_bow tie _ bow tie_0.9622156.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n03124043-0.001154_bow tie _ bow tie_0.9622156.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03196217-0.015958_soap dispenser _ envelope_0.80274254.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03196217-0.015958_soap dispenser _ envelope_0.80274254.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 14,076B, BPFP=0.0267 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 33,420B, BPFP=0.0634 +⌛️ [2/4] FRONTEND: Frontend time: 2.579s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.489s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10340443 13.69712232 + layer.39.0 8.70910111 13602.30320700 + ------------------------------------------------------------------------------------- + TOTAL 4.40625277 6808.00016466 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 47496 +BPFP 0.0451 bits/point +EBPFP 0.0451 equivalent bits/point +MSE 6808.000165 +---------------------- -------------------------------------------------------- +Time: 5.138s Load: 0.070s, Pack+Encode: 2.579s, Decode+Unpack: 2.489s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 6808.0002 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03196217-0.015958_soap dispenser _ envelope_0.80274254.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n03196217-0.015958_soap dispenser _ envelope_0.80274254.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03223299-0.001528_hot dog _ hot dog_0.9147216.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03223299-0.001528_hot dog _ hot dog_0.9147216.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 14,980B, BPFP=0.0284 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 40,888B, BPFP=0.0776 +⌛️ [2/4] FRONTEND: Frontend time: 2.589s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.484s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10130972 13.45964396 + layer.39.0 352.09596696 18518.94266278 + ------------------------------------------------------------------------------------- + TOTAL 176.09863834 9266.20115337 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 55868 +BPFP 0.0530 bits/point +EBPFP 0.0530 equivalent bits/point +MSE 9266.201153 +---------------------- -------------------------------------------------------- +Time: 5.145s Load: 0.071s, Pack+Encode: 2.589s, Decode+Unpack: 2.484s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9266.2012 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03223299-0.001528_hot dog _ hot dog_0.9147216.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n03223299-0.001528_hot dog _ hot dog_0.9147216.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03255030-0.005469_bubble _ bubble_0.9381716.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03255030-0.005469_bubble _ bubble_0.9381716.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.079s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 11,824B, BPFP=0.0224 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 45,056B, BPFP=0.0855 +⌛️ [2/4] FRONTEND: Frontend time: 2.594s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.487s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09675161 13.61167927 + layer.39.0 42.23478499 13480.02526725 + ------------------------------------------------------------------------------------- + TOTAL 21.16576830 6746.81847326 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 56880 +BPFP 0.0540 bits/point +EBPFP 0.0540 equivalent bits/point +MSE 6746.818473 +---------------------- -------------------------------------------------------- +Time: 5.161s Load: 0.079s, Pack+Encode: 2.594s, Decode+Unpack: 2.487s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 6746.8185 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03255030-0.005469_bubble _ bubble_0.9381716.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n03255030-0.005469_bubble _ bubble_0.9381716.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03325584-0.000773_candle _ candle_0.810919.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03325584-0.000773_candle _ candle_0.810919.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,656B, BPFP=0.0240 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 62,768B, BPFP=0.1191 +⌛️ [2/4] FRONTEND: Frontend time: 2.573s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.492s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10394677 13.52158592 + layer.39.0 140.58187561 16913.46355685 + ------------------------------------------------------------------------------------- + TOTAL 70.34291119 8463.49257139 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 75424 +BPFP 0.0716 bits/point +EBPFP 0.0716 equivalent bits/point +MSE 8463.492571 +---------------------- -------------------------------------------------------- +Time: 5.136s Load: 0.072s, Pack+Encode: 2.573s, Decode+Unpack: 2.492s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8463.4926 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03325584-0.000773_candle _ candle_0.810919.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n03325584-0.000773_candle _ candle_0.810919.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03355925-0.004997_spider web _ spider web_0.9142101.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03355925-0.004997_spider web _ spider web_0.9142101.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,012B, BPFP=0.0247 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 25,888B, BPFP=0.0491 +⌛️ [2/4] FRONTEND: Frontend time: 2.563s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.465s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09873271 14.00468921 + layer.39.0 6.60211199 14435.00583090 + ------------------------------------------------------------------------------------- + TOTAL 3.35042235 7224.50526006 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 38900 +BPFP 0.0369 bits/point +EBPFP 0.0369 equivalent bits/point +MSE 7224.505260 +---------------------- -------------------------------------------------------- +Time: 5.079s Load: 0.052s, Pack+Encode: 2.563s, Decode+Unpack: 2.465s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7224.5053 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03355925-0.004997_spider web _ spider web_0.9142101.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n03355925-0.004997_spider web _ spider web_0.9142101.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03384352-0.002273_viaduct _ viaduct_0.6161024.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03384352-0.002273_viaduct _ viaduct_0.6161024.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,408B, BPFP=0.0254 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 39,256B, BPFP=0.0745 +⌛️ [2/4] FRONTEND: Frontend time: 2.576s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.486s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09647940 13.51220845 + layer.39.0 175.50411504 15391.46938776 + ------------------------------------------------------------------------------------- + TOTAL 87.80029722 7702.49079810 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52664 +BPFP 0.0500 bits/point +EBPFP 0.0500 equivalent bits/point +MSE 7702.490798 +---------------------- -------------------------------------------------------- +Time: 5.133s Load: 0.070s, Pack+Encode: 2.576s, Decode+Unpack: 2.486s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7702.4908 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03384352-0.002273_viaduct _ viaduct_0.6161024.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n03384352-0.002273_viaduct _ viaduct_0.6161024.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03388043-0.005154_candle _ candle_0.9636924.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03388043-0.005154_candle _ candle_0.9636924.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,052B, BPFP=0.0229 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,028B, BPFP=0.0608 +⌛️ [2/4] FRONTEND: Frontend time: 2.576s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.492s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09640297 13.72217034 + layer.39.0 7.87377147 13365.80369291 + ------------------------------------------------------------------------------------- + TOTAL 3.98508722 6689.76293162 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 44080 +BPFP 0.0418 bits/point +EBPFP 0.0418 equivalent bits/point +MSE 6689.762932 +---------------------- -------------------------------------------------------- +Time: 5.148s Load: 0.080s, Pack+Encode: 2.576s, Decode+Unpack: 2.492s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 6689.7629 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03388043-0.005154_candle _ candle_0.9636924.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n03388043-0.005154_candle _ candle_0.9636924.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03417042-0.001187_tank _ tank_0.70379025.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03417042-0.001187_tank _ tank_0.70379025.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,384B, BPFP=0.0235 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 46,756B, BPFP=0.0887 +⌛️ [2/4] FRONTEND: Frontend time: 2.576s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.482s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09848782 13.69493099 + layer.39.0 16.63742104 14025.36929057 + ------------------------------------------------------------------------------------- + TOTAL 8.36795443 7019.53211078 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 59140 +BPFP 0.0561 bits/point +EBPFP 0.0561 equivalent bits/point +MSE 7019.532111 +---------------------- -------------------------------------------------------- +Time: 5.128s Load: 0.070s, Pack+Encode: 2.576s, Decode+Unpack: 2.482s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7019.5321 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03417042-0.001187_tank _ tank_0.70379025.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n03417042-0.001187_tank _ tank_0.70379025.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03444034-0.002100_maraca _ maraca_0.502369.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03444034-0.002100_maraca _ maraca_0.502369.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,660B, BPFP=0.0240 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 59,424B, BPFP=0.1128 +⌛️ [2/4] FRONTEND: Frontend time: 2.558s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.499s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11197850 13.26183643 + layer.39.0 347.54634354 15067.06705539 + ------------------------------------------------------------------------------------- + TOTAL 173.82916102 7540.16444591 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 72084 +BPFP 0.0684 bits/point +EBPFP 0.0684 equivalent bits/point +MSE 7540.164446 +---------------------- -------------------------------------------------------- +Time: 5.129s Load: 0.072s, Pack+Encode: 2.558s, Decode+Unpack: 2.499s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7540.1644 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03444034-0.002100_maraca _ maraca_0.502369.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n03444034-0.002100_maraca _ maraca_0.502369.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03445924-0.003505_baseball player _ baseball player_0.6723684.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03445924-0.003505_baseball player _ baseball player_0.6723684.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.081s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,308B, BPFP=0.0234 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 44,644B, BPFP=0.0847 +⌛️ [2/4] FRONTEND: Frontend time: 2.568s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.490s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09665277 13.38663239 + layer.39.0 26.28463618 16136.47716229 + ------------------------------------------------------------------------------------- + TOTAL 13.19064447 8074.93189734 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 56952 +BPFP 0.0540 bits/point +EBPFP 0.0540 equivalent bits/point +MSE 8074.931897 +---------------------- -------------------------------------------------------- +Time: 5.140s Load: 0.081s, Pack+Encode: 2.568s, Decode+Unpack: 2.490s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8074.9319 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03445924-0.003505_baseball player _ baseball player_0.6723684.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n03445924-0.003505_baseball player _ baseball player_0.6723684.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03452741-0.002771_chain _ chain_0.9575044.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03452741-0.002771_chain _ chain_0.9575044.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,364B, BPFP=0.0235 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 50,048B, BPFP=0.0950 +⌛️ [2/4] FRONTEND: Frontend time: 2.576s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.497s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12351380 13.77028099 + layer.39.0 42.82565370 16151.24975705 + ------------------------------------------------------------------------------------- + TOTAL 21.47458375 8082.51001902 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 62412 +BPFP 0.0592 bits/point +EBPFP 0.0592 equivalent bits/point +MSE 8082.510019 +---------------------- -------------------------------------------------------- +Time: 5.144s Load: 0.071s, Pack+Encode: 2.576s, Decode+Unpack: 2.497s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8082.5100 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03452741-0.002771_chain _ chain_0.9575044.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n03452741-0.002771_chain _ chain_0.9575044.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03483316-0.004974_lighter _ lighter_0.27796906.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03483316-0.004974_lighter _ lighter_0.27796906.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 18,164B, BPFP=0.0345 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 47,872B, BPFP=0.0909 +⌛️ [2/4] FRONTEND: Frontend time: 2.588s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.466s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12993333 13.86551814 + layer.39.0 87.07173986 13706.71525753 + ------------------------------------------------------------------------------------- + TOTAL 43.60083660 6860.29038783 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 66036 +BPFP 0.0627 bits/point +EBPFP 0.0627 equivalent bits/point +MSE 6860.290388 +---------------------- -------------------------------------------------------- +Time: 5.106s Load: 0.052s, Pack+Encode: 2.588s, Decode+Unpack: 2.466s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 6860.2904 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03483316-0.004974_lighter _ lighter_0.27796906.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n03483316-0.004974_lighter _ lighter_0.27796906.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03584829-0.104805_golf cart _ golf cart_0.73568964.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03584829-0.104805_golf cart _ golf cart_0.73568964.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.081s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,328B, BPFP=0.0253 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 57,304B, BPFP=0.1088 +⌛️ [2/4] FRONTEND: Frontend time: 2.582s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.484s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09917131 13.41358418 + layer.39.0 24.34873246 16299.52964043 + ------------------------------------------------------------------------------------- + TOTAL 12.22395189 8156.47161231 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 70632 +BPFP 0.0670 bits/point +EBPFP 0.0670 equivalent bits/point +MSE 8156.471612 +---------------------- -------------------------------------------------------- +Time: 5.147s Load: 0.081s, Pack+Encode: 2.582s, Decode+Unpack: 2.484s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8156.4716 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03584829-0.104805_golf cart _ golf cart_0.73568964.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n03584829-0.104805_golf cart _ golf cart_0.73568964.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03590841-0.001115_rocking chair _ rocking chair_0.2192492.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03590841-0.001115_rocking chair _ rocking chair_0.2192492.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 15,408B, BPFP=0.0292 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 36,400B, BPFP=0.0691 +⌛️ [2/4] FRONTEND: Frontend time: 2.583s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.493s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11329899 13.75457532 + layer.39.0 19.97532495 16044.92808552 + ------------------------------------------------------------------------------------- + TOTAL 10.04431197 8029.34133042 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51808 +BPFP 0.0492 bits/point +EBPFP 0.0492 equivalent bits/point +MSE 8029.341330 +---------------------- -------------------------------------------------------- +Time: 5.146s Load: 0.070s, Pack+Encode: 2.583s, Decode+Unpack: 2.493s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8029.3413 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03590841-0.001115_rocking chair _ rocking chair_0.2192492.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n03590841-0.001115_rocking chair _ rocking chair_0.2192492.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03617480-0.003238_basketball _ basketball_0.67568874.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03617480-0.003238_basketball _ basketball_0.67568874.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 14,220B, BPFP=0.0270 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 57,444B, BPFP=0.1090 +⌛️ [2/4] FRONTEND: Frontend time: 2.590s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.488s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12967051 13.78318509 + layer.39.0 57.10576865 13211.45578231 + ------------------------------------------------------------------------------------- + TOTAL 28.61771958 6612.61948370 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 71664 +BPFP 0.0680 bits/point +EBPFP 0.0680 equivalent bits/point +MSE 6612.619484 +---------------------- -------------------------------------------------------- +Time: 5.129s Load: 0.051s, Pack+Encode: 2.590s, Decode+Unpack: 2.488s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 6612.6195 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03617480-0.003238_basketball _ basketball_0.67568874.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n03617480-0.003238_basketball _ basketball_0.67568874.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03666591-0.004622_torch _ torch_0.99906796.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03666591-0.004622_torch _ torch_0.99906796.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.078s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,884B, BPFP=0.0245 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 27,188B, BPFP=0.0516 +⌛️ [2/4] FRONTEND: Frontend time: 2.568s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.488s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.05477861 14.03065970 + layer.39.0 7.78975672 16461.71817298 + ------------------------------------------------------------------------------------- + TOTAL 7.92226767 8237.87441634 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 40072 +BPFP 0.0380 bits/point +EBPFP 0.0380 equivalent bits/point +MSE 8237.874416 +---------------------- -------------------------------------------------------- +Time: 5.134s Load: 0.078s, Pack+Encode: 2.568s, Decode+Unpack: 2.488s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8237.8744 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03666591-0.004622_torch _ torch_0.99906796.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n03666591-0.004622_torch _ torch_0.99906796.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03670208-0.003899_hair dryer _ grand piano_0.7359066.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03670208-0.003899_hair dryer _ grand piano_0.7359066.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,792B, BPFP=0.0243 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 56,992B, BPFP=0.1082 +⌛️ [2/4] FRONTEND: Frontend time: 2.580s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.483s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11232473 13.80508325 + layer.39.0 36.60432231 14903.34596696 + ------------------------------------------------------------------------------------- + TOTAL 18.35832352 7458.57552510 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 69784 +BPFP 0.0662 bits/point +EBPFP 0.0662 equivalent bits/point +MSE 7458.575525 +---------------------- -------------------------------------------------------- +Time: 5.134s Load: 0.071s, Pack+Encode: 2.580s, Decode+Unpack: 2.483s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7458.5755 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03670208-0.003899_hair dryer _ grand piano_0.7359066.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n03670208-0.003899_hair dryer _ grand piano_0.7359066.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03717622-0.001175_sundial _ sundial_0.9998197.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03717622-0.001175_sundial _ sundial_0.9998197.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,872B, BPFP=0.0244 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 54,916B, BPFP=0.1042 +⌛️ [2/4] FRONTEND: Frontend time: 2.590s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.492s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13381931 13.70946174 + layer.39.0 773.52204810 17347.73372206 + ------------------------------------------------------------------------------------- + TOTAL 386.82793371 8680.72159190 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 67788 +BPFP 0.0643 bits/point +EBPFP 0.0643 equivalent bits/point +MSE 8680.721592 +---------------------- -------------------------------------------------------- +Time: 5.152s Load: 0.070s, Pack+Encode: 2.590s, Decode+Unpack: 2.492s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8680.7216 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03717622-0.001175_sundial _ sundial_0.9998197.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n03717622-0.001175_sundial _ sundial_0.9998197.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03720891-0.001854_African bush elephant _ African bush elephant_0.722115.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03720891-0.001854_African bush elephant _ African bush elephant_0.722115.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,312B, BPFP=0.0234 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 53,700B, BPFP=0.1019 +⌛️ [2/4] FRONTEND: Frontend time: 2.593s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.489s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09642763 13.36312845 + layer.39.0 155.23232507 16198.69096210 + ------------------------------------------------------------------------------------- + TOTAL 77.66437635 8106.02704528 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 66012 +BPFP 0.0626 bits/point +EBPFP 0.0626 equivalent bits/point +MSE 8106.027045 +---------------------- -------------------------------------------------------- +Time: 5.152s Load: 0.071s, Pack+Encode: 2.593s, Decode+Unpack: 2.489s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8106.0270 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03720891-0.001854_African bush elephant _ African bush elephant_0.722115.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n03720891-0.001854_African bush elephant _ African bush elephant_0.722115.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03721384-0.003327_chain _ chain_0.5599652.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03721384-0.003327_chain _ chain_0.5599652.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,528B, BPFP=0.0257 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 47,372B, BPFP=0.0899 +⌛️ [2/4] FRONTEND: Frontend time: 2.568s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.477s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09561452 13.60343875 + layer.39.0 742.66502672 15264.91059281 + ------------------------------------------------------------------------------------- + TOTAL 371.38032062 7639.25701578 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 60900 +BPFP 0.0578 bits/point +EBPFP 0.0578 equivalent bits/point +MSE 7639.257016 +---------------------- -------------------------------------------------------- +Time: 5.096s Load: 0.051s, Pack+Encode: 2.568s, Decode+Unpack: 2.477s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7639.2570 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03721384-0.003327_chain _ chain_0.5599652.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n03721384-0.003327_chain _ chain_0.5599652.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03724870-0.001366_Christmas stocking _ Christmas stocking_0.5709616.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03724870-0.001366_Christmas stocking _ Christmas stocking_0.5709616.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,576B, BPFP=0.0258 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 45,696B, BPFP=0.0867 +⌛️ [2/4] FRONTEND: Frontend time: 2.565s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.473s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10329660 13.27755159 + layer.39.0 513.92243683 16278.99125364 + ------------------------------------------------------------------------------------- + TOTAL 257.01286671 8146.13440262 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 59272 +BPFP 0.0563 bits/point +EBPFP 0.0563 equivalent bits/point +MSE 8146.134403 +---------------------- -------------------------------------------------------- +Time: 5.108s Load: 0.070s, Pack+Encode: 2.565s, Decode+Unpack: 2.473s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8146.1344 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03724870-0.001366_Christmas stocking _ Christmas stocking_0.5709616.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n03724870-0.001366_Christmas stocking _ Christmas stocking_0.5709616.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03775071-0.000145_Christmas stocking _ Christmas stocking_0.9986047.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03775071-0.000145_Christmas stocking _ Christmas stocking_0.9986047.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,164B, BPFP=0.0231 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 51,092B, BPFP=0.0970 +⌛️ [2/4] FRONTEND: Frontend time: 2.577s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.485s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09700392 13.56437815 + layer.39.0 284.92189018 17434.37706511 + ------------------------------------------------------------------------------------- + TOTAL 142.50944705 8723.97072163 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 63256 +BPFP 0.0600 bits/point +EBPFP 0.0600 equivalent bits/point +MSE 8723.970722 +---------------------- -------------------------------------------------------- +Time: 5.143s Load: 0.080s, Pack+Encode: 2.577s, Decode+Unpack: 2.485s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8723.9707 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03775071-0.000145_Christmas stocking _ Christmas stocking_0.9986047.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n03775071-0.000145_Christmas stocking _ Christmas stocking_0.9986047.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03788195-0.005975_steam locomotive _ steam locomotive_0.42419377.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03788195-0.005975_steam locomotive _ steam locomotive_0.42419377.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,544B, BPFP=0.0257 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 34,104B, BPFP=0.0647 +⌛️ [2/4] FRONTEND: Frontend time: 2.567s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.475s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10790903 13.35682208 + layer.39.0 10.34781284 16114.98736638 + ------------------------------------------------------------------------------------- + TOTAL 5.22786094 8064.17209423 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 47648 +BPFP 0.0452 bits/point +EBPFP 0.0452 equivalent bits/point +MSE 8064.172094 +---------------------- -------------------------------------------------------- +Time: 5.114s Load: 0.072s, Pack+Encode: 2.567s, Decode+Unpack: 2.475s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8064.1721 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03788195-0.005975_steam locomotive _ steam locomotive_0.42419377.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n03788195-0.005975_steam locomotive _ steam locomotive_0.42419377.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03837869-0.002023_lighthouse _ lighthouse_0.5898798.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03837869-0.002023_lighthouse _ lighthouse_0.5898798.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,556B, BPFP=0.0257 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 24,976B, BPFP=0.0474 +⌛️ [2/4] FRONTEND: Frontend time: 2.568s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.504s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12703056 13.63500953 + layer.39.0 141.21340500 15505.83090379 + ------------------------------------------------------------------------------------- + TOTAL 70.67021778 7759.73295666 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 38532 +BPFP 0.0366 bits/point +EBPFP 0.0366 equivalent bits/point +MSE 7759.732957 +---------------------- -------------------------------------------------------- +Time: 5.121s Load: 0.050s, Pack+Encode: 2.568s, Decode+Unpack: 2.504s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7759.7330 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03837869-0.002023_lighthouse _ lighthouse_0.5898798.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n03837869-0.002023_lighthouse _ lighthouse_0.5898798.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03840681-0.002188_ladybug _ ladybug_0.9972365.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03840681-0.002188_ladybug _ ladybug_0.9972365.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,976B, BPFP=0.0246 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,572B, BPFP=0.0599 +⌛️ [2/4] FRONTEND: Frontend time: 2.579s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.486s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09487485 13.96611736 + layer.39.0 29.40353574 15833.30223518 + ------------------------------------------------------------------------------------- + TOTAL 14.74920530 7923.63417627 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 44548 +BPFP 0.0423 bits/point +EBPFP 0.0423 equivalent bits/point +MSE 7923.634176 +---------------------- -------------------------------------------------------- +Time: 5.137s Load: 0.071s, Pack+Encode: 2.579s, Decode+Unpack: 2.486s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7923.6342 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03840681-0.002188_ladybug _ ladybug_0.9972365.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n03840681-0.002188_ladybug _ ladybug_0.9972365.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03888257-0.004083_rugby ball _ snail_0.41588444.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03888257-0.004083_rugby ball _ snail_0.41588444.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,508B, BPFP=0.0256 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 29,708B, BPFP=0.0564 +⌛️ [2/4] FRONTEND: Frontend time: 2.571s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.477s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10005040 13.38642645 + layer.39.0 7.47115060 15498.59280855 + ------------------------------------------------------------------------------------- + TOTAL 3.78560050 7755.98961750 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 43216 +BPFP 0.0410 bits/point +EBPFP 0.0410 equivalent bits/point +MSE 7755.989617 +---------------------- -------------------------------------------------------- +Time: 5.118s Load: 0.070s, Pack+Encode: 2.571s, Decode+Unpack: 2.477s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7755.9896 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03888257-0.004083_rugby ball _ snail_0.41588444.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n03888257-0.004083_rugby ball _ snail_0.41588444.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03891332-0.003727_syringe _ syringe_0.93799996.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03891332-0.003727_syringe _ syringe_0.93799996.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 11,976B, BPFP=0.0227 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 66,240B, BPFP=0.1257 +⌛️ [2/4] FRONTEND: Frontend time: 2.585s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.471s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09617506 13.88497252 + layer.39.0 18.45312310 16278.89018465 + ------------------------------------------------------------------------------------- + TOTAL 9.27464908 8146.38757858 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 78216 +BPFP 0.0742 bits/point +EBPFP 0.0742 equivalent bits/point +MSE 8146.387579 +---------------------- -------------------------------------------------------- +Time: 5.108s Load: 0.052s, Pack+Encode: 2.585s, Decode+Unpack: 2.471s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8146.3876 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03891332-0.003727_syringe _ syringe_0.93799996.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n03891332-0.003727_syringe _ syringe_0.93799996.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03982430-0.005102_couch _ couch_0.9976859.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03982430-0.005102_couch _ couch_0.9976859.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.068s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,388B, BPFP=0.0235 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 44,856B, BPFP=0.0851 +⌛️ [2/4] FRONTEND: Frontend time: 2.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.478s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09691652 13.62028137 + layer.39.0 169.89398081 15949.74829932 + ------------------------------------------------------------------------------------- + TOTAL 84.99544866 7981.68429035 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 57244 +BPFP 0.0543 bits/point +EBPFP 0.0543 equivalent bits/point +MSE 7981.684290 +---------------------- -------------------------------------------------------- +Time: 5.127s Load: 0.068s, Pack+Encode: 2.581s, Decode+Unpack: 2.478s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7981.6843 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03982430-0.005102_couch _ couch_0.9976859.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n03982430-0.005102_couch _ couch_0.9976859.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04033901-0.007476_envelope _ envelope_0.9990971.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04033901-0.007476_envelope _ envelope_0.9990971.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,204B, BPFP=0.0232 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 33,932B, BPFP=0.0644 +⌛️ [2/4] FRONTEND: Frontend time: 2.576s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.477s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10364226 13.93570442 + layer.39.0 7.34252906 15639.98056365 + ------------------------------------------------------------------------------------- + TOTAL 3.72308566 7826.95813403 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 46136 +BPFP 0.0438 bits/point +EBPFP 0.0438 equivalent bits/point +MSE 7826.958134 +---------------------- -------------------------------------------------------- +Time: 5.122s Load: 0.070s, Pack+Encode: 2.576s, Decode+Unpack: 2.477s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7826.9581 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04033901-0.007476_envelope _ envelope_0.9990971.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n04033901-0.007476_envelope _ envelope_0.9990971.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04039381-0.000054_hair dryer _ hair dryer_0.5667548.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04039381-0.000054_hair dryer _ hair dryer_0.5667548.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 11,836B, BPFP=0.0225 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 46,428B, BPFP=0.0881 +⌛️ [2/4] FRONTEND: Frontend time: 2.566s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.471s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09588603 13.85338181 + layer.39.0 26.21653304 16004.88241011 + ------------------------------------------------------------------------------------- + TOTAL 13.15620954 8009.36789596 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 58264 +BPFP 0.0553 bits/point +EBPFP 0.0553 equivalent bits/point +MSE 8009.367896 +---------------------- -------------------------------------------------------- +Time: 5.089s Load: 0.052s, Pack+Encode: 2.566s, Decode+Unpack: 2.471s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8009.3679 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04039381-0.000054_hair dryer _ hair dryer_0.5667548.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n04039381-0.000054_hair dryer _ hair dryer_0.5667548.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04118538-0.005804_cowboy boot _ cowboy boot_0.21208441.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04118538-0.005804_cowboy boot _ cowboy boot_0.21208441.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,044B, BPFP=0.0248 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 35,908B, BPFP=0.0682 +⌛️ [2/4] FRONTEND: Frontend time: 2.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.459s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09664223 13.02263082 + layer.39.0 8.64007266 15935.19144801 + ------------------------------------------------------------------------------------- + TOTAL 4.36835744 7974.10703941 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 48952 +BPFP 0.0465 bits/point +EBPFP 0.0465 equivalent bits/point +MSE 7974.107039 +---------------------- -------------------------------------------------------- +Time: 5.110s Load: 0.070s, Pack+Encode: 2.581s, Decode+Unpack: 2.459s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7974.1070 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04118538-0.005804_cowboy boot _ cowboy boot_0.21208441.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n04118538-0.005804_cowboy boot _ cowboy boot_0.21208441.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04146614-0.008793_marimba _ marimba_0.54555196.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04146614-0.008793_marimba _ marimba_0.54555196.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 14,872B, BPFP=0.0282 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 65,524B, BPFP=0.1244 +⌛️ [2/4] FRONTEND: Frontend time: 2.587s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.476s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09774729 13.70121458 + layer.39.0 155.07908163 16502.34013605 + ------------------------------------------------------------------------------------- + TOTAL 77.58841446 8258.02067532 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 80396 +BPFP 0.0763 bits/point +EBPFP 0.0763 equivalent bits/point +MSE 8258.020675 +---------------------- -------------------------------------------------------- +Time: 5.115s Load: 0.052s, Pack+Encode: 2.587s, Decode+Unpack: 2.476s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8258.0207 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04146614-0.008793_marimba _ marimba_0.54555196.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n04146614-0.008793_marimba _ marimba_0.54555196.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04179913-0.006024_guacamole _ custard apple_0.5550306.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04179913-0.006024_guacamole _ custard apple_0.5550306.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,912B, BPFP=0.0245 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 45,576B, BPFP=0.0865 +⌛️ [2/4] FRONTEND: Frontend time: 2.587s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.471s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11409367 13.79473776 + layer.39.0 68.43204871 14963.99708455 + ------------------------------------------------------------------------------------- + TOTAL 34.27307119 7488.89591115 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 58488 +BPFP 0.0555 bits/point +EBPFP 0.0555 equivalent bits/point +MSE 7488.895911 +---------------------- -------------------------------------------------------- +Time: 5.130s Load: 0.072s, Pack+Encode: 2.587s, Decode+Unpack: 2.471s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7488.8959 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04179913-0.006024_guacamole _ custard apple_0.5550306.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n04179913-0.006024_guacamole _ custard apple_0.5550306.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04208210-0.007213_doormat _ doormat_0.81736016.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04208210-0.007213_doormat _ doormat_0.81736016.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 14,352B, BPFP=0.0272 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 55,072B, BPFP=0.1045 +⌛️ [2/4] FRONTEND: Frontend time: 2.587s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.485s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10601767 13.67162635 + layer.39.0 349.44518343 17264.79494655 + ------------------------------------------------------------------------------------- + TOTAL 174.77560055 8639.23328645 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 69424 +BPFP 0.0659 bits/point +EBPFP 0.0659 equivalent bits/point +MSE 8639.233286 +---------------------- -------------------------------------------------------- +Time: 5.152s Load: 0.080s, Pack+Encode: 2.587s, Decode+Unpack: 2.485s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8639.2333 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04208210-0.007213_doormat _ doormat_0.81736016.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n04208210-0.007213_doormat _ doormat_0.81736016.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04270147-0.000435_rotary dial telephone _ rotary dial telephone_0.9268941.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04270147-0.000435_rotary dial telephone _ rotary dial telephone_0.9268941.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 11,444B, BPFP=0.0217 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 42,436B, BPFP=0.0805 +⌛️ [2/4] FRONTEND: Frontend time: 2.571s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.507s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09464848 13.69101335 + layer.39.0 229.78908528 16958.85519922 + ------------------------------------------------------------------------------------- + TOTAL 114.94186688 8486.27310628 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 53880 +BPFP 0.0511 bits/point +EBPFP 0.0511 equivalent bits/point +MSE 8486.273106 +---------------------- -------------------------------------------------------- +Time: 5.157s Load: 0.080s, Pack+Encode: 2.571s, Decode+Unpack: 2.507s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8486.2731 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04270147-0.000435_rotary dial telephone _ rotary dial telephone_0.9268941.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n04270147-0.000435_rotary dial telephone _ rotary dial telephone_0.9268941.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04317175-0.006894_bow tie _ bow tie_0.95877784.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04317175-0.006894_bow tie _ bow tie_0.95877784.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,408B, BPFP=0.0236 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 49,944B, BPFP=0.0948 +⌛️ [2/4] FRONTEND: Frontend time: 2.578s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.486s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09706025 13.47437211 + layer.39.0 10.87108806 13689.58989310 + ------------------------------------------------------------------------------------- + TOTAL 5.48407415 6851.53213261 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 62352 +BPFP 0.0592 bits/point +EBPFP 0.0592 equivalent bits/point +MSE 6851.532133 +---------------------- -------------------------------------------------------- +Time: 5.134s Load: 0.070s, Pack+Encode: 2.578s, Decode+Unpack: 2.486s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 6851.5321 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04317175-0.006894_bow tie _ bow tie_0.95877784.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n04317175-0.006894_bow tie _ bow tie_0.95877784.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04347754-0.001405_viaduct _ viaduct_0.8445672.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04347754-0.001405_viaduct _ viaduct_0.8445672.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,592B, BPFP=0.0239 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 52,908B, BPFP=0.1004 +⌛️ [2/4] FRONTEND: Frontend time: 2.588s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.495s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09586499 13.94687272 + layer.39.0 267.55718537 15956.93197279 + ------------------------------------------------------------------------------------- + TOTAL 133.82652518 7985.43942276 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 65500 +BPFP 0.0622 bits/point +EBPFP 0.0622 equivalent bits/point +MSE 7985.439423 +---------------------- -------------------------------------------------------- +Time: 5.164s Load: 0.080s, Pack+Encode: 2.588s, Decode+Unpack: 2.495s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7985.4394 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04347754-0.001405_viaduct _ viaduct_0.8445672.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n04347754-0.001405_viaduct _ viaduct_0.8445672.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04355338-0.000791_envelope _ envelope_0.9969598.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04355338-0.000791_envelope _ envelope_0.9969598.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.056s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,844B, BPFP=0.0244 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 45,912B, BPFP=0.0871 +⌛️ [2/4] FRONTEND: Frontend time: 2.593s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.477s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10273007 14.04151672 + layer.39.0 331.89978134 15351.78522838 + ------------------------------------------------------------------------------------- + TOTAL 166.00125571 7682.91337255 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 58756 +BPFP 0.0558 bits/point +EBPFP 0.0558 equivalent bits/point +MSE 7682.913373 +---------------------- -------------------------------------------------------- +Time: 5.125s Load: 0.056s, Pack+Encode: 2.593s, Decode+Unpack: 2.477s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7682.9134 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04355338-0.000791_envelope _ envelope_0.9969598.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n04355338-0.000791_envelope _ envelope_0.9969598.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04366367-0.002021_parachute _ parachute_0.9226023.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04366367-0.002021_parachute _ parachute_0.9226023.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 14,288B, BPFP=0.0271 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 28,852B, BPFP=0.0548 +⌛️ [2/4] FRONTEND: Frontend time: 2.580s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.493s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09577132 13.39852956 + layer.39.0 47.60657343 15778.94266278 + ------------------------------------------------------------------------------------- + TOTAL 23.85117238 7896.17059617 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 43140 +BPFP 0.0409 bits/point +EBPFP 0.0409 equivalent bits/point +MSE 7896.170596 +---------------------- -------------------------------------------------------- +Time: 5.153s Load: 0.080s, Pack+Encode: 2.580s, Decode+Unpack: 2.493s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7896.1706 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04366367-0.002021_parachute _ parachute_0.9226023.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n04366367-0.002021_parachute _ parachute_0.9226023.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04376876-0.004615_lighter _ lighter_0.69176143.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04376876-0.004615_lighter _ lighter_0.69176143.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,476B, BPFP=0.0237 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 34,364B, BPFP=0.0652 +⌛️ [2/4] FRONTEND: Frontend time: 2.580s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.484s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09912059 13.73260789 + layer.39.0 173.01079628 16322.59086492 + ------------------------------------------------------------------------------------- + TOTAL 86.55495844 8168.16173640 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 46840 +BPFP 0.0445 bits/point +EBPFP 0.0445 equivalent bits/point +MSE 8168.161736 +---------------------- -------------------------------------------------------- +Time: 5.144s Load: 0.080s, Pack+Encode: 2.580s, Decode+Unpack: 2.484s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8168.1617 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04376876-0.004615_lighter _ lighter_0.69176143.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n04376876-0.004615_lighter _ lighter_0.69176143.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04442312-0.001690_washing machine _ washing machine_0.8494714.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04442312-0.001690_washing machine _ washing machine_0.8494714.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,672B, BPFP=0.0241 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 37,348B, BPFP=0.0709 +⌛️ [2/4] FRONTEND: Frontend time: 2.613s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.493s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.08302300 13.92442697 + layer.39.0 28.24609944 15695.04567541 + ------------------------------------------------------------------------------------- + TOTAL 18.16456122 7854.48505119 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50020 +BPFP 0.0475 bits/point +EBPFP 0.0475 equivalent bits/point +MSE 7854.485051 +---------------------- -------------------------------------------------------- +Time: 5.156s Load: 0.051s, Pack+Encode: 2.613s, Decode+Unpack: 2.493s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7854.4851 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04442312-0.001690_washing machine _ washing machine_0.8494714.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n04442312-0.001690_washing machine _ washing machine_0.8494714.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04456115-0.002573_hair dryer _ envelope_0.34823084.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04456115-0.002573_hair dryer _ envelope_0.34823084.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,424B, BPFP=0.0236 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 36,516B, BPFP=0.0693 +⌛️ [2/4] FRONTEND: Frontend time: 2.569s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.498s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09444211 13.98279295 + layer.39.0 8.80792942 15162.67638484 + ------------------------------------------------------------------------------------- + TOTAL 4.45118577 7588.32958889 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 48940 +BPFP 0.0464 bits/point +EBPFP 0.0464 equivalent bits/point +MSE 7588.329589 +---------------------- -------------------------------------------------------- +Time: 5.148s Load: 0.080s, Pack+Encode: 2.569s, Decode+Unpack: 2.498s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7588.3296 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04456115-0.002573_hair dryer _ envelope_0.34823084.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n04456115-0.002573_hair dryer _ envelope_0.34823084.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04509417-0.000350_sundial _ sundial_0.99936897.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04509417-0.000350_sundial _ sundial_0.99936897.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,564B, BPFP=0.0257 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 41,284B, BPFP=0.0784 +⌛️ [2/4] FRONTEND: Frontend time: 2.600s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.478s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10319057 13.67247384 + layer.39.0 8.14296913 15089.91545190 + ------------------------------------------------------------------------------------- + TOTAL 4.12307985 7551.79396287 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 54848 +BPFP 0.0521 bits/point +EBPFP 0.0521 equivalent bits/point +MSE 7551.793963 +---------------------- -------------------------------------------------------- +Time: 5.148s Load: 0.070s, Pack+Encode: 2.600s, Decode+Unpack: 2.478s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7551.7940 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04509417-0.000350_sundial _ sundial_0.99936897.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n04509417-0.000350_sundial _ sundial_0.99936897.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04532670-0.000670_spider web _ spider web_0.70711935.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04532670-0.000670_spider web _ spider web_0.70711935.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,148B, BPFP=0.0231 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,748B, BPFP=0.0603 +⌛️ [2/4] FRONTEND: Frontend time: 2.584s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.479s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09618602 13.25132106 + layer.39.0 175.41615039 18510.32653061 + ------------------------------------------------------------------------------------- + TOTAL 87.75616821 9261.78892584 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 43896 +BPFP 0.0417 bits/point +EBPFP 0.0417 equivalent bits/point +MSE 9261.788926 +---------------------- -------------------------------------------------------- +Time: 5.134s Load: 0.070s, Pack+Encode: 2.584s, Decode+Unpack: 2.479s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9261.7889 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04532670-0.000670_spider web _ spider web_0.70711935.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n04532670-0.000670_spider web _ spider web_0.70711935.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04540053-0.000734_balance beam _ balance beam_0.99765223.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04540053-0.000734_balance beam _ balance beam_0.99765223.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.081s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,116B, BPFP=0.0230 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 26,576B, BPFP=0.0504 +⌛️ [2/4] FRONTEND: Frontend time: 2.597s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.479s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09941827 13.79031618 + layer.39.0 8.11341412 15203.02818270 + ------------------------------------------------------------------------------------- + TOTAL 4.10641619 7608.40924944 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 38692 +BPFP 0.0367 bits/point +EBPFP 0.0367 equivalent bits/point +MSE 7608.409249 +---------------------- -------------------------------------------------------- +Time: 5.157s Load: 0.081s, Pack+Encode: 2.597s, Decode+Unpack: 2.479s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7608.4092 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04540053-0.000734_balance beam _ balance beam_0.99765223.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n04540053-0.000734_balance beam _ balance beam_0.99765223.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04606251-0.003483_cliff _ cliff_0.9972174.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04606251-0.003483_cliff _ cliff_0.9972174.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 14,196B, BPFP=0.0269 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 46,944B, BPFP=0.0891 +⌛️ [2/4] FRONTEND: Frontend time: 2.591s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.469s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09940710 13.21285266 + layer.39.0 906.86880466 18046.14382896 + ------------------------------------------------------------------------------------- + TOTAL 453.48410588 9029.67834081 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 61140 +BPFP 0.0580 bits/point +EBPFP 0.0580 equivalent bits/point +MSE 9029.678341 +---------------------- -------------------------------------------------------- +Time: 5.110s Load: 0.050s, Pack+Encode: 2.591s, Decode+Unpack: 2.469s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9029.6783 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04606251-0.003483_cliff _ cliff_0.9972174.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n04606251-0.003483_cliff _ cliff_0.9972174.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07583066-0.003935_maraca _ maraca_0.5370013.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07583066-0.003935_maraca _ maraca_0.5370013.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 15,264B, BPFP=0.0290 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 56,128B, BPFP=0.1065 +⌛️ [2/4] FRONTEND: Frontend time: 2.595s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.503s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12045678 13.45896350 + layer.39.0 38.29438092 16477.25753158 + ------------------------------------------------------------------------------------- + TOTAL 19.20741885 8245.35824754 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 71392 +BPFP 0.0678 bits/point +EBPFP 0.0678 equivalent bits/point +MSE 8245.358248 +---------------------- -------------------------------------------------------- +Time: 5.167s Load: 0.070s, Pack+Encode: 2.595s, Decode+Unpack: 2.503s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8245.3582 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07583066-0.003935_maraca _ maraca_0.5370013.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n07583066-0.003935_maraca _ maraca_0.5370013.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07695742-0.003226_ant _ ant_0.64413536.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07695742-0.003226_ant _ ant_0.64413536.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 17,912B, BPFP=0.0340 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 43,516B, BPFP=0.0826 +⌛️ [2/4] FRONTEND: Frontend time: 2.585s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.481s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.16263347 13.43366872 + layer.39.0 172.10254191 16287.51506317 + ------------------------------------------------------------------------------------- + TOTAL 86.13258769 8150.47436595 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 61428 +BPFP 0.0583 bits/point +EBPFP 0.0583 equivalent bits/point +MSE 8150.474366 +---------------------- -------------------------------------------------------- +Time: 5.117s Load: 0.052s, Pack+Encode: 2.585s, Decode+Unpack: 2.481s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8150.4744 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07695742-0.003226_ant _ ant_0.64413536.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n07695742-0.003226_ant _ ant_0.64413536.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07718472-0.001330_spatula _ spatula_0.5388231.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07718472-0.001330_spatula _ spatula_0.5388231.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,232B, BPFP=0.0251 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 59,184B, BPFP=0.1123 +⌛️ [2/4] FRONTEND: Frontend time: 2.596s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.496s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09672572 13.40160255 + layer.39.0 34.52145211 15455.92517007 + ------------------------------------------------------------------------------------- + TOTAL 17.30908891 7734.66338631 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 72416 +BPFP 0.0687 bits/point +EBPFP 0.0687 equivalent bits/point +MSE 7734.663386 +---------------------- -------------------------------------------------------- +Time: 5.162s Load: 0.070s, Pack+Encode: 2.596s, Decode+Unpack: 2.496s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7734.6634 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07718472-0.001330_spatula _ spatula_0.5388231.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n07718472-0.001330_spatula _ spatula_0.5388231.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07831146-0.002710_guacamole _ guacamole_0.99510396.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07831146-0.002710_guacamole _ guacamole_0.99510396.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,552B, BPFP=0.0238 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 53,536B, BPFP=0.1016 +⌛️ [2/4] FRONTEND: Frontend time: 2.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.470s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09717902 13.46431513 + layer.39.0 26.55584533 15452.75801749 + ------------------------------------------------------------------------------------- + TOTAL 13.32651218 7733.11116631 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 66088 +BPFP 0.0627 bits/point +EBPFP 0.0627 equivalent bits/point +MSE 7733.111166 +---------------------- -------------------------------------------------------- +Time: 5.121s Load: 0.070s, Pack+Encode: 2.581s, Decode+Unpack: 2.470s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7733.1112 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07831146-0.002710_guacamole _ guacamole_0.99510396.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n07831146-0.002710_guacamole _ guacamole_0.99510396.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n09229709-0.002628_stethoscope _ stethoscope_0.9726458.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n09229709-0.002628_stethoscope _ stethoscope_0.9726458.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.076s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,368B, BPFP=0.0235 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,608B, BPFP=0.0581 +⌛️ [2/4] FRONTEND: Frontend time: 2.589s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.498s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10247729 13.85996341 + layer.39.0 58.71458181 16294.04664723 + ------------------------------------------------------------------------------------- + TOTAL 29.40852955 8153.95330532 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 42976 +BPFP 0.0408 bits/point +EBPFP 0.0408 equivalent bits/point +MSE 8153.953305 +---------------------- -------------------------------------------------------- +Time: 5.163s Load: 0.076s, Pack+Encode: 2.589s, Decode+Unpack: 2.498s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8153.9533 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n09229709-0.002628_stethoscope _ stethoscope_0.9726458.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n09229709-0.002628_stethoscope _ stethoscope_0.9726458.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12057211-0.000404_nail _ newt_0.31321314.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12057211-0.000404_nail _ newt_0.31321314.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 17,292B, BPFP=0.0328 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 34,776B, BPFP=0.0660 +⌛️ [2/4] FRONTEND: Frontend time: 2.583s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.485s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11577855 13.56661599 + layer.39.0 8.72387956 15966.50145773 + ------------------------------------------------------------------------------------- + TOTAL 4.41982905 7990.03403686 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52068 +BPFP 0.0494 bits/point +EBPFP 0.0494 equivalent bits/point +MSE 7990.034037 +---------------------- -------------------------------------------------------- +Time: 5.148s Load: 0.080s, Pack+Encode: 2.583s, Decode+Unpack: 2.485s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7990.0340 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12057211-0.000404_nail _ newt_0.31321314.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n12057211-0.000404_nail _ newt_0.31321314.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12144580-0.002806_banana _ banana_0.999156.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12144580-0.002806_banana _ banana_0.999156.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,940B, BPFP=0.0246 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 56,216B, BPFP=0.1067 +⌛️ [2/4] FRONTEND: Frontend time: 2.593s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.484s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09629347 13.41292270 + layer.39.0 105.38953930 16356.91739553 + ------------------------------------------------------------------------------------- + TOTAL 52.74291638 8185.16515912 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 69156 +BPFP 0.0656 bits/point +EBPFP 0.0656 equivalent bits/point +MSE 8185.165159 +---------------------- -------------------------------------------------------- +Time: 5.148s Load: 0.070s, Pack+Encode: 2.593s, Decode+Unpack: 2.484s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8185.1652 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12144580-0.002806_banana _ banana_0.999156.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n12144580-0.002806_banana _ banana_0.999156.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12267677-0.004617_pomegranate _ pomegranate_0.9159373.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12267677-0.004617_pomegranate _ pomegranate_0.9159373.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,456B, BPFP=0.0236 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 46,288B, BPFP=0.0879 +⌛️ [2/4] FRONTEND: Frontend time: 2.582s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.486s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10323383 13.60460892 + layer.39.0 78.12042942 15586.93100097 + ------------------------------------------------------------------------------------- + TOTAL 39.11183162 7800.26780495 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 58744 +BPFP 0.0558 bits/point +EBPFP 0.0558 equivalent bits/point +MSE 7800.267805 +---------------------- -------------------------------------------------------- +Time: 5.121s Load: 0.052s, Pack+Encode: 2.582s, Decode+Unpack: 2.486s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7800.2678 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12267677-0.004617_pomegranate _ pomegranate_0.9159373.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1ka/n12267677-0.004617_pomegranate _ pomegranate_0.9159373.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 0.0515 bits/point +Avg EBPFP 0.0515 equivalent bits/point +Avg MSE 7936.549360 +Avg Time 5.139s +------------------------ ---------------------------- diff --git a/lambda0.001/elic-featurecoding-8bit-individual/cls_in1kr/dtufc_elic-featurecoding_dinov3-total_individual.log b/lambda0.001/elic-featurecoding-8bit-individual/cls_in1kr/dtufc_elic-featurecoding_dinov3-total_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..572cc34fd1808c60c1f4874bec7f317cc058fd2a --- /dev/null +++ b/lambda0.001/elic-featurecoding-8bit-individual/cls_in1kr/dtufc_elic-featurecoding_dinov3-total_individual.log @@ -0,0 +1,9344 @@ +Experiment: dtufc_elic-featurecoding_dinov3-total_individual +Log file: output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/dtufc_elic-featurecoding_dinov3-total_individual.log +DTUFCCodecConfig: + arch: elic-featurecoding + handler: dinov3-total + checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.001_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.001_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 286 +Loaded elic-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.9' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.19' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.29' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.39' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json +Loaded per-key mappings: model=dinov3-total + Keys: ['layer.9', 'layer.19', 'layer.29', 'layer.39'] +---------------- -------------------------------------------------------------------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +Checkpoint codec_weights/elic_hybrid/elic2022-official_lambda0.001_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-DINOv3/imagenet1k-r +Output output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr +---------------- -------------------------------------------------------------------------------------------------------------------- +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01443537-graffiti_3.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01443537-graffiti_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,840B, BPFP=0.0244 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 35,664B, BPFP=0.0677 +⌛️ [2/4] FRONTEND: Frontend time: 3.062s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.594s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09690064 13.86384878 + layer.39.0 23.14008974 15421.64042760 + ------------------------------------------------------------------------------------- + TOTAL 11.61849519 7717.75213819 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 48504 +BPFP 0.0460 bits/point +EBPFP 0.0460 equivalent bits/point +MSE 7717.752138 +---------------------- -------------------------------------------------------- +Time: 5.728s Load: 0.072s, Pack+Encode: 3.062s, Decode+Unpack: 2.594s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7717.7521 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01443537-graffiti_3.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n01443537-graffiti_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01494475-misc_31.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01494475-misc_31.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.075s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,036B, BPFP=0.0228 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 53,684B, BPFP=0.1019 +⌛️ [2/4] FRONTEND: Frontend time: 2.589s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.471s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09558801 13.60546211 + layer.39.0 281.54433916 15112.04081633 + ------------------------------------------------------------------------------------- + TOTAL 140.81996359 7562.82313922 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 65720 +BPFP 0.0624 bits/point +EBPFP 0.0624 equivalent bits/point +MSE 7562.823139 +---------------------- -------------------------------------------------------- +Time: 5.134s Load: 0.075s, Pack+Encode: 2.589s, Decode+Unpack: 2.471s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7562.8231 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01494475-misc_31.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n01494475-misc_31.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01531178-cartoon_22.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01531178-cartoon_22.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 11,720B, BPFP=0.0222 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 23,080B, BPFP=0.0438 +⌛️ [2/4] FRONTEND: Frontend time: 2.567s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.453s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10319715 13.85284940 + layer.39.0 12.97479918 15397.31195335 + ------------------------------------------------------------------------------------- + TOTAL 6.53899817 7705.58240138 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 34800 +BPFP 0.0330 bits/point +EBPFP 0.0330 equivalent bits/point +MSE 7705.582401 +---------------------- -------------------------------------------------------- +Time: 5.089s Load: 0.069s, Pack+Encode: 2.567s, Decode+Unpack: 2.453s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7705.5824 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01531178-cartoon_22.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n01531178-cartoon_22.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01534433-painting_9.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01534433-painting_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 14,156B, BPFP=0.0269 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,528B, BPFP=0.0617 +⌛️ [2/4] FRONTEND: Frontend time: 2.590s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.462s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10660143 13.65778175 + layer.39.0 8.42910859 16683.24003887 + ------------------------------------------------------------------------------------- + TOTAL 4.26785501 8348.44891031 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 46684 +BPFP 0.0443 bits/point +EBPFP 0.0443 equivalent bits/point +MSE 8348.448910 +---------------------- -------------------------------------------------------- +Time: 5.123s Load: 0.070s, Pack+Encode: 2.590s, Decode+Unpack: 2.462s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8348.4489 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01534433-painting_9.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n01534433-painting_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01632777-toy_21.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01632777-toy_21.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,708B, BPFP=0.0241 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 71,080B, BPFP=0.1349 +⌛️ [2/4] FRONTEND: Frontend time: 2.617s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.504s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09516629 13.59280286 + layer.39.0 31.73491595 17505.40330418 + ------------------------------------------------------------------------------------- + TOTAL 15.91504112 8759.49805352 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 83788 +BPFP 0.0795 bits/point +EBPFP 0.0795 equivalent bits/point +MSE 8759.498054 +---------------------- -------------------------------------------------------- +Time: 5.191s Load: 0.070s, Pack+Encode: 2.617s, Decode+Unpack: 2.504s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8759.4981 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01632777-toy_21.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n01632777-toy_21.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01748264-misc_18.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01748264-misc_18.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,444B, BPFP=0.0369 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 51,456B, BPFP=0.0977 +⌛️ [2/4] FRONTEND: Frontend time: 2.609s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.477s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.16139180 13.84601156 + layer.39.0 362.83485180 16948.66666667 + ------------------------------------------------------------------------------------- + TOTAL 181.49812180 8481.25633911 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 70900 +BPFP 0.0673 bits/point +EBPFP 0.0673 equivalent bits/point +MSE 8481.256339 +---------------------- -------------------------------------------------------- +Time: 5.156s Load: 0.070s, Pack+Encode: 2.609s, Decode+Unpack: 2.477s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8481.2563 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01748264-misc_18.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n01748264-misc_18.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01784675-painting_2.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01784675-painting_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 15,632B, BPFP=0.0297 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 69,584B, BPFP=0.1321 +⌛️ [2/4] FRONTEND: Frontend time: 2.587s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.461s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13866578 13.83872483 + layer.39.0 232.10166120 18289.61321672 + ------------------------------------------------------------------------------------- + TOTAL 116.12016349 9151.72597077 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 85216 +BPFP 0.0809 bits/point +EBPFP 0.0809 equivalent bits/point +MSE 9151.725971 +---------------------- -------------------------------------------------------- +Time: 5.100s Load: 0.051s, Pack+Encode: 2.587s, Decode+Unpack: 2.461s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9151.7260 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01784675-painting_2.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n01784675-painting_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01820546-painting_29.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01820546-painting_29.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.056s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 14,540B, BPFP=0.0276 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 54,500B, BPFP=0.1034 +⌛️ [2/4] FRONTEND: Frontend time: 2.617s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.495s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10398871 13.92125528 + layer.39.0 202.99580904 16463.54518950 + ------------------------------------------------------------------------------------- + TOTAL 101.54989888 8238.73322239 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 69040 +BPFP 0.0655 bits/point +EBPFP 0.0655 equivalent bits/point +MSE 8238.733222 +---------------------- -------------------------------------------------------- +Time: 5.168s Load: 0.056s, Pack+Encode: 2.617s, Decode+Unpack: 2.495s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8238.7332 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01820546-painting_29.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n01820546-painting_29.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01833805-painting_23.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01833805-painting_23.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.057s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,156B, BPFP=0.0231 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,720B, BPFP=0.0621 +⌛️ [2/4] FRONTEND: Frontend time: 2.602s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.469s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09675035 13.67440040 + layer.39.0 56.43029868 15481.84062196 + ------------------------------------------------------------------------------------- + TOTAL 28.26352451 7747.75751118 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 44876 +BPFP 0.0426 bits/point +EBPFP 0.0426 equivalent bits/point +MSE 7747.757511 +---------------------- -------------------------------------------------------- +Time: 5.128s Load: 0.057s, Pack+Encode: 2.602s, Decode+Unpack: 2.469s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7747.7575 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01833805-painting_23.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n01833805-painting_23.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01860187-painting_2.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01860187-painting_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,028B, BPFP=0.0247 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 35,876B, BPFP=0.0681 +⌛️ [2/4] FRONTEND: Frontend time: 2.575s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.461s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09532418 13.19765853 + layer.39.0 11.39113179 15081.07094266 + ------------------------------------------------------------------------------------- + TOTAL 5.74322799 7547.13430060 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 48904 +BPFP 0.0464 bits/point +EBPFP 0.0464 equivalent bits/point +MSE 7547.134301 +---------------------- -------------------------------------------------------- +Time: 5.086s Load: 0.051s, Pack+Encode: 2.575s, Decode+Unpack: 2.461s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7547.1343 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01860187-painting_2.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n01860187-painting_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01944390-deviantart_6.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01944390-deviantart_6.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 14,020B, BPFP=0.0266 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 57,492B, BPFP=0.1091 +⌛️ [2/4] FRONTEND: Frontend time: 2.599s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.461s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10713051 13.62757095 + layer.39.0 82.30322218 15714.63070943 + ------------------------------------------------------------------------------------- + TOTAL 41.20517635 7864.12914019 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 71512 +BPFP 0.0679 bits/point +EBPFP 0.0679 equivalent bits/point +MSE 7864.129140 +---------------------- -------------------------------------------------------- +Time: 5.140s Load: 0.080s, Pack+Encode: 2.599s, Decode+Unpack: 2.461s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7864.1291 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01944390-deviantart_6.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n01944390-deviantart_6.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01983481-misc_6.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01983481-misc_6.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 15,808B, BPFP=0.0300 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 49,512B, BPFP=0.0940 +⌛️ [2/4] FRONTEND: Frontend time: 2.590s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.465s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10315659 13.58502073 + layer.39.0 236.29731535 16685.19922255 + ------------------------------------------------------------------------------------- + TOTAL 118.20023597 8349.39212164 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 65320 +BPFP 0.0620 bits/point +EBPFP 0.0620 equivalent bits/point +MSE 8349.392122 +---------------------- -------------------------------------------------------- +Time: 5.106s Load: 0.051s, Pack+Encode: 2.590s, Decode+Unpack: 2.465s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8349.3921 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01983481-misc_6.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n01983481-misc_6.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02051845-cartoon_2.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02051845-cartoon_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,548B, BPFP=0.0238 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 29,708B, BPFP=0.0564 +⌛️ [2/4] FRONTEND: Frontend time: 2.578s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.467s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11657756 13.87703569 + layer.39.0 123.57765428 17371.65014577 + ------------------------------------------------------------------------------------- + TOTAL 61.84711592 8692.76359073 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 42256 +BPFP 0.0401 bits/point +EBPFP 0.0401 equivalent bits/point +MSE 8692.763591 +---------------------- -------------------------------------------------------- +Time: 5.097s Load: 0.051s, Pack+Encode: 2.578s, Decode+Unpack: 2.467s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8692.7636 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02051845-cartoon_2.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n02051845-cartoon_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02056570-art_2.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02056570-art_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,328B, BPFP=0.0234 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 44,440B, BPFP=0.0844 +⌛️ [2/4] FRONTEND: Frontend time: 2.588s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.470s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09569211 13.58921359 + layer.39.0 33.39981930 15359.06705539 + ------------------------------------------------------------------------------------- + TOTAL 16.74775571 7686.32813449 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 56768 +BPFP 0.0539 bits/point +EBPFP 0.0539 equivalent bits/point +MSE 7686.328134 +---------------------- -------------------------------------------------------- +Time: 5.138s Load: 0.080s, Pack+Encode: 2.588s, Decode+Unpack: 2.470s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7686.3281 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02056570-art_2.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n02056570-art_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02085620-misc_90.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02085620-misc_90.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.081s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,288B, BPFP=0.0233 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 36,600B, BPFP=0.0695 +⌛️ [2/4] FRONTEND: Frontend time: 2.592s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.474s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09843166 13.44939527 + layer.39.0 72.76188958 15404.28377065 + ------------------------------------------------------------------------------------- + TOTAL 36.43016062 7708.86658296 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 48888 +BPFP 0.0464 bits/point +EBPFP 0.0464 equivalent bits/point +MSE 7708.866583 +---------------------- -------------------------------------------------------- +Time: 5.147s Load: 0.081s, Pack+Encode: 2.592s, Decode+Unpack: 2.474s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7708.8666 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02085620-misc_90.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n02085620-misc_90.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088094-misc_39.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088094-misc_39.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.081s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,744B, BPFP=0.0261 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 38,656B, BPFP=0.0734 +⌛️ [2/4] FRONTEND: Frontend time: 2.589s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.464s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09820385 13.41736136 + layer.39.0 12.32374423 16751.87172012 + ------------------------------------------------------------------------------------- + TOTAL 6.21097404 8382.64454074 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52400 +BPFP 0.0497 bits/point +EBPFP 0.0497 equivalent bits/point +MSE 8382.644541 +---------------------- -------------------------------------------------------- +Time: 5.134s Load: 0.081s, Pack+Encode: 2.589s, Decode+Unpack: 2.464s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8382.6445 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088094-misc_39.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n02088094-misc_39.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088466-sketch_11.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088466-sketch_11.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 11,648B, BPFP=0.0221 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 40,788B, BPFP=0.0774 +⌛️ [2/4] FRONTEND: Frontend time: 2.575s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.464s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09459993 13.92896528 + layer.39.0 16.33682960 15117.64042760 + ------------------------------------------------------------------------------------- + TOTAL 8.21571477 7565.78469644 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52436 +BPFP 0.0498 bits/point +EBPFP 0.0498 equivalent bits/point +MSE 7565.784696 +---------------------- -------------------------------------------------------- +Time: 5.091s Load: 0.052s, Pack+Encode: 2.575s, Decode+Unpack: 2.464s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7565.7847 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088466-sketch_11.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n02088466-sketch_11.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02094433-misc_20.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02094433-misc_20.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.056s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,612B, BPFP=0.0239 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 48,656B, BPFP=0.0924 +⌛️ [2/4] FRONTEND: Frontend time: 2.587s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.474s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09538842 13.27800618 + layer.39.0 94.83275632 15372.00000000 + ------------------------------------------------------------------------------------- + TOTAL 47.46407237 7692.63900309 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 61268 +BPFP 0.0581 bits/point +EBPFP 0.0581 equivalent bits/point +MSE 7692.639003 +---------------------- -------------------------------------------------------- +Time: 5.117s Load: 0.056s, Pack+Encode: 2.587s, Decode+Unpack: 2.474s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7692.6390 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02094433-misc_20.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n02094433-misc_20.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02097298-misc_9.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02097298-misc_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 17,880B, BPFP=0.0339 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 49,876B, BPFP=0.0947 +⌛️ [2/4] FRONTEND: Frontend time: 2.587s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.470s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11199322 13.56360658 + layer.39.0 26.16675018 16227.34110787 + ------------------------------------------------------------------------------------- + TOTAL 13.13937170 8120.45235723 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 67756 +BPFP 0.0643 bits/point +EBPFP 0.0643 equivalent bits/point +MSE 8120.452357 +---------------------- -------------------------------------------------------- +Time: 5.126s Load: 0.069s, Pack+Encode: 2.587s, Decode+Unpack: 2.470s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8120.4524 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02097298-misc_9.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n02097298-misc_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02106662-misc_55.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02106662-misc_55.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.079s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,084B, BPFP=0.0248 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 36,872B, BPFP=0.0700 +⌛️ [2/4] FRONTEND: Frontend time: 2.602s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.464s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09642073 13.21746595 + layer.39.0 14.86428154 14559.93780369 + ------------------------------------------------------------------------------------- + TOTAL 7.48035113 7286.57763482 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 49956 +BPFP 0.0474 bits/point +EBPFP 0.0474 equivalent bits/point +MSE 7286.577635 +---------------------- -------------------------------------------------------- +Time: 5.145s Load: 0.079s, Pack+Encode: 2.602s, Decode+Unpack: 2.464s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7286.5776 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02106662-misc_55.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n02106662-misc_55.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02109525-sketch_23.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02109525-sketch_23.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,020B, BPFP=0.0228 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 40,656B, BPFP=0.0772 +⌛️ [2/4] FRONTEND: Frontend time: 2.614s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.471s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09568003 13.52686923 + layer.39.0 14.01675815 14761.80272109 + ------------------------------------------------------------------------------------- + TOTAL 7.05621909 7387.66479516 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52676 +BPFP 0.0500 bits/point +EBPFP 0.0500 equivalent bits/point +MSE 7387.664795 +---------------------- -------------------------------------------------------- +Time: 5.134s Load: 0.050s, Pack+Encode: 2.614s, Decode+Unpack: 2.471s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7387.6648 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02109525-sketch_23.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n02109525-sketch_23.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110185-painting_33.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110185-painting_33.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 11,616B, BPFP=0.0220 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 52,612B, BPFP=0.0999 +⌛️ [2/4] FRONTEND: Frontend time: 2.611s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.465s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09599521 13.60013135 + layer.39.0 22.05506522 14095.08454810 + ------------------------------------------------------------------------------------- + TOTAL 11.07553021 7054.34233973 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 64228 +BPFP 0.0610 bits/point +EBPFP 0.0610 equivalent bits/point +MSE 7054.342340 +---------------------- -------------------------------------------------------- +Time: 5.128s Load: 0.052s, Pack+Encode: 2.611s, Decode+Unpack: 2.465s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7054.3423 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110185-painting_33.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n02110185-painting_33.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110341-misc_162.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110341-misc_162.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.081s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,884B, BPFP=0.0245 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 26,164B, BPFP=0.0497 +⌛️ [2/4] FRONTEND: Frontend time: 2.584s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.470s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11124049 13.90410422 + layer.39.0 14.33747210 15118.68124393 + ------------------------------------------------------------------------------------- + TOTAL 7.22435629 7566.29267407 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 39048 +BPFP 0.0371 bits/point +EBPFP 0.0371 equivalent bits/point +MSE 7566.292674 +---------------------- -------------------------------------------------------- +Time: 5.135s Load: 0.081s, Pack+Encode: 2.584s, Decode+Unpack: 2.470s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7566.2927 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110341-misc_162.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n02110341-misc_162.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02165456-tattoo_37.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02165456-tattoo_37.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,048B, BPFP=0.0248 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 49,624B, BPFP=0.0942 +⌛️ [2/4] FRONTEND: Frontend time: 2.620s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.470s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09780899 13.44065651 + layer.39.0 88.96013271 16118.58406220 + ------------------------------------------------------------------------------------- + TOTAL 44.52897085 8066.01235935 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 62672 +BPFP 0.0595 bits/point +EBPFP 0.0595 equivalent bits/point +MSE 8066.012359 +---------------------- -------------------------------------------------------- +Time: 5.159s Load: 0.069s, Pack+Encode: 2.620s, Decode+Unpack: 2.470s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8066.0124 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02165456-tattoo_37.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n02165456-tattoo_37.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02219486-misc_3.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02219486-misc_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,188B, BPFP=0.0250 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 24,600B, BPFP=0.0467 +⌛️ [2/4] FRONTEND: Frontend time: 2.582s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.461s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10021695 13.81616330 + layer.39.0 75.73793580 15800.02235180 + ------------------------------------------------------------------------------------- + TOTAL 37.91907638 7906.91925755 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 37788 +BPFP 0.0359 bits/point +EBPFP 0.0359 equivalent bits/point +MSE 7906.919258 +---------------------- -------------------------------------------------------- +Time: 5.096s Load: 0.052s, Pack+Encode: 2.582s, Decode+Unpack: 2.461s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7906.9193 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02219486-misc_3.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n02219486-misc_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02226429-tattoo_0.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02226429-tattoo_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,312B, BPFP=0.0234 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 39,856B, BPFP=0.0756 +⌛️ [2/4] FRONTEND: Frontend time: 2.610s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.470s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09506506 13.56969467 + layer.39.0 201.13660107 16825.20505345 + ------------------------------------------------------------------------------------- + TOTAL 100.61583306 8419.38737406 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52168 +BPFP 0.0495 bits/point +EBPFP 0.0495 equivalent bits/point +MSE 8419.387374 +---------------------- -------------------------------------------------------- +Time: 5.129s Load: 0.050s, Pack+Encode: 2.610s, Decode+Unpack: 2.470s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8419.3874 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02226429-tattoo_0.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n02226429-tattoo_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02233338-tattoo_5.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02233338-tattoo_5.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,428B, BPFP=0.0236 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 54,768B, BPFP=0.1040 +⌛️ [2/4] FRONTEND: Frontend time: 2.585s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.466s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09502332 13.49376765 + layer.39.0 172.43500972 15701.70942663 + ------------------------------------------------------------------------------------- + TOTAL 86.26501652 7857.60159714 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 67196 +BPFP 0.0638 bits/point +EBPFP 0.0638 equivalent bits/point +MSE 7857.601597 +---------------------- -------------------------------------------------------- +Time: 5.120s Load: 0.069s, Pack+Encode: 2.585s, Decode+Unpack: 2.466s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7857.6016 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02233338-tattoo_5.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n02233338-tattoo_5.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02279972-painting_2.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02279972-painting_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 15,128B, BPFP=0.0287 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 51,216B, BPFP=0.0972 +⌛️ [2/4] FRONTEND: Frontend time: 2.607s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.499s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11337867 13.35300979 + layer.39.0 361.17623299 16274.64625850 + ------------------------------------------------------------------------------------- + TOTAL 180.64480583 8143.99963414 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 66344 +BPFP 0.0630 bits/point +EBPFP 0.0630 equivalent bits/point +MSE 8143.999634 +---------------------- -------------------------------------------------------- +Time: 5.175s Load: 0.070s, Pack+Encode: 2.607s, Decode+Unpack: 2.499s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8143.9996 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02279972-painting_2.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n02279972-painting_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02317335-sculpture_0.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02317335-sculpture_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 11,852B, BPFP=0.0225 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 45,560B, BPFP=0.0865 +⌛️ [2/4] FRONTEND: Frontend time: 2.609s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.460s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09546056 13.67345705 + layer.39.0 1163.18707483 17730.27599611 + ------------------------------------------------------------------------------------- + TOTAL 581.64126769 8871.97472658 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 57412 +BPFP 0.0545 bits/point +EBPFP 0.0545 equivalent bits/point +MSE 8871.974727 +---------------------- -------------------------------------------------------- +Time: 5.120s Load: 0.051s, Pack+Encode: 2.609s, Decode+Unpack: 2.460s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8871.9747 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02317335-sculpture_0.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n02317335-sculpture_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02346627-painting_9.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02346627-painting_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 14,688B, BPFP=0.0279 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 47,168B, BPFP=0.0895 +⌛️ [2/4] FRONTEND: Frontend time: 2.600s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.460s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13205896 14.04072332 + layer.39.0 503.01482021 17705.64042760 + ------------------------------------------------------------------------------------- + TOTAL 251.57343959 8859.84057546 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 61856 +BPFP 0.0587 bits/point +EBPFP 0.0587 equivalent bits/point +MSE 8859.840575 +---------------------- -------------------------------------------------------- +Time: 5.110s Load: 0.051s, Pack+Encode: 2.600s, Decode+Unpack: 2.460s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8859.8406 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02346627-painting_9.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n02346627-painting_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02391049-deviantart_0.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02391049-deviantart_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,584B, BPFP=0.0239 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,756B, BPFP=0.0603 +⌛️ [2/4] FRONTEND: Frontend time: 2.583s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.464s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10116939 13.55238987 + layer.39.0 17.42674737 14465.97278912 + ------------------------------------------------------------------------------------- + TOTAL 8.76395838 7239.76258949 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 44340 +BPFP 0.0421 bits/point +EBPFP 0.0421 equivalent bits/point +MSE 7239.762589 +---------------------- -------------------------------------------------------- +Time: 5.116s Load: 0.069s, Pack+Encode: 2.583s, Decode+Unpack: 2.464s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7239.7626 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02391049-deviantart_0.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n02391049-deviantart_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02395406-sculpture_31.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02395406-sculpture_31.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 17,944B, BPFP=0.0341 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 42,204B, BPFP=0.0801 +⌛️ [2/4] FRONTEND: Frontend time: 2.612s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.467s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11469608 13.49810572 + layer.39.0 30.55020044 16569.37414966 + ------------------------------------------------------------------------------------- + TOTAL 15.33244826 8291.43612769 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 60148 +BPFP 0.0571 bits/point +EBPFP 0.0571 equivalent bits/point +MSE 8291.436128 +---------------------- -------------------------------------------------------- +Time: 5.128s Load: 0.050s, Pack+Encode: 2.612s, Decode+Unpack: 2.467s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8291.4361 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02395406-sculpture_31.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n02395406-sculpture_31.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02445715-art_3.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02445715-art_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,304B, BPFP=0.0234 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 49,940B, BPFP=0.0948 +⌛️ [2/4] FRONTEND: Frontend time: 2.632s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.458s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09587883 13.82103472 + layer.39.0 77.63827138 16595.35471331 + ------------------------------------------------------------------------------------- + TOTAL 38.86707511 8304.58787402 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 62244 +BPFP 0.0591 bits/point +EBPFP 0.0591 equivalent bits/point +MSE 8304.587874 +---------------------- -------------------------------------------------------- +Time: 5.160s Load: 0.071s, Pack+Encode: 2.632s, Decode+Unpack: 2.458s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8304.5879 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02445715-art_3.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n02445715-art_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02672831-sculpture_2.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02672831-sculpture_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 14,708B, BPFP=0.0279 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 75,624B, BPFP=0.1435 +⌛️ [2/4] FRONTEND: Frontend time: 2.590s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.459s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11638676 13.96362898 + layer.39.0 42.74346681 16131.56268222 + ------------------------------------------------------------------------------------- + TOTAL 21.42992678 8072.76315560 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 90332 +BPFP 0.0857 bits/point +EBPFP 0.0857 equivalent bits/point +MSE 8072.763156 +---------------------- -------------------------------------------------------- +Time: 5.119s Load: 0.071s, Pack+Encode: 2.590s, Decode+Unpack: 2.459s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8072.7632 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02672831-sculpture_2.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n02672831-sculpture_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02701002-videogame_1.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02701002-videogame_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 14,260B, BPFP=0.0271 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 49,736B, BPFP=0.0944 +⌛️ [2/4] FRONTEND: Frontend time: 2.585s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.479s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10320827 13.42114804 + layer.39.0 160.61054422 14835.25655977 + ------------------------------------------------------------------------------------- + TOTAL 80.35687624 7424.33885390 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 63996 +BPFP 0.0607 bits/point +EBPFP 0.0607 equivalent bits/point +MSE 7424.338854 +---------------------- -------------------------------------------------------- +Time: 5.145s Load: 0.080s, Pack+Encode: 2.585s, Decode+Unpack: 2.479s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7424.3389 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02701002-videogame_1.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n02701002-videogame_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02749479-misc_35.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02749479-misc_35.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,356B, BPFP=0.0254 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 51,720B, BPFP=0.0982 +⌛️ [2/4] FRONTEND: Frontend time: 2.582s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.461s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09764870 13.66112712 + layer.39.0 172.65676628 15206.35665695 + ------------------------------------------------------------------------------------- + TOTAL 86.37720749 7610.00889203 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 65076 +BPFP 0.0618 bits/point +EBPFP 0.0618 equivalent bits/point +MSE 7610.008892 +---------------------- -------------------------------------------------------- +Time: 5.092s Load: 0.050s, Pack+Encode: 2.582s, Decode+Unpack: 2.461s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7610.0089 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02749479-misc_35.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n02749479-misc_35.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02769748-cartoon_11.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02769748-cartoon_11.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,736B, BPFP=0.0242 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,864B, BPFP=0.0605 +⌛️ [2/4] FRONTEND: Frontend time: 2.588s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.466s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12263774 14.05921063 + layer.39.0 11.02823964 14568.34305151 + ------------------------------------------------------------------------------------- + TOTAL 5.57543869 7291.20113107 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 44600 +BPFP 0.0423 bits/point +EBPFP 0.0423 equivalent bits/point +MSE 7291.201131 +---------------------- -------------------------------------------------------- +Time: 5.105s Load: 0.052s, Pack+Encode: 2.588s, Decode+Unpack: 2.466s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7291.2011 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02769748-cartoon_11.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n02769748-cartoon_11.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02793495-sketch_11.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02793495-sketch_11.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.081s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 14,288B, BPFP=0.0271 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 42,368B, BPFP=0.0804 +⌛️ [2/4] FRONTEND: Frontend time: 2.593s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.488s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09793751 13.49508112 + layer.39.0 182.75789602 16309.71622935 + ------------------------------------------------------------------------------------- + TOTAL 91.42791676 8161.60565524 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 56656 +BPFP 0.0538 bits/point +EBPFP 0.0538 equivalent bits/point +MSE 8161.605655 +---------------------- -------------------------------------------------------- +Time: 5.162s Load: 0.081s, Pack+Encode: 2.593s, Decode+Unpack: 2.488s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8161.6057 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02793495-sketch_11.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n02793495-sketch_11.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02797295-misc_13.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02797295-misc_13.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 18,716B, BPFP=0.0355 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 58,116B, BPFP=0.1103 +⌛️ [2/4] FRONTEND: Frontend time: 2.571s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.466s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.17140635 13.83375661 + layer.39.0 172.50999150 14826.65889213 + ------------------------------------------------------------------------------------- + TOTAL 86.34069892 7420.24632437 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 76832 +BPFP 0.0729 bits/point +EBPFP 0.0729 equivalent bits/point +MSE 7420.246324 +---------------------- -------------------------------------------------------- +Time: 5.089s Load: 0.052s, Pack+Encode: 2.571s, Decode+Unpack: 2.466s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7420.2463 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02797295-misc_13.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n02797295-misc_13.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02802426-art_1.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02802426-art_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 14,752B, BPFP=0.0280 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 47,308B, BPFP=0.0898 +⌛️ [2/4] FRONTEND: Frontend time: 2.614s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.457s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.16523854 13.91635728 + layer.39.0 477.65184645 16489.23420797 + ------------------------------------------------------------------------------------- + TOTAL 238.90854250 8251.57528262 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 62060 +BPFP 0.0589 bits/point +EBPFP 0.0589 equivalent bits/point +MSE 8251.575283 +---------------------- -------------------------------------------------------- +Time: 5.151s Load: 0.080s, Pack+Encode: 2.614s, Decode+Unpack: 2.457s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8251.5753 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02802426-art_1.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n02802426-art_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02814860-sticker_8.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02814860-sticker_8.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,164B, BPFP=0.0250 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,688B, BPFP=0.0582 +⌛️ [2/4] FRONTEND: Frontend time: 2.571s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.473s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12757226 13.83713899 + layer.39.0 19.27598852 15609.20019436 + ------------------------------------------------------------------------------------- + TOTAL 9.70178039 7811.51866667 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 43852 +BPFP 0.0416 bits/point +EBPFP 0.0416 equivalent bits/point +MSE 7811.518667 +---------------------- -------------------------------------------------------- +Time: 5.095s Load: 0.052s, Pack+Encode: 2.571s, Decode+Unpack: 2.473s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7811.5187 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02814860-sticker_8.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n02814860-sticker_8.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02841315-graphic_4.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02841315-graphic_4.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,604B, BPFP=0.0258 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 58,780B, BPFP=0.1116 +⌛️ [2/4] FRONTEND: Frontend time: 2.596s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.475s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11826141 13.92420869 + layer.39.0 55.46440340 13931.15743440 + ------------------------------------------------------------------------------------- + TOTAL 27.79133240 6972.54082155 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 72384 +BPFP 0.0687 bits/point +EBPFP 0.0687 equivalent bits/point +MSE 6972.540822 +---------------------- -------------------------------------------------------- +Time: 5.123s Load: 0.051s, Pack+Encode: 2.596s, Decode+Unpack: 2.475s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 6972.5408 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02841315-graphic_4.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n02841315-graphic_4.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02843684-cartoon_9.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02843684-cartoon_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 15,540B, BPFP=0.0295 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,340B, BPFP=0.0595 +⌛️ [2/4] FRONTEND: Frontend time: 2.577s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.464s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12386809 13.95830296 + layer.39.0 312.00962707 16129.79883382 + ------------------------------------------------------------------------------------- + TOTAL 156.06674758 8071.87856839 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 46880 +BPFP 0.0445 bits/point +EBPFP 0.0445 equivalent bits/point +MSE 8071.878568 +---------------------- -------------------------------------------------------- +Time: 5.092s Load: 0.051s, Pack+Encode: 2.577s, Decode+Unpack: 2.464s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8071.8786 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02843684-cartoon_9.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n02843684-cartoon_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02883205-sketch_15.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02883205-sketch_15.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.049s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,636B, BPFP=0.0259 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 44,768B, BPFP=0.0850 +⌛️ [2/4] FRONTEND: Frontend time: 2.604s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.482s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09796664 13.83306570 + layer.39.0 103.64267493 14654.64042760 + ------------------------------------------------------------------------------------- + TOTAL 51.87032078 7334.23674665 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 58404 +BPFP 0.0554 bits/point +EBPFP 0.0554 equivalent bits/point +MSE 7334.236747 +---------------------- -------------------------------------------------------- +Time: 5.136s Load: 0.049s, Pack+Encode: 2.604s, Decode+Unpack: 2.482s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7334.2367 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02883205-sketch_15.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n02883205-sketch_15.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02906734-graffiti_3.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02906734-graffiti_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,212B, BPFP=0.0384 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 55,520B, BPFP=0.1054 +⌛️ [2/4] FRONTEND: Frontend time: 2.613s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.474s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.17339475 13.79320601 + layer.39.0 166.12656402 17115.11758989 + ------------------------------------------------------------------------------------- + TOTAL 83.14997939 8564.45539795 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 75732 +BPFP 0.0719 bits/point +EBPFP 0.0719 equivalent bits/point +MSE 8564.455398 +---------------------- -------------------------------------------------------- +Time: 5.158s Load: 0.070s, Pack+Encode: 2.613s, Decode+Unpack: 2.474s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8564.4554 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02906734-graffiti_3.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n02906734-graffiti_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02909870-sketch_0.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02909870-sketch_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 15,808B, BPFP=0.0300 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 39,676B, BPFP=0.0753 +⌛️ [2/4] FRONTEND: Frontend time: 2.595s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.478s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.15317524 13.86054612 + layer.39.0 167.75886783 16470.76773567 + ------------------------------------------------------------------------------------- + TOTAL 83.95602154 8242.31414089 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 55484 +BPFP 0.0527 bits/point +EBPFP 0.0527 equivalent bits/point +MSE 8242.314141 +---------------------- -------------------------------------------------------- +Time: 5.125s Load: 0.052s, Pack+Encode: 2.595s, Decode+Unpack: 2.478s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8242.3141 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02909870-sketch_0.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n02909870-sketch_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02939185-painting_0.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02939185-painting_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,312B, BPFP=0.0234 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 44,000B, BPFP=0.0835 +⌛️ [2/4] FRONTEND: Frontend time: 2.582s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.463s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09512242 13.75043466 + layer.39.0 131.28711127 17606.04081633 + ------------------------------------------------------------------------------------- + TOTAL 65.69111684 8809.89562549 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 56312 +BPFP 0.0534 bits/point +EBPFP 0.0534 equivalent bits/point +MSE 8809.895625 +---------------------- -------------------------------------------------------- +Time: 5.096s Load: 0.051s, Pack+Encode: 2.582s, Decode+Unpack: 2.463s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8809.8956 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02939185-painting_0.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n02939185-painting_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02948072-misc_10.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02948072-misc_10.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,276B, BPFP=0.0233 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 42,524B, BPFP=0.0807 +⌛️ [2/4] FRONTEND: Frontend time: 2.606s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.456s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09566823 13.62038387 + layer.39.0 102.81622783 16379.02235180 + ------------------------------------------------------------------------------------- + TOTAL 51.45594803 8196.32136783 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 54800 +BPFP 0.0520 bits/point +EBPFP 0.0520 equivalent bits/point +MSE 8196.321368 +---------------------- -------------------------------------------------------- +Time: 5.133s Load: 0.070s, Pack+Encode: 2.606s, Decode+Unpack: 2.456s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8196.3214 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02948072-misc_10.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n02948072-misc_10.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02950826-art_1.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02950826-art_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,032B, BPFP=0.0228 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 61,184B, BPFP=0.1161 +⌛️ [2/4] FRONTEND: Frontend time: 2.602s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.465s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09506074 13.53176533 + layer.39.0 1071.96149174 17119.73372206 + ------------------------------------------------------------------------------------- + TOTAL 536.02827624 8566.63274369 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 73216 +BPFP 0.0695 bits/point +EBPFP 0.0695 equivalent bits/point +MSE 8566.632744 +---------------------- -------------------------------------------------------- +Time: 5.146s Load: 0.080s, Pack+Encode: 2.602s, Decode+Unpack: 2.465s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8566.6327 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02950826-art_1.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n02950826-art_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02951358-misc_1.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02951358-misc_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,724B, BPFP=0.0260 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 37,756B, BPFP=0.0717 +⌛️ [2/4] FRONTEND: Frontend time: 2.585s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.463s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09568294 13.44735863 + layer.39.0 598.97078474 15017.56656948 + ------------------------------------------------------------------------------------- + TOTAL 299.53323384 7515.50696406 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51480 +BPFP 0.0489 bits/point +EBPFP 0.0489 equivalent bits/point +MSE 7515.506964 +---------------------- -------------------------------------------------------- +Time: 5.108s Load: 0.061s, Pack+Encode: 2.585s, Decode+Unpack: 2.463s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7515.5070 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02951358-misc_1.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n02951358-misc_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02966193-cartoon_22.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02966193-cartoon_22.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 18,372B, BPFP=0.0349 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 56,436B, BPFP=0.1071 +⌛️ [2/4] FRONTEND: Frontend time: 2.589s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.469s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10376222 13.48683966 + layer.39.0 767.85532070 15320.33333333 + ------------------------------------------------------------------------------------- + TOTAL 383.97954146 7666.91008650 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 74808 +BPFP 0.0710 bits/point +EBPFP 0.0710 equivalent bits/point +MSE 7666.910086 +---------------------- -------------------------------------------------------- +Time: 5.137s Load: 0.080s, Pack+Encode: 2.589s, Decode+Unpack: 2.469s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7666.9101 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02966193-cartoon_22.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n02966193-cartoon_22.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02980441-graphic_3.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02980441-graphic_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 14,032B, BPFP=0.0266 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 26,236B, BPFP=0.0498 +⌛️ [2/4] FRONTEND: Frontend time: 2.584s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.465s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09509088 13.73588872 + layer.39.0 13.13791359 14694.38872692 + ------------------------------------------------------------------------------------- + TOTAL 6.61650224 7354.06230782 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 40268 +BPFP 0.0382 bits/point +EBPFP 0.0382 equivalent bits/point +MSE 7354.062308 +---------------------- -------------------------------------------------------- +Time: 5.119s Load: 0.070s, Pack+Encode: 2.584s, Decode+Unpack: 2.465s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7354.0623 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02980441-graphic_3.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n02980441-graphic_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03124170-painting_15.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03124170-painting_15.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,648B, BPFP=0.0240 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 73,216B, BPFP=0.1390 +⌛️ [2/4] FRONTEND: Frontend time: 2.601s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.476s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10783903 13.84643958 + layer.39.0 326.57091229 16910.90767736 + ------------------------------------------------------------------------------------- + TOTAL 163.33937566 8462.37705847 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 85864 +BPFP 0.0815 bits/point +EBPFP 0.0815 equivalent bits/point +MSE 8462.377058 +---------------------- -------------------------------------------------------- +Time: 5.127s Load: 0.051s, Pack+Encode: 2.601s, Decode+Unpack: 2.476s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8462.3771 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03124170-painting_15.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n03124170-painting_15.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03345487-toy_17.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03345487-toy_17.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 14,208B, BPFP=0.0270 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 50,136B, BPFP=0.0952 +⌛️ [2/4] FRONTEND: Frontend time: 2.569s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.468s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10662318 13.64503899 + layer.39.0 198.63900024 15460.10204082 + ------------------------------------------------------------------------------------- + TOTAL 99.37281171 7736.87353990 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 64344 +BPFP 0.0611 bits/point +EBPFP 0.0611 equivalent bits/point +MSE 7736.873540 +---------------------- -------------------------------------------------------- +Time: 5.107s Load: 0.070s, Pack+Encode: 2.569s, Decode+Unpack: 2.468s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7736.8735 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03345487-toy_17.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n03345487-toy_17.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03372029-sketch_14.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03372029-sketch_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,688B, BPFP=0.0260 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 41,272B, BPFP=0.0783 +⌛️ [2/4] FRONTEND: Frontend time: 2.588s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.467s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12162214 13.89126370 + layer.39.0 228.06095117 17260.93877551 + ------------------------------------------------------------------------------------- + TOTAL 114.09128665 8637.41501961 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 54960 +BPFP 0.0522 bits/point +EBPFP 0.0522 equivalent bits/point +MSE 8637.415020 +---------------------- -------------------------------------------------------- +Time: 5.115s Load: 0.060s, Pack+Encode: 2.588s, Decode+Unpack: 2.467s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8637.4150 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03372029-sketch_14.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n03372029-sketch_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03424325-misc_4.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03424325-misc_4.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,452B, BPFP=0.0236 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 45,700B, BPFP=0.0867 +⌛️ [2/4] FRONTEND: Frontend time: 2.594s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.486s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10761499 13.44011935 + layer.39.0 21.03287666 14488.42371234 + ------------------------------------------------------------------------------------- + TOTAL 10.57024582 7250.93191585 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 58152 +BPFP 0.0552 bits/point +EBPFP 0.0552 equivalent bits/point +MSE 7250.931916 +---------------------- -------------------------------------------------------- +Time: 5.152s Load: 0.072s, Pack+Encode: 2.594s, Decode+Unpack: 2.486s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7250.9319 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03424325-misc_4.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n03424325-misc_4.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03467068-sketch_17.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03467068-sketch_17.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,116B, BPFP=0.0249 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 45,772B, BPFP=0.0869 +⌛️ [2/4] FRONTEND: Frontend time: 2.597s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.474s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09564773 13.42634023 + layer.39.0 208.14688107 18439.97278912 + ------------------------------------------------------------------------------------- + TOTAL 104.12126440 9226.69956468 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 58888 +BPFP 0.0559 bits/point +EBPFP 0.0559 equivalent bits/point +MSE 9226.699565 +---------------------- -------------------------------------------------------- +Time: 5.150s Load: 0.080s, Pack+Encode: 2.597s, Decode+Unpack: 2.474s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9226.6996 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03467068-sketch_17.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n03467068-sketch_17.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03481172-sketch_9.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03481172-sketch_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,992B, BPFP=0.0266 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 41,684B, BPFP=0.0791 +⌛️ [2/4] FRONTEND: Frontend time: 2.601s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.486s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14641065 13.61749499 + layer.39.0 516.28267736 17291.13896987 + ------------------------------------------------------------------------------------- + TOTAL 258.21454400 8652.37823243 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 55676 +BPFP 0.0528 bits/point +EBPFP 0.0528 equivalent bits/point +MSE 8652.378232 +---------------------- -------------------------------------------------------- +Time: 5.158s Load: 0.071s, Pack+Encode: 2.601s, Decode+Unpack: 2.486s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8652.3782 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03481172-sketch_9.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n03481172-sketch_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03494278-deviantart_3.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03494278-deviantart_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,356B, BPFP=0.0254 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 36,648B, BPFP=0.0696 +⌛️ [2/4] FRONTEND: Frontend time: 2.586s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.473s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09714438 13.69996849 + layer.39.0 11.38600982 14136.65208941 + ------------------------------------------------------------------------------------- + TOTAL 5.74157710 7075.17602895 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50004 +BPFP 0.0475 bits/point +EBPFP 0.0475 equivalent bits/point +MSE 7075.176029 +---------------------- -------------------------------------------------------- +Time: 5.130s Load: 0.070s, Pack+Encode: 2.586s, Decode+Unpack: 2.473s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7075.1760 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03494278-deviantart_3.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n03494278-deviantart_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03495258-painting_3.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03495258-painting_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 14,888B, BPFP=0.0283 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 62,380B, BPFP=0.1184 +⌛️ [2/4] FRONTEND: Frontend time: 2.578s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.481s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10398556 13.56648407 + layer.39.0 359.17207240 16460.19825073 + ------------------------------------------------------------------------------------- + TOTAL 179.63802898 8236.88236740 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 77268 +BPFP 0.0733 bits/point +EBPFP 0.0733 equivalent bits/point +MSE 8236.882367 +---------------------- -------------------------------------------------------- +Time: 5.129s Load: 0.070s, Pack+Encode: 2.578s, Decode+Unpack: 2.481s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8236.8824 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03495258-painting_3.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n03495258-painting_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03498962-sketch_8.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03498962-sketch_8.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.053s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 16,980B, BPFP=0.0322 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 38,260B, BPFP=0.0726 +⌛️ [2/4] FRONTEND: Frontend time: 2.568s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.462s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.16074808 13.88974239 + layer.39.0 476.99061589 16870.41010690 + ------------------------------------------------------------------------------------- + TOTAL 238.57568198 8442.14992465 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 55240 +BPFP 0.0524 bits/point +EBPFP 0.0524 equivalent bits/point +MSE 8442.149925 +---------------------- -------------------------------------------------------- +Time: 5.083s Load: 0.053s, Pack+Encode: 2.568s, Decode+Unpack: 2.462s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8442.1499 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03498962-sketch_8.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n03498962-sketch_8.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03602883-misc_12.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03602883-misc_12.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,144B, BPFP=0.0249 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 29,076B, BPFP=0.0552 +⌛️ [2/4] FRONTEND: Frontend time: 2.566s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.462s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.09080038 13.80963580 + layer.39.0 100.93773536 15298.71817298 + ------------------------------------------------------------------------------------- + TOTAL 54.51426787 7656.26390439 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 42220 +BPFP 0.0401 bits/point +EBPFP 0.0401 equivalent bits/point +MSE 7656.263904 +---------------------- -------------------------------------------------------- +Time: 5.097s Load: 0.070s, Pack+Encode: 2.566s, Decode+Unpack: 2.462s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7656.2639 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03602883-misc_12.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n03602883-misc_12.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03630383-toy_1.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03630383-toy_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 11,980B, BPFP=0.0227 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,688B, BPFP=0.0620 +⌛️ [2/4] FRONTEND: Frontend time: 2.580s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.480s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09574974 13.73597223 + layer.39.0 14.66923857 13644.83284742 + ------------------------------------------------------------------------------------- + TOTAL 7.38249415 6829.28440983 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 44668 +BPFP 0.0424 bits/point +EBPFP 0.0424 equivalent bits/point +MSE 6829.284410 +---------------------- -------------------------------------------------------- +Time: 5.112s Load: 0.052s, Pack+Encode: 2.580s, Decode+Unpack: 2.480s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 6829.2844 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03630383-toy_1.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n03630383-toy_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03649909-toy_22.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03649909-toy_22.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.081s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,088B, BPFP=0.0248 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 26,456B, BPFP=0.0502 +⌛️ [2/4] FRONTEND: Frontend time: 2.573s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.456s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09878858 13.61210729 + layer.39.0 29.68475348 15336.54130224 + ------------------------------------------------------------------------------------- + TOTAL 14.89177103 7675.07670476 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 39544 +BPFP 0.0375 bits/point +EBPFP 0.0375 equivalent bits/point +MSE 7675.076705 +---------------------- -------------------------------------------------------- +Time: 5.110s Load: 0.081s, Pack+Encode: 2.573s, Decode+Unpack: 2.456s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7675.0767 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03649909-toy_22.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n03649909-toy_22.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03676483-sculpture_3.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03676483-sculpture_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.092s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 11,532B, BPFP=0.0219 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 58,020B, BPFP=0.1101 +⌛️ [2/4] FRONTEND: Frontend time: 2.572s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.477s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09491264 13.70123261 + layer.39.0 32.22669916 16043.78911565 + ------------------------------------------------------------------------------------- + TOTAL 16.16080590 8028.74517413 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 69552 +BPFP 0.0660 bits/point +EBPFP 0.0660 equivalent bits/point +MSE 8028.745174 +---------------------- -------------------------------------------------------- +Time: 5.141s Load: 0.092s, Pack+Encode: 2.572s, Decode+Unpack: 2.477s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8028.7452 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03676483-sculpture_3.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n03676483-sculpture_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03710193-sketch_14.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03710193-sketch_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.056s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,456B, BPFP=0.0255 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 38,692B, BPFP=0.0734 +⌛️ [2/4] FRONTEND: Frontend time: 2.591s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.477s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.47394152 13.70709294 + layer.39.0 335.99814747 16943.43634597 + ------------------------------------------------------------------------------------- + TOTAL 168.23604450 8478.57171945 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52148 +BPFP 0.0495 bits/point +EBPFP 0.0495 equivalent bits/point +MSE 8478.571719 +---------------------- -------------------------------------------------------- +Time: 5.124s Load: 0.056s, Pack+Encode: 2.591s, Decode+Unpack: 2.477s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8478.5717 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03710193-sketch_14.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n03710193-sketch_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03773504-graphic_4.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03773504-graphic_4.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.079s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,092B, BPFP=0.0230 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 27,752B, BPFP=0.0527 +⌛️ [2/4] FRONTEND: Frontend time: 2.603s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.485s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09681199 13.96137975 + layer.39.0 18.83313593 15660.46161322 + ------------------------------------------------------------------------------------- + TOTAL 9.46497396 7837.21149648 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 39844 +BPFP 0.0378 bits/point +EBPFP 0.0378 equivalent bits/point +MSE 7837.211496 +---------------------- -------------------------------------------------------- +Time: 5.167s Load: 0.079s, Pack+Encode: 2.603s, Decode+Unpack: 2.485s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7837.2115 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03773504-graphic_4.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n03773504-graphic_4.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03775071-painting_1.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03775071-painting_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.053s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,356B, BPFP=0.0254 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 48,456B, BPFP=0.0920 +⌛️ [2/4] FRONTEND: Frontend time: 2.599s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.470s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11048905 13.88971962 + layer.39.0 386.73560496 17728.47424684 + ------------------------------------------------------------------------------------- + TOTAL 193.42304701 8871.18198323 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 61812 +BPFP 0.0587 bits/point +EBPFP 0.0587 equivalent bits/point +MSE 8871.181983 +---------------------- -------------------------------------------------------- +Time: 5.121s Load: 0.053s, Pack+Encode: 2.599s, Decode+Unpack: 2.470s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8871.1820 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03775071-painting_1.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n03775071-painting_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03888257-cartoon_30.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03888257-cartoon_30.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.079s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,100B, BPFP=0.0249 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 34,032B, BPFP=0.0646 +⌛️ [2/4] FRONTEND: Frontend time: 2.590s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.472s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13203045 13.79562416 + layer.39.0 375.96832483 16552.37123421 + ------------------------------------------------------------------------------------- + TOTAL 188.05017764 8283.08342919 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 47132 +BPFP 0.0447 bits/point +EBPFP 0.0447 equivalent bits/point +MSE 8283.083429 +---------------------- -------------------------------------------------------- +Time: 5.142s Load: 0.079s, Pack+Encode: 2.590s, Decode+Unpack: 2.472s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8283.0834 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03888257-cartoon_30.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n03888257-cartoon_30.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03930630-toy_2.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03930630-toy_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.053s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,364B, BPFP=0.0235 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 43,304B, BPFP=0.0822 +⌛️ [2/4] FRONTEND: Frontend time: 2.666s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.471s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09699417 13.88205042 + layer.39.0 46.17573949 14295.08260447 + ------------------------------------------------------------------------------------- + TOTAL 23.13636683 7154.48232745 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 55668 +BPFP 0.0528 bits/point +EBPFP 0.0528 equivalent bits/point +MSE 7154.482327 +---------------------- -------------------------------------------------------- +Time: 5.190s Load: 0.053s, Pack+Encode: 2.666s, Decode+Unpack: 2.471s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7154.4823 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03930630-toy_2.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n03930630-toy_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04086273-sticker_5.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04086273-sticker_5.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,876B, BPFP=0.0263 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 42,140B, BPFP=0.0800 +⌛️ [2/4] FRONTEND: Frontend time: 2.619s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.486s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10161624 13.59364086 + layer.39.0 24.98063198 16326.09523810 + ------------------------------------------------------------------------------------- + TOTAL 12.54112411 8169.84443948 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 56016 +BPFP 0.0532 bits/point +EBPFP 0.0532 equivalent bits/point +MSE 8169.844439 +---------------------- -------------------------------------------------------- +Time: 5.175s Load: 0.071s, Pack+Encode: 2.619s, Decode+Unpack: 2.486s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8169.8444 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04086273-sticker_5.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n04086273-sticker_5.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04118538-sculpture_0.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04118538-sculpture_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,140B, BPFP=0.0230 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 50,588B, BPFP=0.0960 +⌛️ [2/4] FRONTEND: Frontend time: 2.644s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.492s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09846411 13.56414279 + layer.39.0 11.87055944 15531.35179786 + ------------------------------------------------------------------------------------- + TOTAL 5.98451177 7772.45797033 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 62728 +BPFP 0.0595 bits/point +EBPFP 0.0595 equivalent bits/point +MSE 7772.457970 +---------------------- -------------------------------------------------------- +Time: 5.208s Load: 0.072s, Pack+Encode: 2.644s, Decode+Unpack: 2.492s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7772.4580 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04118538-sculpture_0.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n04118538-sculpture_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04133789-cartoon_7.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04133789-cartoon_7.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 17,036B, BPFP=0.0323 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 50,360B, BPFP=0.0956 +⌛️ [2/4] FRONTEND: Frontend time: 2.595s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.474s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13739287 13.85386867 + layer.39.0 370.52532799 17172.11078717 + ------------------------------------------------------------------------------------- + TOTAL 185.33136043 8592.98232792 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 67396 +BPFP 0.0640 bits/point +EBPFP 0.0640 equivalent bits/point +MSE 8592.982328 +---------------------- -------------------------------------------------------- +Time: 5.140s Load: 0.070s, Pack+Encode: 2.595s, Decode+Unpack: 2.474s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8592.9823 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04133789-cartoon_7.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n04133789-cartoon_7.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04141076-cartoon_14.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04141076-cartoon_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,636B, BPFP=0.0259 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,284B, BPFP=0.0575 +⌛️ [2/4] FRONTEND: Frontend time: 2.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.473s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11960477 13.68336124 + layer.39.0 53.25505649 15892.88241011 + ------------------------------------------------------------------------------------- + TOTAL 26.68733063 7953.28288567 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 43920 +BPFP 0.0417 bits/point +EBPFP 0.0417 equivalent bits/point +MSE 7953.282886 +---------------------- -------------------------------------------------------- +Time: 5.123s Load: 0.069s, Pack+Encode: 2.581s, Decode+Unpack: 2.473s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7953.2829 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04141076-cartoon_14.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n04141076-cartoon_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04146614-misc_4.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04146614-misc_4.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.091s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,976B, BPFP=0.0265 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 49,796B, BPFP=0.0945 +⌛️ [2/4] FRONTEND: Frontend time: 2.600s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.474s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10047569 13.50081997 + layer.39.0 167.29959305 16120.09620991 + ------------------------------------------------------------------------------------- + TOTAL 83.70003437 8066.79851494 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 63772 +BPFP 0.0605 bits/point +EBPFP 0.0605 equivalent bits/point +MSE 8066.798515 +---------------------- -------------------------------------------------------- +Time: 5.165s Load: 0.091s, Pack+Encode: 2.600s, Decode+Unpack: 2.474s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8066.7985 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04146614-misc_4.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n04146614-misc_4.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04147183-art_9.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04147183-art_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 14,680B, BPFP=0.0279 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 39,548B, BPFP=0.0751 +⌛️ [2/4] FRONTEND: Frontend time: 2.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.456s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11332939 13.76489898 + layer.39.0 22.95352360 15067.05928086 + ------------------------------------------------------------------------------------- + TOTAL 11.53342649 7540.41208992 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 54228 +BPFP 0.0515 bits/point +EBPFP 0.0515 equivalent bits/point +MSE 7540.412090 +---------------------- -------------------------------------------------------- +Time: 5.117s Load: 0.080s, Pack+Encode: 2.581s, Decode+Unpack: 2.456s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7540.4121 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04147183-art_9.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n04147183-art_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04192698-videogame_16.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04192698-videogame_16.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 15,576B, BPFP=0.0296 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 35,628B, BPFP=0.0676 +⌛️ [2/4] FRONTEND: Frontend time: 2.578s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.466s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09706018 13.45004726 + layer.39.0 404.66927843 17201.45383868 + ------------------------------------------------------------------------------------- + TOTAL 202.38316930 8607.45194297 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51204 +BPFP 0.0486 bits/point +EBPFP 0.0486 equivalent bits/point +MSE 8607.451943 +---------------------- -------------------------------------------------------- +Time: 5.095s Load: 0.051s, Pack+Encode: 2.578s, Decode+Unpack: 2.466s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8607.4519 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04192698-videogame_16.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n04192698-videogame_16.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04254680-deviantart_17.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04254680-deviantart_17.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,276B, BPFP=0.0252 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 38,188B, BPFP=0.0725 +⌛️ [2/4] FRONTEND: Frontend time: 2.579s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.481s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10685510 13.68692014 + layer.39.0 151.81593173 16794.05636540 + ------------------------------------------------------------------------------------- + TOTAL 75.96139341 8403.87164277 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51464 +BPFP 0.0488 bits/point +EBPFP 0.0488 equivalent bits/point +MSE 8403.871643 +---------------------- -------------------------------------------------------- +Time: 5.110s Load: 0.051s, Pack+Encode: 2.579s, Decode+Unpack: 2.481s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8403.8716 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04254680-deviantart_17.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n04254680-deviantart_17.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04266014-painting_13.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04266014-painting_13.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 15,892B, BPFP=0.0302 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 44,520B, BPFP=0.0845 +⌛️ [2/4] FRONTEND: Frontend time: 2.566s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.473s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09568562 13.94107409 + layer.39.0 29.62437363 16180.57531584 + ------------------------------------------------------------------------------------- + TOTAL 14.86002963 8097.25819496 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 60412 +BPFP 0.0573 bits/point +EBPFP 0.0573 equivalent bits/point +MSE 8097.258195 +---------------------- -------------------------------------------------------- +Time: 5.110s Load: 0.070s, Pack+Encode: 2.566s, Decode+Unpack: 2.473s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8097.2582 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04266014-painting_13.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n04266014-painting_13.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04310018-art_3.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04310018-art_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 18,076B, BPFP=0.0343 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 53,196B, BPFP=0.1010 +⌛️ [2/4] FRONTEND: Frontend time: 2.593s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.463s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13375617 13.63289127 + layer.39.0 75.24515610 15597.75898931 + ------------------------------------------------------------------------------------- + TOTAL 37.68945614 7805.69594029 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 71272 +BPFP 0.0676 bits/point +EBPFP 0.0676 equivalent bits/point +MSE 7805.695940 +---------------------- -------------------------------------------------------- +Time: 5.127s Load: 0.071s, Pack+Encode: 2.593s, Decode+Unpack: 2.463s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7805.6959 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04310018-art_3.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n04310018-art_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04347754-sculpture_0.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04347754-sculpture_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 17,464B, BPFP=0.0331 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,792B, BPFP=0.0603 +⌛️ [2/4] FRONTEND: Frontend time: 2.592s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.475s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14257451 13.77366907 + layer.39.0 394.23636419 16758.38678328 + ------------------------------------------------------------------------------------- + TOTAL 197.18946935 8386.08022618 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 49256 +BPFP 0.0467 bits/point +EBPFP 0.0467 equivalent bits/point +MSE 8386.080226 +---------------------- -------------------------------------------------------- +Time: 5.137s Load: 0.071s, Pack+Encode: 2.592s, Decode+Unpack: 2.475s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8386.0802 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04347754-sculpture_0.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n04347754-sculpture_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04409515-deviantart_1.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04409515-deviantart_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.068s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,680B, BPFP=0.0260 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 43,952B, BPFP=0.0834 +⌛️ [2/4] FRONTEND: Frontend time: 2.582s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.490s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09627266 13.70444796 + layer.39.0 9.33068077 14808.72886297 + ------------------------------------------------------------------------------------- + TOTAL 4.71347671 7411.21665547 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 57632 +BPFP 0.0547 bits/point +EBPFP 0.0547 equivalent bits/point +MSE 7411.216655 +---------------------- -------------------------------------------------------- +Time: 5.141s Load: 0.068s, Pack+Encode: 2.582s, Decode+Unpack: 2.490s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7411.2167 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04409515-deviantart_1.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n04409515-deviantart_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04487394-deviantart_8.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04487394-deviantart_8.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,588B, BPFP=0.0239 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 43,296B, BPFP=0.0822 +⌛️ [2/4] FRONTEND: Frontend time: 2.567s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.463s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09911632 13.83660183 + layer.39.0 99.63155977 16305.90281827 + ------------------------------------------------------------------------------------- + TOTAL 49.86533804 8159.86971005 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 55884 +BPFP 0.0530 bits/point +EBPFP 0.0530 equivalent bits/point +MSE 8159.869710 +---------------------- -------------------------------------------------------- +Time: 5.083s Load: 0.052s, Pack+Encode: 2.567s, Decode+Unpack: 2.463s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8159.8697 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04487394-deviantart_8.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n04487394-deviantart_8.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04522168-painting_32.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04522168-painting_32.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.081s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,216B, BPFP=0.0251 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 25,548B, BPFP=0.0485 +⌛️ [2/4] FRONTEND: Frontend time: 2.592s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.472s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11740584 13.85944808 + layer.39.0 10.95138066 14824.18950437 + ------------------------------------------------------------------------------------- + TOTAL 5.53439325 7419.02447622 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 38764 +BPFP 0.0368 bits/point +EBPFP 0.0368 equivalent bits/point +MSE 7419.024476 +---------------------- -------------------------------------------------------- +Time: 5.145s Load: 0.081s, Pack+Encode: 2.592s, Decode+Unpack: 2.472s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7419.0245 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04522168-painting_32.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n04522168-painting_32.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04591713-painting_14.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04591713-painting_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.081s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 14,988B, BPFP=0.0284 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 36,876B, BPFP=0.0700 +⌛️ [2/4] FRONTEND: Frontend time: 2.570s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.477s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11212821 13.91305936 + layer.39.0 165.22564383 17668.37317784 + ------------------------------------------------------------------------------------- + TOTAL 82.66888602 8841.14311860 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51864 +BPFP 0.0492 bits/point +EBPFP 0.0492 equivalent bits/point +MSE 8841.143119 +---------------------- -------------------------------------------------------- +Time: 5.128s Load: 0.081s, Pack+Encode: 2.570s, Decode+Unpack: 2.477s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8841.1431 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04591713-painting_14.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n04591713-painting_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07693725-sketch_15.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07693725-sketch_15.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 14,736B, BPFP=0.0280 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 44,808B, BPFP=0.0850 +⌛️ [2/4] FRONTEND: Frontend time: 2.589s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.469s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10569874 13.59803397 + layer.39.0 214.96065658 15601.91448008 + ------------------------------------------------------------------------------------- + TOTAL 107.53317766 7807.75625702 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 59544 +BPFP 0.0565 bits/point +EBPFP 0.0565 equivalent bits/point +MSE 7807.756257 +---------------------- -------------------------------------------------------- +Time: 5.109s Load: 0.051s, Pack+Encode: 2.589s, Decode+Unpack: 2.469s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7807.7563 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07693725-sketch_15.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n07693725-sketch_15.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07695742-videogame_1.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07695742-videogame_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 16,756B, BPFP=0.0318 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 42,504B, BPFP=0.0807 +⌛️ [2/4] FRONTEND: Frontend time: 2.602s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.492s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12460778 13.56555876 + layer.39.0 438.29433916 15588.81729835 + ------------------------------------------------------------------------------------- + TOTAL 219.20947347 7801.19142855 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 59260 +BPFP 0.0562 bits/point +EBPFP 0.0562 equivalent bits/point +MSE 7801.191429 +---------------------- -------------------------------------------------------- +Time: 5.146s Load: 0.052s, Pack+Encode: 2.602s, Decode+Unpack: 2.492s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7801.1914 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07695742-videogame_1.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n07695742-videogame_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697313-deviantart_7.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697313-deviantart_7.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,508B, BPFP=0.0256 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 53,376B, BPFP=0.1013 +⌛️ [2/4] FRONTEND: Frontend time: 2.615s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.469s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09520741 13.53196463 + layer.39.0 14.69109212 14523.66958212 + ------------------------------------------------------------------------------------- + TOTAL 7.39314977 7268.60077337 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 66884 +BPFP 0.0635 bits/point +EBPFP 0.0635 equivalent bits/point +MSE 7268.600773 +---------------------- -------------------------------------------------------- +Time: 5.136s Load: 0.052s, Pack+Encode: 2.615s, Decode+Unpack: 2.469s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7268.6008 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697313-deviantart_7.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n07697313-deviantart_7.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697537-deviantart_16.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697537-deviantart_16.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 15,768B, BPFP=0.0299 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 47,184B, BPFP=0.0896 +⌛️ [2/4] FRONTEND: Frontend time: 2.591s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.467s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09755328 13.31129757 + layer.39.0 90.32537658 14444.21088435 + ------------------------------------------------------------------------------------- + TOTAL 45.21146493 7228.76109096 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 62952 +BPFP 0.0597 bits/point +EBPFP 0.0597 equivalent bits/point +MSE 7228.761091 +---------------------- -------------------------------------------------------- +Time: 5.108s Load: 0.051s, Pack+Encode: 2.591s, Decode+Unpack: 2.467s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7228.7611 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697537-deviantart_16.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n07697537-deviantart_16.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714571-deviantart_0.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714571-deviantart_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,732B, BPFP=0.0242 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 58,224B, BPFP=0.1105 +⌛️ [2/4] FRONTEND: Frontend time: 2.586s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.462s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09528512 13.25153270 + layer.39.0 45.81401467 13267.16229349 + ------------------------------------------------------------------------------------- + TOTAL 22.95464989 6640.20691309 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 70956 +BPFP 0.0673 bits/point +EBPFP 0.0673 equivalent bits/point +MSE 6640.206913 +---------------------- -------------------------------------------------------- +Time: 5.099s Load: 0.051s, Pack+Encode: 2.586s, Decode+Unpack: 2.462s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 6640.2069 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714571-deviantart_0.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n07714571-deviantart_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714990-toy_14.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714990-toy_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.079s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,060B, BPFP=0.0229 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 59,064B, BPFP=0.1121 +⌛️ [2/4] FRONTEND: Frontend time: 2.590s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.470s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09793257 13.58955809 + layer.39.0 322.50334062 15618.87755102 + ------------------------------------------------------------------------------------- + TOTAL 161.30063660 7816.23355455 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 71124 +BPFP 0.0675 bits/point +EBPFP 0.0675 equivalent bits/point +MSE 7816.233555 +---------------------- -------------------------------------------------------- +Time: 5.140s Load: 0.079s, Pack+Encode: 2.590s, Decode+Unpack: 2.470s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7816.2336 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714990-toy_14.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n07714990-toy_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07718472-cartoon_1.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07718472-cartoon_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 17,608B, BPFP=0.0334 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 37,000B, BPFP=0.0702 +⌛️ [2/4] FRONTEND: Frontend time: 2.590s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.477s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11235230 13.79953326 + layer.39.0 14.49942963 14964.15549077 + ------------------------------------------------------------------------------------- + TOTAL 7.30589096 7488.97751201 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 54608 +BPFP 0.0518 bits/point +EBPFP 0.0518 equivalent bits/point +MSE 7488.977512 +---------------------- -------------------------------------------------------- +Time: 5.137s Load: 0.070s, Pack+Encode: 2.590s, Decode+Unpack: 2.477s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7488.9775 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07718472-cartoon_1.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n07718472-cartoon_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07742313-deviantart_8.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07742313-deviantart_8.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,280B, BPFP=0.0252 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 39,884B, BPFP=0.0757 +⌛️ [2/4] FRONTEND: Frontend time: 2.597s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.485s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09669835 13.84707259 + layer.39.0 8.77690150 14400.11175899 + ------------------------------------------------------------------------------------- + TOTAL 4.43679992 7206.97941579 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 53164 +BPFP 0.0505 bits/point +EBPFP 0.0505 equivalent bits/point +MSE 7206.979416 +---------------------- -------------------------------------------------------- +Time: 5.152s Load: 0.070s, Pack+Encode: 2.597s, Decode+Unpack: 2.485s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7206.9794 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07742313-deviantart_8.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n07742313-deviantart_8.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07749582-sticker_0.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07749582-sticker_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,728B, BPFP=0.0242 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 58,572B, BPFP=0.1112 +⌛️ [2/4] FRONTEND: Frontend time: 2.599s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.468s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09550123 13.52565066 + layer.39.0 34.64631545 17851.73372206 + ------------------------------------------------------------------------------------- + TOTAL 17.37090834 8932.62968636 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 71300 +BPFP 0.0677 bits/point +EBPFP 0.0677 equivalent bits/point +MSE 8932.629686 +---------------------- -------------------------------------------------------- +Time: 5.147s Load: 0.080s, Pack+Encode: 2.599s, Decode+Unpack: 2.468s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8932.6297 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07749582-sticker_0.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n07749582-sticker_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07753275-painting_2.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07753275-painting_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.058s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 18,936B, BPFP=0.0359 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 68,916B, BPFP=0.1308 +⌛️ [2/4] FRONTEND: Frontend time: 2.593s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.458s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10429548 13.74174430 + layer.39.0 540.43106171 21022.05636540 + ------------------------------------------------------------------------------------- + TOTAL 270.26767859 10517.89905485 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 87852 +BPFP 0.0834 bits/point +EBPFP 0.0834 equivalent bits/point +MSE 10517.899055 +---------------------- -------------------------------------------------------- +Time: 5.109s Load: 0.058s, Pack+Encode: 2.593s, Decode+Unpack: 2.458s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10517.8991 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07753275-painting_2.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n07753275-painting_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07768694-painting_12.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07768694-painting_12.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 14,600B, BPFP=0.0277 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 55,964B, BPFP=0.1062 +⌛️ [2/4] FRONTEND: Frontend time: 2.592s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.489s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09821300 13.45201462 + layer.39.0 635.68343052 19345.81924198 + ------------------------------------------------------------------------------------- + TOTAL 317.89082176 9679.63562830 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 70564 +BPFP 0.0670 bits/point +EBPFP 0.0670 equivalent bits/point +MSE 9679.635628 +---------------------- -------------------------------------------------------- +Time: 5.151s Load: 0.070s, Pack+Encode: 2.592s, Decode+Unpack: 2.489s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9679.6356 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07768694-painting_12.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n07768694-painting_12.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07920052-painting_1.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07920052-painting_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,968B, BPFP=0.0246 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 55,172B, BPFP=0.1047 +⌛️ [2/4] FRONTEND: Frontend time: 2.585s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.498s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09582097 13.62665797 + layer.39.0 9.59182155 15480.23615160 + ------------------------------------------------------------------------------------- + TOTAL 4.84382126 7746.93140479 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 68140 +BPFP 0.0647 bits/point +EBPFP 0.0647 equivalent bits/point +MSE 7746.931405 +---------------------- -------------------------------------------------------- +Time: 5.153s Load: 0.071s, Pack+Encode: 2.585s, Decode+Unpack: 2.498s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7746.9314 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07920052-painting_1.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n07920052-painting_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09472597-painting_15.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09472597-painting_15.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.081s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,508B, BPFP=0.0237 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 39,052B, BPFP=0.0741 +⌛️ [2/4] FRONTEND: Frontend time: 2.590s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.477s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09164813 13.90288755 + layer.39.0 9.11265014 15944.68804665 + ------------------------------------------------------------------------------------- + TOTAL 4.60214913 7979.29546710 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51560 +BPFP 0.0489 bits/point +EBPFP 0.0489 equivalent bits/point +MSE 7979.295467 +---------------------- -------------------------------------------------------- +Time: 5.148s Load: 0.081s, Pack+Encode: 2.590s, Decode+Unpack: 2.477s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7979.2955 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09472597-painting_15.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n09472597-painting_15.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09835506-videogame_3.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09835506-videogame_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,740B, BPFP=0.0242 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 54,360B, BPFP=0.1032 +⌛️ [2/4] FRONTEND: Frontend time: 2.609s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.471s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09585661 13.44049232 + layer.39.0 12.34450164 15155.11758989 + ------------------------------------------------------------------------------------- + TOTAL 6.22017912 7584.27904111 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 67100 +BPFP 0.0637 bits/point +EBPFP 0.0637 equivalent bits/point +MSE 7584.279041 +---------------------- -------------------------------------------------------- +Time: 5.151s Load: 0.071s, Pack+Encode: 2.609s, Decode+Unpack: 2.471s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7584.2790 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09835506-videogame_3.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n09835506-videogame_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n12267677-misc_105.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n12267677-misc_105.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.082s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,972B, BPFP=0.0246 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 34,124B, BPFP=0.0648 +⌛️ [2/4] FRONTEND: Frontend time: 2.582s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.475s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10166193 13.81215929 + layer.39.0 219.41089650 15697.55685131 + ------------------------------------------------------------------------------------- + TOTAL 109.75627921 7855.68450530 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 47096 +BPFP 0.0447 bits/point +EBPFP 0.0447 equivalent bits/point +MSE 7855.684505 +---------------------- -------------------------------------------------------- +Time: 5.139s Load: 0.082s, Pack+Encode: 2.582s, Decode+Unpack: 2.475s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7855.6845 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n12267677-misc_105.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kr/n12267677-misc_105.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 0.0557 bits/point +Avg EBPFP 0.0557 equivalent bits/point +Avg MSE 7992.353611 +Avg Time 5.136s +------------------------ ---------------------------- diff --git a/lambda0.001/elic-featurecoding-8bit-individual/cls_in1kval/dtufc_elic-featurecoding_dinov3-total_individual.log b/lambda0.001/elic-featurecoding-8bit-individual/cls_in1kval/dtufc_elic-featurecoding_dinov3-total_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..3f7d62dfe6c466b1645f31355c1b03a695eada10 --- /dev/null +++ b/lambda0.001/elic-featurecoding-8bit-individual/cls_in1kval/dtufc_elic-featurecoding_dinov3-total_individual.log @@ -0,0 +1,9344 @@ +Experiment: dtufc_elic-featurecoding_dinov3-total_individual +Log file: output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/dtufc_elic-featurecoding_dinov3-total_individual.log +DTUFCCodecConfig: + arch: elic-featurecoding + handler: dinov3-total + checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.001_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.001_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 286 +Loaded elic-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.9' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.19' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.29' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.39' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json +Loaded per-key mappings: model=dinov3-total + Keys: ['layer.9', 'layer.19', 'layer.29', 'layer.39'] +---------------- -------------------------------------------------------------------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +Checkpoint codec_weights/elic_hybrid/elic2022-official_lambda0.001_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-DINOv3/imagenet1k-val +Output output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval +---------------- -------------------------------------------------------------------------------------------------------------------- +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02825657-ILSVRC2012_val_00001103.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02825657-ILSVRC2012_val_00001103.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.081s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,112B, BPFP=0.0249 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 23,268B, BPFP=0.0442 +⌛️ [2/4] FRONTEND: Frontend time: 3.012s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.498s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10264289 13.60667688 + layer.39.0 9.47367932 14162.22837707 + ------------------------------------------------------------------------------------- + TOTAL 4.78816110 7087.91752697 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 36380 +BPFP 0.0345 bits/point +EBPFP 0.0345 equivalent bits/point +MSE 7087.917527 +---------------------- -------------------------------------------------------- +Time: 5.591s Load: 0.081s, Pack+Encode: 3.012s, Decode+Unpack: 2.498s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7087.9175 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02825657-ILSVRC2012_val_00001103.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n02825657-ILSVRC2012_val_00001103.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02834397-ILSVRC2012_val_00001252.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02834397-ILSVRC2012_val_00001252.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,308B, BPFP=0.0366 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 51,056B, BPFP=0.0969 +⌛️ [2/4] FRONTEND: Frontend time: 2.648s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.473s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14789204 13.85416002 + layer.39.0 415.43227648 17346.29931973 + ------------------------------------------------------------------------------------- + TOTAL 207.79008426 8680.07673988 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 70364 +BPFP 0.0668 bits/point +EBPFP 0.0668 equivalent bits/point +MSE 8680.076740 +---------------------- -------------------------------------------------------- +Time: 5.192s Load: 0.071s, Pack+Encode: 2.648s, Decode+Unpack: 2.473s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8680.0767 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02834397-ILSVRC2012_val_00001252.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n02834397-ILSVRC2012_val_00001252.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02840245-ILSVRC2012_val_00003446.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02840245-ILSVRC2012_val_00003446.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,340B, BPFP=0.0253 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 41,828B, BPFP=0.0794 +⌛️ [2/4] FRONTEND: Frontend time: 2.633s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.475s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10761288 13.76405054 + layer.39.0 28.71820525 14726.36734694 + ------------------------------------------------------------------------------------- + TOTAL 14.41290906 7370.06569874 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 55168 +BPFP 0.0524 bits/point +EBPFP 0.0524 equivalent bits/point +MSE 7370.065699 +---------------------- -------------------------------------------------------- +Time: 5.177s Load: 0.070s, Pack+Encode: 2.633s, Decode+Unpack: 2.475s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7370.0657 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02840245-ILSVRC2012_val_00003446.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n02840245-ILSVRC2012_val_00003446.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02843684-ILSVRC2012_val_00000514.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02843684-ILSVRC2012_val_00000514.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,908B, BPFP=0.0245 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 36,464B, BPFP=0.0692 +⌛️ [2/4] FRONTEND: Frontend time: 2.640s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.471s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11482661 13.66084905 + layer.39.0 84.54469600 14798.94169096 + ------------------------------------------------------------------------------------- + TOTAL 42.32976130 7406.30127001 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 49372 +BPFP 0.0469 bits/point +EBPFP 0.0469 equivalent bits/point +MSE 7406.301270 +---------------------- -------------------------------------------------------- +Time: 5.181s Load: 0.071s, Pack+Encode: 2.640s, Decode+Unpack: 2.471s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7406.3013 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02843684-ILSVRC2012_val_00000514.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n02843684-ILSVRC2012_val_00000514.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02859443-ILSVRC2012_val_00000193.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02859443-ILSVRC2012_val_00000193.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 14,904B, BPFP=0.0283 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 25,936B, BPFP=0.0492 +⌛️ [2/4] FRONTEND: Frontend time: 2.649s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.464s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11417333 14.04087042 + layer.39.0 9.67809406 12963.09815355 + ------------------------------------------------------------------------------------- + TOTAL 4.89613370 6488.56951198 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 40840 +BPFP 0.0388 bits/point +EBPFP 0.0388 equivalent bits/point +MSE 6488.569512 +---------------------- -------------------------------------------------------- +Time: 5.162s Load: 0.050s, Pack+Encode: 2.649s, Decode+Unpack: 2.464s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 6488.5695 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02859443-ILSVRC2012_val_00000193.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n02859443-ILSVRC2012_val_00000193.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02860847-ILSVRC2012_val_00000601.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02860847-ILSVRC2012_val_00000601.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.058s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 16,416B, BPFP=0.0312 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 46,304B, BPFP=0.0879 +⌛️ [2/4] FRONTEND: Frontend time: 2.656s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.498s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12653054 13.39450088 + layer.39.0 266.35249636 14433.00680272 + ------------------------------------------------------------------------------------- + TOTAL 133.23951345 7223.20065180 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 62720 +BPFP 0.0595 bits/point +EBPFP 0.0595 equivalent bits/point +MSE 7223.200652 +---------------------- -------------------------------------------------------- +Time: 5.212s Load: 0.058s, Pack+Encode: 2.656s, Decode+Unpack: 2.498s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7223.2007 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02860847-ILSVRC2012_val_00000601.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n02860847-ILSVRC2012_val_00000601.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02865351-ILSVRC2012_val_00000763.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02865351-ILSVRC2012_val_00000763.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,788B, BPFP=0.0243 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 57,008B, BPFP=0.1082 +⌛️ [2/4] FRONTEND: Frontend time: 2.648s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.468s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09467571 13.82955046 + layer.39.0 15.47581086 15038.90864917 + ------------------------------------------------------------------------------------- + TOTAL 7.78524328 7526.36909982 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 69796 +BPFP 0.0662 bits/point +EBPFP 0.0662 equivalent bits/point +MSE 7526.369100 +---------------------- -------------------------------------------------------- +Time: 5.167s Load: 0.051s, Pack+Encode: 2.648s, Decode+Unpack: 2.468s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7526.3691 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02865351-ILSVRC2012_val_00000763.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n02865351-ILSVRC2012_val_00000763.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02869837-ILSVRC2012_val_00000906.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02869837-ILSVRC2012_val_00000906.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,012B, BPFP=0.0247 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 47,776B, BPFP=0.0907 +⌛️ [2/4] FRONTEND: Frontend time: 2.640s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.481s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09659988 13.70948642 + layer.39.0 16.39405483 13749.77745384 + ------------------------------------------------------------------------------------- + TOTAL 8.24532736 6881.74347013 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 60788 +BPFP 0.0577 bits/point +EBPFP 0.0577 equivalent bits/point +MSE 6881.743470 +---------------------- -------------------------------------------------------- +Time: 5.183s Load: 0.061s, Pack+Encode: 2.640s, Decode+Unpack: 2.481s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 6881.7435 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02869837-ILSVRC2012_val_00000906.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n02869837-ILSVRC2012_val_00000906.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02870880-ILSVRC2012_val_00003274.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02870880-ILSVRC2012_val_00003274.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 16,424B, BPFP=0.0312 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 49,980B, BPFP=0.0949 +⌛️ [2/4] FRONTEND: Frontend time: 2.643s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.476s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10254154 13.86088208 + layer.39.0 9.36513093 12470.46064140 + ------------------------------------------------------------------------------------- + TOTAL 4.73383623 6242.16076174 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 66404 +BPFP 0.0630 bits/point +EBPFP 0.0630 equivalent bits/point +MSE 6242.160762 +---------------------- -------------------------------------------------------- +Time: 5.178s Load: 0.059s, Pack+Encode: 2.643s, Decode+Unpack: 2.476s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 6242.1608 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02870880-ILSVRC2012_val_00003274.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n02870880-ILSVRC2012_val_00003274.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02871525-ILSVRC2012_val_00000879.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02871525-ILSVRC2012_val_00000879.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 18,476B, BPFP=0.0351 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 43,728B, BPFP=0.0830 +⌛️ [2/4] FRONTEND: Frontend time: 2.649s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.472s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.17072899 13.72002171 + layer.39.0 20.29403547 16620.36734694 + ------------------------------------------------------------------------------------- + TOTAL 10.23238223 8317.04368433 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 62204 +BPFP 0.0590 bits/point +EBPFP 0.0590 equivalent bits/point +MSE 8317.043684 +---------------------- -------------------------------------------------------- +Time: 5.172s Load: 0.051s, Pack+Encode: 2.649s, Decode+Unpack: 2.472s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8317.0437 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02871525-ILSVRC2012_val_00000879.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n02871525-ILSVRC2012_val_00000879.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02877765-ILSVRC2012_val_00000634.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02877765-ILSVRC2012_val_00000634.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,736B, BPFP=0.0242 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 57,908B, BPFP=0.1099 +⌛️ [2/4] FRONTEND: Frontend time: 2.663s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.485s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10908128 13.42162445 + layer.39.0 364.97770894 16378.84159378 + ------------------------------------------------------------------------------------- + TOTAL 182.54339511 8196.13160912 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 70644 +BPFP 0.0670 bits/point +EBPFP 0.0670 equivalent bits/point +MSE 8196.131609 +---------------------- -------------------------------------------------------- +Time: 5.200s Load: 0.051s, Pack+Encode: 2.663s, Decode+Unpack: 2.485s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8196.1316 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02877765-ILSVRC2012_val_00000634.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n02877765-ILSVRC2012_val_00000634.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02879718-ILSVRC2012_val_00001354.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02879718-ILSVRC2012_val_00001354.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,512B, BPFP=0.0256 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 39,868B, BPFP=0.0757 +⌛️ [2/4] FRONTEND: Frontend time: 2.637s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.481s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10948122 13.62848962 + layer.39.0 55.92460444 16033.19533528 + ------------------------------------------------------------------------------------- + TOTAL 28.01704283 8023.41191245 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 53380 +BPFP 0.0507 bits/point +EBPFP 0.0507 equivalent bits/point +MSE 8023.411912 +---------------------- -------------------------------------------------------- +Time: 5.179s Load: 0.061s, Pack+Encode: 2.637s, Decode+Unpack: 2.481s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8023.4119 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02879718-ILSVRC2012_val_00001354.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n02879718-ILSVRC2012_val_00001354.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02883205-ILSVRC2012_val_00000126.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02883205-ILSVRC2012_val_00000126.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,088B, BPFP=0.0229 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,372B, BPFP=0.0576 +⌛️ [2/4] FRONTEND: Frontend time: 2.643s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.480s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.06711708 13.93121830 + layer.39.0 7.82069686 14625.97084548 + ------------------------------------------------------------------------------------- + TOTAL 7.94390697 7319.95103189 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 42460 +BPFP 0.0403 bits/point +EBPFP 0.0403 equivalent bits/point +MSE 7319.951032 +---------------------- -------------------------------------------------------- +Time: 5.175s Load: 0.051s, Pack+Encode: 2.643s, Decode+Unpack: 2.480s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7319.9510 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02883205-ILSVRC2012_val_00000126.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n02883205-ILSVRC2012_val_00000126.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892201-ILSVRC2012_val_00001145.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892201-ILSVRC2012_val_00001145.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 16,412B, BPFP=0.0312 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 50,072B, BPFP=0.0950 +⌛️ [2/4] FRONTEND: Frontend time: 2.657s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.480s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11297333 13.76142455 + layer.39.0 15.09638643 16854.03109815 + ------------------------------------------------------------------------------------- + TOTAL 7.60467988 8433.89626135 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 66484 +BPFP 0.0631 bits/point +EBPFP 0.0631 equivalent bits/point +MSE 8433.896261 +---------------------- -------------------------------------------------------- +Time: 5.208s Load: 0.071s, Pack+Encode: 2.657s, Decode+Unpack: 2.480s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8433.8963 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892201-ILSVRC2012_val_00001145.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n02892201-ILSVRC2012_val_00001145.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892767-ILSVRC2012_val_00000808.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892767-ILSVRC2012_val_00000808.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 11,384B, BPFP=0.0216 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 48,876B, BPFP=0.0928 +⌛️ [2/4] FRONTEND: Frontend time: 2.644s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.484s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09598007 13.49164465 + layer.39.0 31.15013059 14243.46452867 + ------------------------------------------------------------------------------------- + TOTAL 15.62305533 7128.47808666 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 60260 +BPFP 0.0572 bits/point +EBPFP 0.0572 equivalent bits/point +MSE 7128.478087 +---------------------- -------------------------------------------------------- +Time: 5.198s Load: 0.070s, Pack+Encode: 2.644s, Decode+Unpack: 2.484s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7128.4781 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892767-ILSVRC2012_val_00000808.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n02892767-ILSVRC2012_val_00000808.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02895154-ILSVRC2012_val_00000080.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02895154-ILSVRC2012_val_00000080.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.058s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,176B, BPFP=0.0250 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 62,768B, BPFP=0.1191 +⌛️ [2/4] FRONTEND: Frontend time: 2.623s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.474s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09530723 13.64507980 + layer.39.0 971.40427600 17823.57045675 + ------------------------------------------------------------------------------------- + TOTAL 485.74979162 8918.60776827 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 75944 +BPFP 0.0721 bits/point +EBPFP 0.0721 equivalent bits/point +MSE 8918.607768 +---------------------- -------------------------------------------------------- +Time: 5.155s Load: 0.058s, Pack+Encode: 2.623s, Decode+Unpack: 2.474s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8918.6078 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02895154-ILSVRC2012_val_00000080.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n02895154-ILSVRC2012_val_00000080.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02906734-ILSVRC2012_val_00002937.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02906734-ILSVRC2012_val_00002937.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,852B, BPFP=0.0263 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 46,452B, BPFP=0.0882 +⌛️ [2/4] FRONTEND: Frontend time: 2.631s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.479s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09767962 13.21733973 + layer.39.0 32.09536716 17615.22837707 + ------------------------------------------------------------------------------------- + TOTAL 16.09652339 8814.22285840 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 60304 +BPFP 0.0572 bits/point +EBPFP 0.0572 equivalent bits/point +MSE 8814.222858 +---------------------- -------------------------------------------------------- +Time: 5.180s Load: 0.070s, Pack+Encode: 2.631s, Decode+Unpack: 2.479s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8814.2229 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02906734-ILSVRC2012_val_00002937.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n02906734-ILSVRC2012_val_00002937.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02910353-ILSVRC2012_val_00000558.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02910353-ILSVRC2012_val_00000558.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 17,092B, BPFP=0.0324 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 53,552B, BPFP=0.1016 +⌛️ [2/4] FRONTEND: Frontend time: 2.657s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.501s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11017090 13.73825088 + layer.39.0 483.40066205 15776.84936832 + ------------------------------------------------------------------------------------- + TOTAL 241.75541648 7895.29380960 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 70644 +BPFP 0.0670 bits/point +EBPFP 0.0670 equivalent bits/point +MSE 7895.293810 +---------------------- -------------------------------------------------------- +Time: 5.208s Load: 0.051s, Pack+Encode: 2.657s, Decode+Unpack: 2.501s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7895.2938 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02910353-ILSVRC2012_val_00000558.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n02910353-ILSVRC2012_val_00000558.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02916936-ILSVRC2012_val_00000366.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02916936-ILSVRC2012_val_00000366.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,824B, BPFP=0.0262 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 50,168B, BPFP=0.0952 +⌛️ [2/4] FRONTEND: Frontend time: 2.645s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.486s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10657579 13.67212650 + layer.39.0 435.18944363 15449.24878523 + ------------------------------------------------------------------------------------- + TOTAL 217.64800971 7731.46045586 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 63992 +BPFP 0.0607 bits/point +EBPFP 0.0607 equivalent bits/point +MSE 7731.460456 +---------------------- -------------------------------------------------------- +Time: 5.182s Load: 0.051s, Pack+Encode: 2.645s, Decode+Unpack: 2.486s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7731.4605 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02916936-ILSVRC2012_val_00000366.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n02916936-ILSVRC2012_val_00000366.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02917067-ILSVRC2012_val_00000562.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02917067-ILSVRC2012_val_00000562.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 15,124B, BPFP=0.0287 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 65,744B, BPFP=0.1248 +⌛️ [2/4] FRONTEND: Frontend time: 2.647s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.486s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10760244 13.89208367 + layer.39.0 37.55795979 16164.65500486 + ------------------------------------------------------------------------------------- + TOTAL 18.83278111 8089.27354427 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 80868 +BPFP 0.0767 bits/point +EBPFP 0.0767 equivalent bits/point +MSE 8089.273544 +---------------------- -------------------------------------------------------- +Time: 5.185s Load: 0.052s, Pack+Encode: 2.647s, Decode+Unpack: 2.486s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8089.2735 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02917067-ILSVRC2012_val_00000562.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n02917067-ILSVRC2012_val_00000562.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02930766-ILSVRC2012_val_00000056.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02930766-ILSVRC2012_val_00000056.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,712B, BPFP=0.0260 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 60,368B, BPFP=0.1146 +⌛️ [2/4] FRONTEND: Frontend time: 2.646s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.474s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10591127 13.75571892 + layer.39.0 18.32421875 15139.83187561 + ------------------------------------------------------------------------------------- + TOTAL 9.21506501 7576.79379726 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 74080 +BPFP 0.0703 bits/point +EBPFP 0.0703 equivalent bits/point +MSE 7576.793797 +---------------------- -------------------------------------------------------- +Time: 5.171s Load: 0.051s, Pack+Encode: 2.646s, Decode+Unpack: 2.474s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7576.7938 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02930766-ILSVRC2012_val_00000056.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n02930766-ILSVRC2012_val_00000056.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02939185-ILSVRC2012_val_00000302.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02939185-ILSVRC2012_val_00000302.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,972B, BPFP=0.0246 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 52,004B, BPFP=0.0987 +⌛️ [2/4] FRONTEND: Frontend time: 2.657s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.484s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09694758 13.63697879 + layer.39.0 25.52453269 15988.19436346 + ------------------------------------------------------------------------------------- + TOTAL 12.81074014 8000.91567112 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 64976 +BPFP 0.0617 bits/point +EBPFP 0.0617 equivalent bits/point +MSE 8000.915671 +---------------------- -------------------------------------------------------- +Time: 5.203s Load: 0.061s, Pack+Encode: 2.657s, Decode+Unpack: 2.484s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8000.9157 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02939185-ILSVRC2012_val_00000302.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n02939185-ILSVRC2012_val_00000302.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02950826-ILSVRC2012_val_00000392.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02950826-ILSVRC2012_val_00000392.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 14,128B, BPFP=0.0268 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 46,748B, BPFP=0.0887 +⌛️ [2/4] FRONTEND: Frontend time: 2.635s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.480s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10873010 13.74500900 + layer.39.0 707.96944849 15790.79980564 + ------------------------------------------------------------------------------------- + TOTAL 354.03908930 7902.27240732 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 60876 +BPFP 0.0578 bits/point +EBPFP 0.0578 equivalent bits/point +MSE 7902.272407 +---------------------- -------------------------------------------------------- +Time: 5.177s Load: 0.062s, Pack+Encode: 2.635s, Decode+Unpack: 2.480s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7902.2724 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02950826-ILSVRC2012_val_00000392.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n02950826-ILSVRC2012_val_00000392.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951358-ILSVRC2012_val_00000347.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951358-ILSVRC2012_val_00000347.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 15,864B, BPFP=0.0301 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 38,544B, BPFP=0.0732 +⌛️ [2/4] FRONTEND: Frontend time: 2.626s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.473s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12200860 13.60282188 + layer.39.0 237.66299198 15908.08551992 + ------------------------------------------------------------------------------------- + TOTAL 118.89250029 7960.84417090 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 54408 +BPFP 0.0516 bits/point +EBPFP 0.0516 equivalent bits/point +MSE 7960.844171 +---------------------- -------------------------------------------------------- +Time: 5.151s Load: 0.052s, Pack+Encode: 2.626s, Decode+Unpack: 2.473s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7960.8442 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951358-ILSVRC2012_val_00000347.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n02951358-ILSVRC2012_val_00000347.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951585-ILSVRC2012_val_00000101.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951585-ILSVRC2012_val_00000101.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,716B, BPFP=0.0241 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 48,688B, BPFP=0.0924 +⌛️ [2/4] FRONTEND: Frontend time: 2.678s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.472s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.07385432 13.73458664 + layer.39.0 181.90962099 15534.39747328 + ------------------------------------------------------------------------------------- + TOTAL 94.99173765 7774.06602996 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 61404 +BPFP 0.0583 bits/point +EBPFP 0.0583 equivalent bits/point +MSE 7774.066030 +---------------------- -------------------------------------------------------- +Time: 5.201s Load: 0.051s, Pack+Encode: 2.678s, Decode+Unpack: 2.472s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7774.0660 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951585-ILSVRC2012_val_00000101.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n02951585-ILSVRC2012_val_00000101.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02963159-ILSVRC2012_val_00000061.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02963159-ILSVRC2012_val_00000061.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,896B, BPFP=0.0245 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 40,444B, BPFP=0.0768 +⌛️ [2/4] FRONTEND: Frontend time: 2.660s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.472s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11232698 13.57566414 + layer.39.0 24.77479842 14984.36540330 + ------------------------------------------------------------------------------------- + TOTAL 12.44356270 7498.97053372 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 53340 +BPFP 0.0506 bits/point +EBPFP 0.0506 equivalent bits/point +MSE 7498.970534 +---------------------- -------------------------------------------------------- +Time: 5.183s Load: 0.051s, Pack+Encode: 2.660s, Decode+Unpack: 2.472s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7498.9705 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02963159-ILSVRC2012_val_00000061.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n02963159-ILSVRC2012_val_00000061.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02965783-ILSVRC2012_val_00000213.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02965783-ILSVRC2012_val_00000213.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,216B, BPFP=0.0232 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 50,796B, BPFP=0.0964 +⌛️ [2/4] FRONTEND: Frontend time: 2.648s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.480s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09516161 13.68370669 + layer.39.0 223.32294704 15439.74538387 + ------------------------------------------------------------------------------------- + TOTAL 111.70905432 7726.71454528 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 63012 +BPFP 0.0598 bits/point +EBPFP 0.0598 equivalent bits/point +MSE 7726.714545 +---------------------- -------------------------------------------------------- +Time: 5.179s Load: 0.051s, Pack+Encode: 2.648s, Decode+Unpack: 2.480s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7726.7145 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02965783-ILSVRC2012_val_00000213.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n02965783-ILSVRC2012_val_00000213.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966193-ILSVRC2012_val_00000074.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966193-ILSVRC2012_val_00000074.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,944B, BPFP=0.0265 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 71,936B, BPFP=0.1365 +⌛️ [2/4] FRONTEND: Frontend time: 2.634s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.483s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12190965 13.96948740 + layer.39.0 378.75431244 16976.99708455 + ------------------------------------------------------------------------------------- + TOTAL 189.43811104 8495.48328598 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 85880 +BPFP 0.0815 bits/point +EBPFP 0.0815 equivalent bits/point +MSE 8495.483286 +---------------------- -------------------------------------------------------- +Time: 5.169s Load: 0.052s, Pack+Encode: 2.634s, Decode+Unpack: 2.483s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8495.4833 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966193-ILSVRC2012_val_00000074.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n02966193-ILSVRC2012_val_00000074.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966687-ILSVRC2012_val_00001041.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966687-ILSVRC2012_val_00001041.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,116B, BPFP=0.0249 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 43,688B, BPFP=0.0829 +⌛️ [2/4] FRONTEND: Frontend time: 2.649s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.481s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12487827 14.02436281 + layer.39.0 254.07423773 16688.75801749 + ------------------------------------------------------------------------------------- + TOTAL 127.09955800 8351.39119015 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 56804 +BPFP 0.0539 bits/point +EBPFP 0.0539 equivalent bits/point +MSE 8351.391190 +---------------------- -------------------------------------------------------- +Time: 5.182s Load: 0.052s, Pack+Encode: 2.649s, Decode+Unpack: 2.481s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8351.3912 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966687-ILSVRC2012_val_00001041.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n02966687-ILSVRC2012_val_00001041.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02971356-ILSVRC2012_val_00000019.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02971356-ILSVRC2012_val_00000019.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,808B, BPFP=0.0243 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 35,232B, BPFP=0.0669 +⌛️ [2/4] FRONTEND: Frontend time: 2.643s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.479s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09754465 13.77033224 + layer.39.0 24.51746044 15245.51895044 + ------------------------------------------------------------------------------------- + TOTAL 12.30750255 7629.64464134 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 48040 +BPFP 0.0456 bits/point +EBPFP 0.0456 equivalent bits/point +MSE 7629.644641 +---------------------- -------------------------------------------------------- +Time: 5.182s Load: 0.060s, Pack+Encode: 2.643s, Decode+Unpack: 2.479s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7629.6446 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02971356-ILSVRC2012_val_00000019.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n02971356-ILSVRC2012_val_00000019.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02978881-ILSVRC2012_val_00000353.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02978881-ILSVRC2012_val_00000353.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,252B, BPFP=0.0252 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 44,480B, BPFP=0.0844 +⌛️ [2/4] FRONTEND: Frontend time: 2.672s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.473s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09975241 13.15925276 + layer.39.0 226.62124939 16057.54518950 + ------------------------------------------------------------------------------------- + TOTAL 113.36050090 8035.35222113 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 57732 +BPFP 0.0548 bits/point +EBPFP 0.0548 equivalent bits/point +MSE 8035.352221 +---------------------- -------------------------------------------------------- +Time: 5.197s Load: 0.052s, Pack+Encode: 2.672s, Decode+Unpack: 2.473s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8035.3522 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02978881-ILSVRC2012_val_00000353.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n02978881-ILSVRC2012_val_00000353.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02980441-ILSVRC2012_val_00000122.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02980441-ILSVRC2012_val_00000122.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,104B, BPFP=0.0230 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 23,528B, BPFP=0.0447 +⌛️ [2/4] FRONTEND: Frontend time: 2.643s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.484s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10186533 13.94692682 + layer.39.0 8.25151846 13906.90962099 + ------------------------------------------------------------------------------------- + TOTAL 4.17669190 6960.42827390 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 35632 +BPFP 0.0338 bits/point +EBPFP 0.0338 equivalent bits/point +MSE 6960.428274 +---------------------- -------------------------------------------------------- +Time: 5.179s Load: 0.052s, Pack+Encode: 2.643s, Decode+Unpack: 2.484s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 6960.4283 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02980441-ILSVRC2012_val_00000122.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n02980441-ILSVRC2012_val_00000122.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02988304-ILSVRC2012_val_00003491.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02988304-ILSVRC2012_val_00003491.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 14,576B, BPFP=0.0277 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 54,100B, BPFP=0.1027 +⌛️ [2/4] FRONTEND: Frontend time: 2.664s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.472s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10176498 13.74440920 + layer.39.0 516.16180758 18306.36929057 + ------------------------------------------------------------------------------------- + TOTAL 258.13178628 9160.05684989 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 68676 +BPFP 0.0652 bits/point +EBPFP 0.0652 equivalent bits/point +MSE 9160.056850 +---------------------- -------------------------------------------------------- +Time: 5.187s Load: 0.051s, Pack+Encode: 2.664s, Decode+Unpack: 2.472s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9160.0568 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02988304-ILSVRC2012_val_00003491.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n02988304-ILSVRC2012_val_00003491.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992211-ILSVRC2012_val_00000108.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992211-ILSVRC2012_val_00000108.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 14,704B, BPFP=0.0279 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 71,480B, BPFP=0.1357 +⌛️ [2/4] FRONTEND: Frontend time: 2.650s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.464s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10107529 13.64377202 + layer.39.0 89.13089923 13680.00388727 + ------------------------------------------------------------------------------------- + TOTAL 44.61598726 6846.82382964 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 86184 +BPFP 0.0818 bits/point +EBPFP 0.0818 equivalent bits/point +MSE 6846.823830 +---------------------- -------------------------------------------------------- +Time: 5.165s Load: 0.052s, Pack+Encode: 2.650s, Decode+Unpack: 2.464s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 6846.8238 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992211-ILSVRC2012_val_00000108.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n02992211-ILSVRC2012_val_00000108.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992529-ILSVRC2012_val_00000089.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992529-ILSVRC2012_val_00000089.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,216B, BPFP=0.0232 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 57,924B, BPFP=0.1099 +⌛️ [2/4] FRONTEND: Frontend time: 2.622s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.464s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11197385 13.77639186 + layer.39.0 964.25631681 17541.99028183 + ------------------------------------------------------------------------------------- + TOTAL 482.18414533 8777.88333684 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 70140 +BPFP 0.0666 bits/point +EBPFP 0.0666 equivalent bits/point +MSE 8777.883337 +---------------------- -------------------------------------------------------- +Time: 5.138s Load: 0.052s, Pack+Encode: 2.622s, Decode+Unpack: 2.464s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8777.8833 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992529-ILSVRC2012_val_00000089.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n02992529-ILSVRC2012_val_00000089.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02999410-ILSVRC2012_val_00000376.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02999410-ILSVRC2012_val_00000376.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,644B, BPFP=0.0240 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 34,004B, BPFP=0.0645 +⌛️ [2/4] FRONTEND: Frontend time: 2.640s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.470s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10398186 13.49738065 + layer.39.0 145.78410471 14803.92031098 + ------------------------------------------------------------------------------------- + TOTAL 72.94404329 7408.70884582 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 46648 +BPFP 0.0443 bits/point +EBPFP 0.0443 equivalent bits/point +MSE 7408.708846 +---------------------- -------------------------------------------------------- +Time: 5.161s Load: 0.052s, Pack+Encode: 2.640s, Decode+Unpack: 2.470s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7408.7088 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02999410-ILSVRC2012_val_00000376.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n02999410-ILSVRC2012_val_00000376.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000134-ILSVRC2012_val_00001094.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000134-ILSVRC2012_val_00001094.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,552B, BPFP=0.0238 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 51,868B, BPFP=0.0984 +⌛️ [2/4] FRONTEND: Frontend time: 2.647s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.470s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09696872 13.98450312 + layer.39.0 22.81329530 14220.35471331 + ------------------------------------------------------------------------------------- + TOTAL 11.45513201 7117.16960822 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 64420 +BPFP 0.0611 bits/point +EBPFP 0.0611 equivalent bits/point +MSE 7117.169608 +---------------------- -------------------------------------------------------- +Time: 5.177s Load: 0.059s, Pack+Encode: 2.647s, Decode+Unpack: 2.470s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7117.1696 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000134-ILSVRC2012_val_00001094.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03000134-ILSVRC2012_val_00001094.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000247-ILSVRC2012_val_00002280.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000247-ILSVRC2012_val_00002280.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 31,288B, BPFP=0.0594 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 50,836B, BPFP=0.0965 +⌛️ [2/4] FRONTEND: Frontend time: 2.634s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.479s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.29135144 14.28574750 + layer.39.0 428.26293732 17019.35276968 + ------------------------------------------------------------------------------------- + TOTAL 214.27714438 8516.81925859 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 82124 +BPFP 0.0779 bits/point +EBPFP 0.0779 equivalent bits/point +MSE 8516.819259 +---------------------- -------------------------------------------------------- +Time: 5.185s Load: 0.072s, Pack+Encode: 2.634s, Decode+Unpack: 2.479s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8516.8193 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000247-ILSVRC2012_val_00002280.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03000247-ILSVRC2012_val_00002280.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000684-ILSVRC2012_val_00000537.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000684-ILSVRC2012_val_00000537.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 15,096B, BPFP=0.0287 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 57,016B, BPFP=0.1082 +⌛️ [2/4] FRONTEND: Frontend time: 2.631s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.473s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13150742 13.74497578 + layer.39.0 55.24585459 16379.03012634 + ------------------------------------------------------------------------------------- + TOTAL 27.68868101 8196.38755106 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 72112 +BPFP 0.0684 bits/point +EBPFP 0.0684 equivalent bits/point +MSE 8196.387551 +---------------------- -------------------------------------------------------- +Time: 5.164s Load: 0.060s, Pack+Encode: 2.631s, Decode+Unpack: 2.473s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8196.3876 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000684-ILSVRC2012_val_00000537.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03000684-ILSVRC2012_val_00000537.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03014705-ILSVRC2012_val_00001168.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03014705-ILSVRC2012_val_00001168.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,716B, BPFP=0.0260 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 50,456B, BPFP=0.0958 +⌛️ [2/4] FRONTEND: Frontend time: 2.640s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.469s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09787338 13.72192264 + layer.39.0 322.89622813 14620.07580175 + ------------------------------------------------------------------------------------- + TOTAL 161.49705076 7316.89886220 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 64172 +BPFP 0.0609 bits/point +EBPFP 0.0609 equivalent bits/point +MSE 7316.898862 +---------------------- -------------------------------------------------------- +Time: 5.170s Load: 0.061s, Pack+Encode: 2.640s, Decode+Unpack: 2.469s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7316.8989 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03014705-ILSVRC2012_val_00001168.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03014705-ILSVRC2012_val_00001168.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03017168-ILSVRC2012_val_00001601.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03017168-ILSVRC2012_val_00001601.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.073s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 14,560B, BPFP=0.0276 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 56,584B, BPFP=0.1074 +⌛️ [2/4] FRONTEND: Frontend time: 2.644s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.492s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10213913 13.55126052 + layer.39.0 475.40952988 14175.40816327 + ------------------------------------------------------------------------------------- + TOTAL 237.75583451 7094.47971189 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 71144 +BPFP 0.0675 bits/point +EBPFP 0.0675 equivalent bits/point +MSE 7094.479712 +---------------------- -------------------------------------------------------- +Time: 5.209s Load: 0.073s, Pack+Encode: 2.644s, Decode+Unpack: 2.492s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7094.4797 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03017168-ILSVRC2012_val_00001601.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03017168-ILSVRC2012_val_00001601.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03018349-ILSVRC2012_val_00000346.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03018349-ILSVRC2012_val_00000346.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,360B, BPFP=0.0235 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 54,648B, BPFP=0.1037 +⌛️ [2/4] FRONTEND: Frontend time: 2.649s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.474s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09959339 13.98104577 + layer.39.0 56.59841169 14254.23906706 + ------------------------------------------------------------------------------------- + TOTAL 28.34900254 7134.11005641 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 67008 +BPFP 0.0636 bits/point +EBPFP 0.0636 equivalent bits/point +MSE 7134.110056 +---------------------- -------------------------------------------------------- +Time: 5.174s Load: 0.051s, Pack+Encode: 2.649s, Decode+Unpack: 2.474s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7134.1101 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03018349-ILSVRC2012_val_00000346.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03018349-ILSVRC2012_val_00000346.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03026506-ILSVRC2012_val_00001908.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03026506-ILSVRC2012_val_00001908.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.057s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,420B, BPFP=0.0236 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 55,088B, BPFP=0.1046 +⌛️ [2/4] FRONTEND: Frontend time: 2.648s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.482s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10977067 13.54023646 + layer.39.0 668.54063411 17501.83479106 + ------------------------------------------------------------------------------------- + TOTAL 334.32520239 8757.68751376 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 67508 +BPFP 0.0641 bits/point +EBPFP 0.0641 equivalent bits/point +MSE 8757.687514 +---------------------- -------------------------------------------------------- +Time: 5.187s Load: 0.057s, Pack+Encode: 2.648s, Decode+Unpack: 2.482s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8757.6875 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03026506-ILSVRC2012_val_00001908.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03026506-ILSVRC2012_val_00001908.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03028079-ILSVRC2012_val_00003351.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03028079-ILSVRC2012_val_00003351.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.073s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,852B, BPFP=0.0244 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 24,388B, BPFP=0.0463 +⌛️ [2/4] FRONTEND: Frontend time: 2.656s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.496s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10934904 13.78088367 + layer.39.0 15.31112010 14150.27793975 + ------------------------------------------------------------------------------------- + TOTAL 7.71023457 7082.02941171 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 37240 +BPFP 0.0353 bits/point +EBPFP 0.0353 equivalent bits/point +MSE 7082.029412 +---------------------- -------------------------------------------------------- +Time: 5.225s Load: 0.073s, Pack+Encode: 2.656s, Decode+Unpack: 2.496s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7082.0294 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03028079-ILSVRC2012_val_00003351.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03028079-ILSVRC2012_val_00003351.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03032252-ILSVRC2012_val_00000086.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03032252-ILSVRC2012_val_00000086.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 15,732B, BPFP=0.0299 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 49,768B, BPFP=0.0945 +⌛️ [2/4] FRONTEND: Frontend time: 2.667s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.472s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13507480 14.01751359 + layer.39.0 103.55165816 15622.80077745 + ------------------------------------------------------------------------------------- + TOTAL 51.84336648 7818.40914552 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 65500 +BPFP 0.0622 bits/point +EBPFP 0.0622 equivalent bits/point +MSE 7818.409146 +---------------------- -------------------------------------------------------- +Time: 5.207s Load: 0.069s, Pack+Encode: 2.667s, Decode+Unpack: 2.472s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7818.4091 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03032252-ILSVRC2012_val_00000086.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03032252-ILSVRC2012_val_00000086.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03041632-ILSVRC2012_val_00000564.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03041632-ILSVRC2012_val_00000564.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 16,216B, BPFP=0.0308 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 49,836B, BPFP=0.0946 +⌛️ [2/4] FRONTEND: Frontend time: 2.632s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.479s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10123130 13.71697150 + layer.39.0 371.34277818 15032.37706511 + ------------------------------------------------------------------------------------- + TOTAL 185.72200474 7523.04701831 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 66052 +BPFP 0.0627 bits/point +EBPFP 0.0627 equivalent bits/point +MSE 7523.047018 +---------------------- -------------------------------------------------------- +Time: 5.161s Load: 0.050s, Pack+Encode: 2.632s, Decode+Unpack: 2.479s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7523.0470 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03041632-ILSVRC2012_val_00000564.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03041632-ILSVRC2012_val_00000564.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03042490-ILSVRC2012_val_00001426.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03042490-ILSVRC2012_val_00001426.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,724B, BPFP=0.0260 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 56,772B, BPFP=0.1078 +⌛️ [2/4] FRONTEND: Frontend time: 2.639s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.482s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10706725 13.39946341 + layer.39.0 141.71039845 17391.65597668 + ------------------------------------------------------------------------------------- + TOTAL 70.90873285 8702.52772004 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 70496 +BPFP 0.0669 bits/point +EBPFP 0.0669 equivalent bits/point +MSE 8702.527720 +---------------------- -------------------------------------------------------- +Time: 5.183s Load: 0.062s, Pack+Encode: 2.639s, Decode+Unpack: 2.482s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8702.5277 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03042490-ILSVRC2012_val_00001426.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03042490-ILSVRC2012_val_00001426.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03047690-ILSVRC2012_val_00001500.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03047690-ILSVRC2012_val_00001500.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,112B, BPFP=0.0230 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 53,144B, BPFP=0.1009 +⌛️ [2/4] FRONTEND: Frontend time: 2.651s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.474s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09570478 13.51974858 + layer.39.0 226.76483540 16973.37026239 + ------------------------------------------------------------------------------------- + TOTAL 113.43027009 8493.44500549 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 65256 +BPFP 0.0619 bits/point +EBPFP 0.0619 equivalent bits/point +MSE 8493.445005 +---------------------- -------------------------------------------------------- +Time: 5.185s Load: 0.060s, Pack+Encode: 2.651s, Decode+Unpack: 2.474s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8493.4450 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03047690-ILSVRC2012_val_00001500.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03047690-ILSVRC2012_val_00001500.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03062245-ILSVRC2012_val_00000344.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03062245-ILSVRC2012_val_00000344.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,424B, BPFP=0.0236 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 35,984B, BPFP=0.0683 +⌛️ [2/4] FRONTEND: Frontend time: 2.650s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.483s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09619164 13.91885231 + layer.39.0 46.71096787 16913.15646259 + ------------------------------------------------------------------------------------- + TOTAL 23.40357976 8463.53765745 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 48408 +BPFP 0.0459 bits/point +EBPFP 0.0459 equivalent bits/point +MSE 8463.537657 +---------------------- -------------------------------------------------------- +Time: 5.183s Load: 0.050s, Pack+Encode: 2.650s, Decode+Unpack: 2.483s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8463.5377 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03062245-ILSVRC2012_val_00000344.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03062245-ILSVRC2012_val_00000344.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063599-ILSVRC2012_val_00000164.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063599-ILSVRC2012_val_00000164.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,548B, BPFP=0.0238 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 47,504B, BPFP=0.0902 +⌛️ [2/4] FRONTEND: Frontend time: 2.645s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.471s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10111790 13.44187128 + layer.39.0 9.80528160 14050.07288630 + ------------------------------------------------------------------------------------- + TOTAL 4.95319975 7031.75737879 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 60052 +BPFP 0.0570 bits/point +EBPFP 0.0570 equivalent bits/point +MSE 7031.757379 +---------------------- -------------------------------------------------------- +Time: 5.167s Load: 0.051s, Pack+Encode: 2.645s, Decode+Unpack: 2.471s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7031.7574 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063599-ILSVRC2012_val_00000164.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03063599-ILSVRC2012_val_00000164.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063689-ILSVRC2012_val_00001940.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063689-ILSVRC2012_val_00001940.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,820B, BPFP=0.0243 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 64,940B, BPFP=0.1233 +⌛️ [2/4] FRONTEND: Frontend time: 2.652s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.496s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09645106 13.53591074 + layer.39.0 18.48014797 16547.90087464 + ------------------------------------------------------------------------------------- + TOTAL 9.28829952 8280.71839269 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 77760 +BPFP 0.0738 bits/point +EBPFP 0.0738 equivalent bits/point +MSE 8280.718393 +---------------------- -------------------------------------------------------- +Time: 5.200s Load: 0.052s, Pack+Encode: 2.652s, Decode+Unpack: 2.496s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8280.7184 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063689-ILSVRC2012_val_00001940.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03063689-ILSVRC2012_val_00001940.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03065424-ILSVRC2012_val_00000915.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03065424-ILSVRC2012_val_00000915.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 14,860B, BPFP=0.0282 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 68,716B, BPFP=0.1304 +⌛️ [2/4] FRONTEND: Frontend time: 2.641s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.467s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12384982 13.87626507 + layer.39.0 2154.15986395 19597.02818270 + ------------------------------------------------------------------------------------- + TOTAL 1077.14185688 9805.45222389 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 83576 +BPFP 0.0793 bits/point +EBPFP 0.0793 equivalent bits/point +MSE 9805.452224 +---------------------- -------------------------------------------------------- +Time: 5.169s Load: 0.062s, Pack+Encode: 2.641s, Decode+Unpack: 2.467s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9805.4522 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03065424-ILSVRC2012_val_00000915.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03065424-ILSVRC2012_val_00000915.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03075370-ILSVRC2012_val_00004971.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03075370-ILSVRC2012_val_00004971.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,544B, BPFP=0.0238 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 39,908B, BPFP=0.0757 +⌛️ [2/4] FRONTEND: Frontend time: 2.664s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.483s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10672879 13.78862689 + layer.39.0 301.29020894 16582.09135083 + ------------------------------------------------------------------------------------- + TOTAL 150.69846886 8297.93998886 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52452 +BPFP 0.0498 bits/point +EBPFP 0.0498 equivalent bits/point +MSE 8297.939989 +---------------------- -------------------------------------------------------- +Time: 5.217s Load: 0.071s, Pack+Encode: 2.664s, Decode+Unpack: 2.483s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8297.9400 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03075370-ILSVRC2012_val_00004971.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03075370-ILSVRC2012_val_00004971.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03089624-ILSVRC2012_val_00001190.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03089624-ILSVRC2012_val_00001190.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,728B, BPFP=0.0242 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 51,268B, BPFP=0.0973 +⌛️ [2/4] FRONTEND: Frontend time: 2.639s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.478s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10385029 13.75475754 + layer.39.0 606.38896987 16987.76287658 + ------------------------------------------------------------------------------------- + TOTAL 303.24641008 8500.75881706 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 63996 +BPFP 0.0607 bits/point +EBPFP 0.0607 equivalent bits/point +MSE 8500.758817 +---------------------- -------------------------------------------------------- +Time: 5.167s Load: 0.051s, Pack+Encode: 2.639s, Decode+Unpack: 2.478s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8500.7588 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03089624-ILSVRC2012_val_00001190.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03089624-ILSVRC2012_val_00001190.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03095699-ILSVRC2012_val_00000403.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03095699-ILSVRC2012_val_00000403.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 16,240B, BPFP=0.0308 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 63,840B, BPFP=0.1212 +⌛️ [2/4] FRONTEND: Frontend time: 2.658s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.474s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12139760 13.62798568 + layer.39.0 62.59250486 15724.20019436 + ------------------------------------------------------------------------------------- + TOTAL 31.35695123 7868.91409002 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 80080 +BPFP 0.0760 bits/point +EBPFP 0.0760 equivalent bits/point +MSE 7868.914090 +---------------------- -------------------------------------------------------- +Time: 5.182s Load: 0.051s, Pack+Encode: 2.658s, Decode+Unpack: 2.474s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7868.9141 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03095699-ILSVRC2012_val_00000403.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03095699-ILSVRC2012_val_00000403.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03100240-ILSVRC2012_val_00001201.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03100240-ILSVRC2012_val_00001201.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,912B, BPFP=0.0264 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 38,760B, BPFP=0.0736 +⌛️ [2/4] FRONTEND: Frontend time: 2.642s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.479s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10258218 13.21260401 + layer.39.0 42.98202138 13923.10204082 + ------------------------------------------------------------------------------------- + TOTAL 21.54230178 6968.15732242 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52672 +BPFP 0.0500 bits/point +EBPFP 0.0500 equivalent bits/point +MSE 6968.157322 +---------------------- -------------------------------------------------------- +Time: 5.172s Load: 0.051s, Pack+Encode: 2.642s, Decode+Unpack: 2.479s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 6968.1573 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03100240-ILSVRC2012_val_00001201.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03100240-ILSVRC2012_val_00001201.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03109150-ILSVRC2012_val_00000678.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03109150-ILSVRC2012_val_00000678.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,200B, BPFP=0.0251 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 55,608B, BPFP=0.1055 +⌛️ [2/4] FRONTEND: Frontend time: 2.651s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.477s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09720685 13.34905134 + layer.39.0 496.21158285 16933.21477162 + ------------------------------------------------------------------------------------- + TOTAL 248.15439485 8473.28191148 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 68808 +BPFP 0.0653 bits/point +EBPFP 0.0653 equivalent bits/point +MSE 8473.281911 +---------------------- -------------------------------------------------------- +Time: 5.180s Load: 0.052s, Pack+Encode: 2.651s, Decode+Unpack: 2.477s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8473.2819 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03109150-ILSVRC2012_val_00000678.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03109150-ILSVRC2012_val_00000678.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03110669-ILSVRC2012_val_00002171.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03110669-ILSVRC2012_val_00002171.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 16,140B, BPFP=0.0306 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 46,328B, BPFP=0.0879 +⌛️ [2/4] FRONTEND: Frontend time: 2.646s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.476s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.15128201 13.62347299 + layer.39.0 15.00769387 13612.01263362 + ------------------------------------------------------------------------------------- + TOTAL 7.57948794 6812.81805331 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 62468 +BPFP 0.0593 bits/point +EBPFP 0.0593 equivalent bits/point +MSE 6812.818053 +---------------------- -------------------------------------------------------- +Time: 5.173s Load: 0.051s, Pack+Encode: 2.646s, Decode+Unpack: 2.476s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 6812.8181 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03110669-ILSVRC2012_val_00002171.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03110669-ILSVRC2012_val_00002171.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124043-ILSVRC2012_val_00000766.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124043-ILSVRC2012_val_00000766.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 14,480B, BPFP=0.0275 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 57,876B, BPFP=0.1099 +⌛️ [2/4] FRONTEND: Frontend time: 2.643s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.470s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11473456 13.85157484 + layer.39.0 54.83309418 17527.56851312 + ------------------------------------------------------------------------------------- + TOTAL 27.47391437 8770.71004398 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 72356 +BPFP 0.0687 bits/point +EBPFP 0.0687 equivalent bits/point +MSE 8770.710044 +---------------------- -------------------------------------------------------- +Time: 5.173s Load: 0.060s, Pack+Encode: 2.643s, Decode+Unpack: 2.470s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8770.7100 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124043-ILSVRC2012_val_00000766.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03124043-ILSVRC2012_val_00000766.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124170-ILSVRC2012_val_00001875.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124170-ILSVRC2012_val_00001875.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,224B, BPFP=0.0251 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 33,520B, BPFP=0.0636 +⌛️ [2/4] FRONTEND: Frontend time: 2.636s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.480s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11393612 13.79667285 + layer.39.0 9.06747107 14898.97959184 + ------------------------------------------------------------------------------------- + TOTAL 4.59070360 7456.38813235 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 46744 +BPFP 0.0444 bits/point +EBPFP 0.0444 equivalent bits/point +MSE 7456.388132 +---------------------- -------------------------------------------------------- +Time: 5.168s Load: 0.051s, Pack+Encode: 2.636s, Decode+Unpack: 2.480s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7456.3881 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124170-ILSVRC2012_val_00001875.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03124170-ILSVRC2012_val_00001875.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03126707-ILSVRC2012_val_00000020.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03126707-ILSVRC2012_val_00000020.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 16,380B, BPFP=0.0311 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 35,840B, BPFP=0.0680 +⌛️ [2/4] FRONTEND: Frontend time: 2.633s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.491s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.15273996 13.82799213 + layer.39.0 1033.15269679 14054.81924198 + ------------------------------------------------------------------------------------- + TOTAL 516.65271838 7034.32361706 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52220 +BPFP 0.0496 bits/point +EBPFP 0.0496 equivalent bits/point +MSE 7034.323617 +---------------------- -------------------------------------------------------- +Time: 5.176s Load: 0.052s, Pack+Encode: 2.633s, Decode+Unpack: 2.491s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7034.3236 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03126707-ILSVRC2012_val_00000020.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03126707-ILSVRC2012_val_00000020.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03127747-ILSVRC2012_val_00001689.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03127747-ILSVRC2012_val_00001689.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,764B, BPFP=0.0242 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 43,384B, BPFP=0.0823 +⌛️ [2/4] FRONTEND: Frontend time: 2.622s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.457s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10152024 13.71738338 + layer.39.0 322.92343902 16923.26919339 + ------------------------------------------------------------------------------------- + TOTAL 161.51247963 8468.49328839 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 56148 +BPFP 0.0533 bits/point +EBPFP 0.0533 equivalent bits/point +MSE 8468.493288 +---------------------- -------------------------------------------------------- +Time: 5.139s Load: 0.060s, Pack+Encode: 2.622s, Decode+Unpack: 2.457s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8468.4933 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03127747-ILSVRC2012_val_00001689.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03127747-ILSVRC2012_val_00001689.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03131574-ILSVRC2012_val_00003036.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03131574-ILSVRC2012_val_00003036.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,808B, BPFP=0.0243 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 55,112B, BPFP=0.1046 +⌛️ [2/4] FRONTEND: Frontend time: 2.645s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.473s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09568423 13.92519474 + layer.39.0 163.24681122 17268.97376093 + ------------------------------------------------------------------------------------- + TOTAL 81.67124773 8641.44947784 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 67920 +BPFP 0.0645 bits/point +EBPFP 0.0645 equivalent bits/point +MSE 8641.449478 +---------------------- -------------------------------------------------------- +Time: 5.169s Load: 0.051s, Pack+Encode: 2.645s, Decode+Unpack: 2.473s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8641.4495 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03131574-ILSVRC2012_val_00003036.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03131574-ILSVRC2012_val_00003036.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03133878-ILSVRC2012_val_00000534.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03133878-ILSVRC2012_val_00000534.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 14,344B, BPFP=0.0272 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 61,968B, BPFP=0.1176 +⌛️ [2/4] FRONTEND: Frontend time: 2.646s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.474s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11186348 13.54646122 + layer.39.0 28.46096218 16316.59669582 + ------------------------------------------------------------------------------------- + TOTAL 14.28641283 8165.07157852 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 76312 +BPFP 0.0724 bits/point +EBPFP 0.0724 equivalent bits/point +MSE 8165.071579 +---------------------- -------------------------------------------------------- +Time: 5.171s Load: 0.051s, Pack+Encode: 2.646s, Decode+Unpack: 2.474s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8165.0716 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03133878-ILSVRC2012_val_00000534.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03133878-ILSVRC2012_val_00000534.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03134739-ILSVRC2012_val_00000249.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03134739-ILSVRC2012_val_00000249.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 11,872B, BPFP=0.0225 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 63,000B, BPFP=0.1196 +⌛️ [2/4] FRONTEND: Frontend time: 2.651s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.497s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09967384 14.02545516 + layer.39.0 372.24465500 14665.81146744 + ------------------------------------------------------------------------------------- + TOTAL 186.17216442 7339.91846130 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 74872 +BPFP 0.0711 bits/point +EBPFP 0.0711 equivalent bits/point +MSE 7339.918461 +---------------------- -------------------------------------------------------- +Time: 5.201s Load: 0.052s, Pack+Encode: 2.651s, Decode+Unpack: 2.497s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7339.9185 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03134739-ILSVRC2012_val_00000249.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03134739-ILSVRC2012_val_00000249.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03141823-ILSVRC2012_val_00001337.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03141823-ILSVRC2012_val_00001337.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,364B, BPFP=0.0235 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 55,640B, BPFP=0.1056 +⌛️ [2/4] FRONTEND: Frontend time: 2.654s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.495s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10422104 13.72936407 + layer.39.0 29.45558301 15417.20116618 + ------------------------------------------------------------------------------------- + TOTAL 14.77990203 7715.46526512 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 68004 +BPFP 0.0645 bits/point +EBPFP 0.0645 equivalent bits/point +MSE 7715.465265 +---------------------- -------------------------------------------------------- +Time: 5.200s Load: 0.051s, Pack+Encode: 2.654s, Decode+Unpack: 2.495s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7715.4653 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03141823-ILSVRC2012_val_00001337.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03141823-ILSVRC2012_val_00001337.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03160309-ILSVRC2012_val_00000330.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03160309-ILSVRC2012_val_00000330.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 14,512B, BPFP=0.0275 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 39,740B, BPFP=0.0754 +⌛️ [2/4] FRONTEND: Frontend time: 2.650s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.480s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09980877 13.43000068 + layer.39.0 30.04123011 14220.67638484 + ------------------------------------------------------------------------------------- + TOTAL 15.07051944 7117.05319276 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 54252 +BPFP 0.0515 bits/point +EBPFP 0.0515 equivalent bits/point +MSE 7117.053193 +---------------------- -------------------------------------------------------- +Time: 5.180s Load: 0.050s, Pack+Encode: 2.650s, Decode+Unpack: 2.480s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7117.0532 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03160309-ILSVRC2012_val_00000330.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03160309-ILSVRC2012_val_00000330.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03187595-ILSVRC2012_val_00000137.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03187595-ILSVRC2012_val_00000137.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,744B, BPFP=0.0242 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 40,220B, BPFP=0.0763 +⌛️ [2/4] FRONTEND: Frontend time: 2.638s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.481s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10716813 13.58440480 + layer.39.0 12.39187394 16024.17784257 + ------------------------------------------------------------------------------------- + TOTAL 6.24952103 8018.88112368 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52964 +BPFP 0.0503 bits/point +EBPFP 0.0503 equivalent bits/point +MSE 8018.881124 +---------------------- -------------------------------------------------------- +Time: 5.181s Load: 0.061s, Pack+Encode: 2.638s, Decode+Unpack: 2.481s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8018.8811 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03187595-ILSVRC2012_val_00000137.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03187595-ILSVRC2012_val_00000137.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03188531-ILSVRC2012_val_00000493.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03188531-ILSVRC2012_val_00000493.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 11,668B, BPFP=0.0221 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 48,756B, BPFP=0.0925 +⌛️ [2/4] FRONTEND: Frontend time: 2.648s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.474s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09509044 13.82168386 + layer.39.0 10.77256154 17772.16326531 + ------------------------------------------------------------------------------------- + TOTAL 5.43382599 8892.99247458 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 60424 +BPFP 0.0573 bits/point +EBPFP 0.0573 equivalent bits/point +MSE 8892.992475 +---------------------- -------------------------------------------------------- +Time: 5.173s Load: 0.050s, Pack+Encode: 2.648s, Decode+Unpack: 2.474s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8892.9925 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03188531-ILSVRC2012_val_00000493.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03188531-ILSVRC2012_val_00000493.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03196217-ILSVRC2012_val_00003643.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03196217-ILSVRC2012_val_00003643.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,064B, BPFP=0.0229 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 51,356B, BPFP=0.0975 +⌛️ [2/4] FRONTEND: Frontend time: 2.643s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.504s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09478207 13.92549464 + layer.39.0 65.57403274 15617.03887269 + ------------------------------------------------------------------------------------- + TOTAL 32.83440740 7815.48218367 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 63420 +BPFP 0.0602 bits/point +EBPFP 0.0602 equivalent bits/point +MSE 7815.482184 +---------------------- -------------------------------------------------------- +Time: 5.216s Load: 0.070s, Pack+Encode: 2.643s, Decode+Unpack: 2.504s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7815.4822 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03196217-ILSVRC2012_val_00003643.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03196217-ILSVRC2012_val_00003643.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03201208-ILSVRC2012_val_00000241.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03201208-ILSVRC2012_val_00000241.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,316B, BPFP=0.0253 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 38,172B, BPFP=0.0725 +⌛️ [2/4] FRONTEND: Frontend time: 2.657s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.479s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10331685 13.32832999 + layer.39.0 136.59314261 16711.70651118 + ------------------------------------------------------------------------------------- + TOTAL 68.34822973 8362.51742058 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51488 +BPFP 0.0489 bits/point +EBPFP 0.0489 equivalent bits/point +MSE 8362.517421 +---------------------- -------------------------------------------------------- +Time: 5.189s Load: 0.052s, Pack+Encode: 2.657s, Decode+Unpack: 2.479s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8362.5174 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03201208-ILSVRC2012_val_00000241.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03201208-ILSVRC2012_val_00000241.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03207743-ILSVRC2012_val_00000256.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03207743-ILSVRC2012_val_00000256.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,076B, BPFP=0.0362 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 53,564B, BPFP=0.1017 +⌛️ [2/4] FRONTEND: Frontend time: 2.655s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.464s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09674843 13.58499036 + layer.39.0 189.63590258 15745.07677357 + ------------------------------------------------------------------------------------- + TOTAL 94.86632550 7879.33088196 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 72640 +BPFP 0.0689 bits/point +EBPFP 0.0689 equivalent bits/point +MSE 7879.330882 +---------------------- -------------------------------------------------------- +Time: 5.180s Load: 0.061s, Pack+Encode: 2.655s, Decode+Unpack: 2.464s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7879.3309 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03207743-ILSVRC2012_val_00000256.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03207743-ILSVRC2012_val_00000256.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03216828-ILSVRC2012_val_00001729.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03216828-ILSVRC2012_val_00001729.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 14,712B, BPFP=0.0279 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 39,108B, BPFP=0.0742 +⌛️ [2/4] FRONTEND: Frontend time: 2.653s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.470s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10800209 13.77295823 + layer.39.0 31.30713223 15470.86783285 + ------------------------------------------------------------------------------------- + TOTAL 15.70756716 7742.32039554 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 53820 +BPFP 0.0511 bits/point +EBPFP 0.0511 equivalent bits/point +MSE 7742.320396 +---------------------- -------------------------------------------------------- +Time: 5.175s Load: 0.052s, Pack+Encode: 2.653s, Decode+Unpack: 2.470s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7742.3204 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03216828-ILSVRC2012_val_00001729.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03216828-ILSVRC2012_val_00001729.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03218198-ILSVRC2012_val_00002266.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03218198-ILSVRC2012_val_00002266.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,720B, BPFP=0.0260 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 48,608B, BPFP=0.0923 +⌛️ [2/4] FRONTEND: Frontend time: 2.662s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.492s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11617067 13.92083106 + layer.39.0 195.83184524 15492.20991254 + ------------------------------------------------------------------------------------- + TOTAL 97.97400795 7753.06537180 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 62328 +BPFP 0.0592 bits/point +EBPFP 0.0592 equivalent bits/point +MSE 7753.065372 +---------------------- -------------------------------------------------------- +Time: 5.205s Load: 0.051s, Pack+Encode: 2.662s, Decode+Unpack: 2.492s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7753.0654 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03218198-ILSVRC2012_val_00002266.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03218198-ILSVRC2012_val_00002266.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03220513-ILSVRC2012_val_00001868.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03220513-ILSVRC2012_val_00001868.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 26,120B, BPFP=0.0496 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 60,104B, BPFP=0.1141 +⌛️ [2/4] FRONTEND: Frontend time: 2.653s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.483s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.20032125 13.84273927 + layer.39.0 377.00176142 17260.43731778 + ------------------------------------------------------------------------------------- + TOTAL 188.60104134 8637.14002853 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 86224 +BPFP 0.0818 bits/point +EBPFP 0.0818 equivalent bits/point +MSE 8637.140029 +---------------------- -------------------------------------------------------- +Time: 5.188s Load: 0.052s, Pack+Encode: 2.653s, Decode+Unpack: 2.483s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8637.1400 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03220513-ILSVRC2012_val_00001868.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03220513-ILSVRC2012_val_00001868.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03223299-ILSVRC2012_val_00001893.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03223299-ILSVRC2012_val_00001893.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 15,076B, BPFP=0.0286 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 48,444B, BPFP=0.0920 +⌛️ [2/4] FRONTEND: Frontend time: 2.647s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.477s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10735053 13.70893787 + layer.39.0 354.51621720 17052.68610301 + ------------------------------------------------------------------------------------- + TOTAL 177.31178386 8533.19752044 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 63520 +BPFP 0.0603 bits/point +EBPFP 0.0603 equivalent bits/point +MSE 8533.197520 +---------------------- -------------------------------------------------------- +Time: 5.176s Load: 0.052s, Pack+Encode: 2.647s, Decode+Unpack: 2.477s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8533.1975 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03223299-ILSVRC2012_val_00001893.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03223299-ILSVRC2012_val_00001893.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03240683-ILSVRC2012_val_00000504.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03240683-ILSVRC2012_val_00000504.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.053s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,632B, BPFP=0.0259 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 41,776B, BPFP=0.0793 +⌛️ [2/4] FRONTEND: Frontend time: 2.645s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.480s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10065408 13.31174836 + layer.39.0 443.53838678 14879.02526725 + ------------------------------------------------------------------------------------- + TOTAL 221.81952043 7446.16850780 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 55408 +BPFP 0.0526 bits/point +EBPFP 0.0526 equivalent bits/point +MSE 7446.168508 +---------------------- -------------------------------------------------------- +Time: 5.178s Load: 0.053s, Pack+Encode: 2.645s, Decode+Unpack: 2.480s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7446.1685 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03240683-ILSVRC2012_val_00000504.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03240683-ILSVRC2012_val_00000504.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03250847-ILSVRC2012_val_00000542.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03250847-ILSVRC2012_val_00000542.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,492B, BPFP=0.0237 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 55,576B, BPFP=0.1055 +⌛️ [2/4] FRONTEND: Frontend time: 2.650s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.462s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10136319 13.57359143 + layer.39.0 140.24735787 16440.21379981 + ------------------------------------------------------------------------------------- + TOTAL 70.17436053 8226.89369562 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 68068 +BPFP 0.0646 bits/point +EBPFP 0.0646 equivalent bits/point +MSE 8226.893696 +---------------------- -------------------------------------------------------- +Time: 5.164s Load: 0.052s, Pack+Encode: 2.650s, Decode+Unpack: 2.462s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8226.8937 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03250847-ILSVRC2012_val_00000542.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03250847-ILSVRC2012_val_00000542.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03255030-ILSVRC2012_val_00001045.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03255030-ILSVRC2012_val_00001045.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,320B, BPFP=0.0253 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 42,360B, BPFP=0.0804 +⌛️ [2/4] FRONTEND: Frontend time: 2.634s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.478s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10050351 13.66619025 + layer.39.0 12.06722622 15794.19047619 + ------------------------------------------------------------------------------------- + TOTAL 6.08386487 7903.92833322 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 55680 +BPFP 0.0528 bits/point +EBPFP 0.0528 equivalent bits/point +MSE 7903.928333 +---------------------- -------------------------------------------------------- +Time: 5.173s Load: 0.061s, Pack+Encode: 2.634s, Decode+Unpack: 2.478s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7903.9283 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03255030-ILSVRC2012_val_00001045.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03255030-ILSVRC2012_val_00001045.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03271574-ILSVRC2012_val_00000942.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03271574-ILSVRC2012_val_00000942.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,708B, BPFP=0.0241 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 60,608B, BPFP=0.1150 +⌛️ [2/4] FRONTEND: Frontend time: 2.645s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.462s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10164264 13.70547862 + layer.39.0 660.63544704 18301.72789116 + ------------------------------------------------------------------------------------- + TOTAL 330.36854484 9157.71668489 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 73316 +BPFP 0.0696 bits/point +EBPFP 0.0696 equivalent bits/point +MSE 9157.716685 +---------------------- -------------------------------------------------------- +Time: 5.167s Load: 0.059s, Pack+Encode: 2.645s, Decode+Unpack: 2.462s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9157.7167 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03271574-ILSVRC2012_val_00000942.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03271574-ILSVRC2012_val_00000942.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272010-ILSVRC2012_val_00000374.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272010-ILSVRC2012_val_00000374.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 11,716B, BPFP=0.0222 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 35,256B, BPFP=0.0669 +⌛️ [2/4] FRONTEND: Frontend time: 2.628s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.478s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10420663 13.62158440 + layer.39.0 9.63653369 16727.89504373 + ------------------------------------------------------------------------------------- + TOTAL 4.87037016 8370.75831407 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 46972 +BPFP 0.0446 bits/point +EBPFP 0.0446 equivalent bits/point +MSE 8370.758314 +---------------------- -------------------------------------------------------- +Time: 5.158s Load: 0.052s, Pack+Encode: 2.628s, Decode+Unpack: 2.478s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8370.7583 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272010-ILSVRC2012_val_00000374.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03272010-ILSVRC2012_val_00000374.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272562-ILSVRC2012_val_00001699.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272562-ILSVRC2012_val_00001699.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 15,460B, BPFP=0.0293 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 45,072B, BPFP=0.0856 +⌛️ [2/4] FRONTEND: Frontend time: 2.648s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.471s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11399285 13.50076967 + layer.39.0 12.79457642 15757.10301263 + ------------------------------------------------------------------------------------- + TOTAL 6.45428464 7885.30189115 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 60532 +BPFP 0.0574 bits/point +EBPFP 0.0574 equivalent bits/point +MSE 7885.301891 +---------------------- -------------------------------------------------------- +Time: 5.172s Load: 0.052s, Pack+Encode: 2.648s, Decode+Unpack: 2.471s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7885.3019 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272562-ILSVRC2012_val_00001699.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03272562-ILSVRC2012_val_00001699.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03290653-ILSVRC2012_val_00000199.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03290653-ILSVRC2012_val_00000199.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,948B, BPFP=0.0246 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 46,392B, BPFP=0.0881 +⌛️ [2/4] FRONTEND: Frontend time: 2.641s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.476s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09581849 13.17159219 + layer.39.0 9.30266794 16718.72886297 + ------------------------------------------------------------------------------------- + TOTAL 4.69924322 8365.95022758 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 59340 +BPFP 0.0563 bits/point +EBPFP 0.0563 equivalent bits/point +MSE 8365.950228 +---------------------- -------------------------------------------------------- +Time: 5.176s Load: 0.060s, Pack+Encode: 2.641s, Decode+Unpack: 2.476s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8365.9502 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03290653-ILSVRC2012_val_00000199.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03290653-ILSVRC2012_val_00000199.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03291819-ILSVRC2012_val_00000419.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03291819-ILSVRC2012_val_00000419.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,700B, BPFP=0.0241 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 36,828B, BPFP=0.0699 +⌛️ [2/4] FRONTEND: Frontend time: 2.659s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.474s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10621172 13.89419339 + layer.39.0 31.36357166 15842.48590865 + ------------------------------------------------------------------------------------- + TOTAL 15.73489169 7928.19005102 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 49528 +BPFP 0.0470 bits/point +EBPFP 0.0470 equivalent bits/point +MSE 7928.190051 +---------------------- -------------------------------------------------------- +Time: 5.182s Load: 0.050s, Pack+Encode: 2.659s, Decode+Unpack: 2.474s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7928.1901 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03291819-ILSVRC2012_val_00000419.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03291819-ILSVRC2012_val_00000419.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03314780-ILSVRC2012_val_00000624.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03314780-ILSVRC2012_val_00000624.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 11,984B, BPFP=0.0227 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 55,708B, BPFP=0.1057 +⌛️ [2/4] FRONTEND: Frontend time: 2.666s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.482s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10172509 13.73654451 + layer.39.0 35.60390853 16180.59669582 + ------------------------------------------------------------------------------------- + TOTAL 17.85281681 8097.16662016 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 67692 +BPFP 0.0642 bits/point +EBPFP 0.0642 equivalent bits/point +MSE 8097.166620 +---------------------- -------------------------------------------------------- +Time: 5.218s Load: 0.070s, Pack+Encode: 2.666s, Decode+Unpack: 2.482s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8097.1666 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03314780-ILSVRC2012_val_00000624.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03314780-ILSVRC2012_val_00000624.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03325584-ILSVRC2012_val_00001256.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03325584-ILSVRC2012_val_00001256.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,612B, BPFP=0.0239 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 44,496B, BPFP=0.0845 +⌛️ [2/4] FRONTEND: Frontend time: 2.685s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.505s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11348933 13.69475446 + layer.39.0 26.85401292 16848.83770651 + ------------------------------------------------------------------------------------- + TOTAL 13.48375113 8431.26623049 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 57108 +BPFP 0.0542 bits/point +EBPFP 0.0542 equivalent bits/point +MSE 8431.266230 +---------------------- -------------------------------------------------------- +Time: 5.240s Load: 0.051s, Pack+Encode: 2.685s, Decode+Unpack: 2.505s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8431.2662 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03325584-ILSVRC2012_val_00001256.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03325584-ILSVRC2012_val_00001256.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03337140-ILSVRC2012_val_00000132.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03337140-ILSVRC2012_val_00000132.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,712B, BPFP=0.0241 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 45,684B, BPFP=0.0867 +⌛️ [2/4] FRONTEND: Frontend time: 2.656s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.475s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09852950 13.56350219 + layer.39.0 10.39905343 17778.03498542 + ------------------------------------------------------------------------------------- + TOTAL 5.24879146 8895.79924380 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 58396 +BPFP 0.0554 bits/point +EBPFP 0.0554 equivalent bits/point +MSE 8895.799244 +---------------------- -------------------------------------------------------- +Time: 5.183s Load: 0.052s, Pack+Encode: 2.656s, Decode+Unpack: 2.475s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8895.7992 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03337140-ILSVRC2012_val_00000132.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03337140-ILSVRC2012_val_00000132.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03344393-ILSVRC2012_val_00000288.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03344393-ILSVRC2012_val_00000288.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,284B, BPFP=0.0252 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 52,440B, BPFP=0.0995 +⌛️ [2/4] FRONTEND: Frontend time: 2.676s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.484s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09830858 13.58353073 + layer.39.0 109.00505649 14508.92808552 + ------------------------------------------------------------------------------------- + TOTAL 54.55168253 7261.25580813 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 65724 +BPFP 0.0624 bits/point +EBPFP 0.0624 equivalent bits/point +MSE 7261.255808 +---------------------- -------------------------------------------------------- +Time: 5.220s Load: 0.060s, Pack+Encode: 2.676s, Decode+Unpack: 2.484s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7261.2558 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03344393-ILSVRC2012_val_00000288.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03344393-ILSVRC2012_val_00000288.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03345487-ILSVRC2012_val_00000764.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03345487-ILSVRC2012_val_00000764.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,252B, BPFP=0.0233 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 50,272B, BPFP=0.0954 +⌛️ [2/4] FRONTEND: Frontend time: 2.655s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.480s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10639974 13.66273669 + layer.39.0 14.55993569 14479.37900875 + ------------------------------------------------------------------------------------- + TOTAL 7.33316771 7246.52087272 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 62524 +BPFP 0.0593 bits/point +EBPFP 0.0593 equivalent bits/point +MSE 7246.520873 +---------------------- -------------------------------------------------------- +Time: 5.205s Load: 0.070s, Pack+Encode: 2.655s, Decode+Unpack: 2.480s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7246.5209 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03345487-ILSVRC2012_val_00000764.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03345487-ILSVRC2012_val_00000764.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03347037-ILSVRC2012_val_00000743.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03347037-ILSVRC2012_val_00000743.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 18,528B, BPFP=0.0352 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 52,188B, BPFP=0.0991 +⌛️ [2/4] FRONTEND: Frontend time: 2.639s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.472s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14351733 13.56591465 + layer.39.0 355.98426871 16666.12050534 + ------------------------------------------------------------------------------------- + TOTAL 178.06389302 8339.84321000 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 70716 +BPFP 0.0671 bits/point +EBPFP 0.0671 equivalent bits/point +MSE 8339.843210 +---------------------- -------------------------------------------------------- +Time: 5.161s Load: 0.051s, Pack+Encode: 2.639s, Decode+Unpack: 2.472s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8339.8432 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03347037-ILSVRC2012_val_00000743.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03347037-ILSVRC2012_val_00000743.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03355925-ILSVRC2012_val_00000445.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03355925-ILSVRC2012_val_00000445.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.058s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,704B, BPFP=0.0241 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 20,016B, BPFP=0.0380 +⌛️ [2/4] FRONTEND: Frontend time: 2.653s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.472s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09979894 13.73166549 + layer.39.0 9.06502540 14111.90281827 + ------------------------------------------------------------------------------------- + TOTAL 4.58241217 7062.81724188 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 32720 +BPFP 0.0311 bits/point +EBPFP 0.0311 equivalent bits/point +MSE 7062.817242 +---------------------- -------------------------------------------------------- +Time: 5.183s Load: 0.058s, Pack+Encode: 2.653s, Decode+Unpack: 2.472s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7062.8172 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03355925-ILSVRC2012_val_00000445.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03355925-ILSVRC2012_val_00000445.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03376595-ILSVRC2012_val_00001616.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03376595-ILSVRC2012_val_00001616.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 14,132B, BPFP=0.0268 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 60,892B, BPFP=0.1156 +⌛️ [2/4] FRONTEND: Frontend time: 2.661s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.470s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09988844 13.53352580 + layer.39.0 1408.20760447 18418.19241983 + ------------------------------------------------------------------------------------- + TOTAL 704.15374646 9215.86297281 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 75024 +BPFP 0.0712 bits/point +EBPFP 0.0712 equivalent bits/point +MSE 9215.862973 +---------------------- -------------------------------------------------------- +Time: 5.193s Load: 0.061s, Pack+Encode: 2.661s, Decode+Unpack: 2.470s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9215.8630 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03376595-ILSVRC2012_val_00001616.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03376595-ILSVRC2012_val_00001616.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03379051-ILSVRC2012_val_00002562.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03379051-ILSVRC2012_val_00002562.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,116B, BPFP=0.0230 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 66,364B, BPFP=0.1260 +⌛️ [2/4] FRONTEND: Frontend time: 2.658s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.484s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10889592 13.51418341 + layer.39.0 102.95462828 14683.17978620 + ------------------------------------------------------------------------------------- + TOTAL 51.53176210 7348.34698480 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 78480 +BPFP 0.0745 bits/point +EBPFP 0.0745 equivalent bits/point +MSE 7348.346985 +---------------------- -------------------------------------------------------- +Time: 5.212s Load: 0.070s, Pack+Encode: 2.658s, Decode+Unpack: 2.484s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7348.3470 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03379051-ILSVRC2012_val_00002562.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03379051-ILSVRC2012_val_00002562.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388043-ILSVRC2012_val_00001018.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388043-ILSVRC2012_val_00001018.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 14,516B, BPFP=0.0276 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 47,652B, BPFP=0.0904 +⌛️ [2/4] FRONTEND: Frontend time: 2.651s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.484s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09747427 13.90078732 + layer.39.0 21.12933142 15727.67346939 + ------------------------------------------------------------------------------------- + TOTAL 10.61340285 7870.78712836 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 62168 +BPFP 0.0590 bits/point +EBPFP 0.0590 equivalent bits/point +MSE 7870.787128 +---------------------- -------------------------------------------------------- +Time: 5.185s Load: 0.051s, Pack+Encode: 2.651s, Decode+Unpack: 2.484s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7870.7871 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388043-ILSVRC2012_val_00001018.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03388043-ILSVRC2012_val_00001018.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388183-ILSVRC2012_val_00002799.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388183-ILSVRC2012_val_00002799.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,456B, BPFP=0.0255 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 48,756B, BPFP=0.0925 +⌛️ [2/4] FRONTEND: Frontend time: 2.666s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.481s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10066175 13.33538421 + layer.39.0 786.68810739 14163.27308066 + ------------------------------------------------------------------------------------- + TOTAL 393.39438457 7088.30423244 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 62212 +BPFP 0.0590 bits/point +EBPFP 0.0590 equivalent bits/point +MSE 7088.304232 +---------------------- -------------------------------------------------------- +Time: 5.199s Load: 0.052s, Pack+Encode: 2.666s, Decode+Unpack: 2.481s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7088.3042 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388183-ILSVRC2012_val_00002799.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03388183-ILSVRC2012_val_00002799.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388549-ILSVRC2012_val_00002945.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388549-ILSVRC2012_val_00002945.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,936B, BPFP=0.0246 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 57,160B, BPFP=0.1085 +⌛️ [2/4] FRONTEND: Frontend time: 2.657s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.493s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09849939 13.62704708 + layer.39.0 10.79426799 13987.35471331 + ------------------------------------------------------------------------------------- + TOTAL 5.44638369 7000.49088020 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 70096 +BPFP 0.0665 bits/point +EBPFP 0.0665 equivalent bits/point +MSE 7000.490880 +---------------------- -------------------------------------------------------- +Time: 5.221s Load: 0.070s, Pack+Encode: 2.657s, Decode+Unpack: 2.493s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7000.4909 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388549-ILSVRC2012_val_00002945.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03388549-ILSVRC2012_val_00002945.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03393912-ILSVRC2012_val_00000047.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03393912-ILSVRC2012_val_00000047.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,520B, BPFP=0.0257 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 66,380B, BPFP=0.1260 +⌛️ [2/4] FRONTEND: Frontend time: 2.656s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.471s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09729456 13.58733069 + layer.39.0 38.26720800 13907.87949466 + ------------------------------------------------------------------------------------- + TOTAL 19.18225128 6960.73341267 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 79900 +BPFP 0.0758 bits/point +EBPFP 0.0758 equivalent bits/point +MSE 6960.733413 +---------------------- -------------------------------------------------------- +Time: 5.188s Load: 0.061s, Pack+Encode: 2.656s, Decode+Unpack: 2.471s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 6960.7334 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03393912-ILSVRC2012_val_00000047.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03393912-ILSVRC2012_val_00000047.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03394916-ILSVRC2012_val_00000957.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03394916-ILSVRC2012_val_00000957.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 13,084B, BPFP=0.0248 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 38,496B, BPFP=0.0731 +⌛️ [2/4] FRONTEND: Frontend time: 2.677s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.478s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10421823 14.06040072 + layer.39.0 9.72561820 16695.14091351 + ------------------------------------------------------------------------------------- + TOTAL 4.91491822 8354.60065712 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51580 +BPFP 0.0490 bits/point +EBPFP 0.0490 equivalent bits/point +MSE 8354.600657 +---------------------- -------------------------------------------------------- +Time: 5.226s Load: 0.070s, Pack+Encode: 2.677s, Decode+Unpack: 2.478s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8354.6007 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03394916-ILSVRC2012_val_00000957.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03394916-ILSVRC2012_val_00000957.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03404251-ILSVRC2012_val_00000641.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03404251-ILSVRC2012_val_00000641.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.057s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,868B, BPFP=0.0244 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 49,628B, BPFP=0.0942 +⌛️ [2/4] FRONTEND: Frontend time: 2.660s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.505s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10764784 13.68434824 + layer.39.0 585.45553936 16420.34791059 + ------------------------------------------------------------------------------------- + TOTAL 292.78159360 8217.01612941 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 62496 +BPFP 0.0593 bits/point +EBPFP 0.0593 equivalent bits/point +MSE 8217.016129 +---------------------- -------------------------------------------------------- +Time: 5.222s Load: 0.057s, Pack+Encode: 2.660s, Decode+Unpack: 2.505s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8217.0161 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03404251-ILSVRC2012_val_00000641.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03404251-ILSVRC2012_val_00000641.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03417042-ILSVRC2012_val_00001144.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03417042-ILSVRC2012_val_00001144.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 12,816B, BPFP=0.0243 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 58,980B, BPFP=0.1119 +⌛️ [2/4] FRONTEND: Frontend time: 2.660s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.465s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10091509 13.50300751 + layer.39.0 202.93364310 15874.87852284 + ------------------------------------------------------------------------------------- + TOTAL 101.51727910 7944.19076517 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 71796 +BPFP 0.0681 bits/point +EBPFP 0.0681 equivalent bits/point +MSE 7944.190765 +---------------------- -------------------------------------------------------- +Time: 5.186s Load: 0.061s, Pack+Encode: 2.660s, Decode+Unpack: 2.465s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7944.1908 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03417042-ILSVRC2012_val_00001144.zst + to output-fixed/dinov3-total/lambda0.001/elic-featurecoding-8bit-layerwise/cls_in1kval/n03417042-ILSVRC2012_val_00001144.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 0.0596 bits/point +Avg EBPFP 0.0596 equivalent bits/point +Avg MSE 7891.005826 +Avg Time 5.188s +------------------------ ---------------------------- diff --git a/lambda0.001/elic-featurecoding-8bit-individual/cls_in1kval/n03109150-ILSVRC2012_val_00000678.zst b/lambda0.001/elic-featurecoding-8bit-individual/cls_in1kval/n03109150-ILSVRC2012_val_00000678.zst new file mode 100644 index 0000000000000000000000000000000000000000..ab60c036492fcd59dfc0bea216c3a1f4d0875f5e --- /dev/null +++ b/lambda0.001/elic-featurecoding-8bit-individual/cls_in1kval/n03109150-ILSVRC2012_val_00000678.zst @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/dtufc_hyperprior-featurecoding_dinov3-total_individual.log +DTUFCCodecConfig: + arch: hyperprior-featurecoding + handler: dinov3-total + checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.001_epochs600_lr0.0001_bs360_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.001_epochs600_lr0.0001_bs360_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 405 +Loaded hyperprior-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.9' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.19' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.29' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.39' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json +Loaded per-key mappings: model=dinov3-total + Keys: ['layer.9', 'layer.19', 'layer.29', 'layer.39'] +---------------- ------------------------------------------------------------------------------------------------------------------------------ +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +Checkpoint codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.001_epochs600_lr0.0001_bs360_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-DINOv3/imagenet1k-a +Output output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka +---------------- ------------------------------------------------------------------------------------------------------------------------------ +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01498041-0.006639_koala _ American bullfrog_0.6658246.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01498041-0.006639_koala _ American bullfrog_0.6658246.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.091s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,052B, BPFP=0.0362 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,648B, BPFP=0.0601 +⌛️ [2/4] FRONTEND: Frontend time: 0.810s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.044s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09594801 14.94952434 + layer.39.0 58.94484178 20831.03984451 + ------------------------------------------------------------------------------------- + TOTAL 29.52039490 10422.99468443 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50700 +BPFP 0.0481 bits/point +EBPFP 0.0481 equivalent bits/point +MSE 10422.994684 +---------------------- -------------------------------------------------------- +Time: 1.945s Load: 0.091s, Pack+Encode: 0.810s, Decode+Unpack: 1.044s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10422.9947 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01498041-0.006639_koala _ American bullfrog_0.6658246.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01498041-0.006639_koala _ American bullfrog_0.6658246.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01531178-0.000765_fox squirrel _ ant_0.9486805.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01531178-0.000765_fox squirrel _ ant_0.9486805.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 18,560B, BPFP=0.0352 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 28,840B, BPFP=0.0547 +⌛️ [2/4] FRONTEND: Frontend time: 0.565s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.986s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09773727 14.75699443 + layer.39.0 17.17825445 19282.20019436 + ------------------------------------------------------------------------------------- + TOTAL 8.63799586 9648.47859440 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 47400 +BPFP 0.0450 bits/point +EBPFP 0.0450 equivalent bits/point +MSE 9648.478594 +---------------------- -------------------------------------------------------- +Time: 1.632s Load: 0.080s, Pack+Encode: 0.565s, Decode+Unpack: 0.986s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9648.4786 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01531178-0.000765_fox squirrel _ ant_0.9486805.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01531178-0.000765_fox squirrel _ ant_0.9486805.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01534433-0.004573_stingray _ stingray_0.97124094.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01534433-0.004573_stingray _ stingray_0.97124094.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 18,744B, BPFP=0.0356 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,276B, BPFP=0.0575 +⌛️ [2/4] FRONTEND: Frontend time: 0.560s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.985s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09515371 14.58110688 + layer.39.0 6.87362484 16717.89310010 + ------------------------------------------------------------------------------------- + TOTAL 3.48438928 8366.23710349 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 49020 +BPFP 0.0465 bits/point +EBPFP 0.0465 equivalent bits/point +MSE 8366.237103 +---------------------- -------------------------------------------------------- +Time: 1.598s Load: 0.052s, Pack+Encode: 0.560s, Decode+Unpack: 0.985s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8366.2371 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01534433-0.004573_stingray _ stingray_0.97124094.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01534433-0.004573_stingray _ stingray_0.97124094.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01558993-0.000522_bow _ bow_0.9033333.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01558993-0.000522_bow _ bow_0.9033333.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,556B, BPFP=0.0409 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,308B, BPFP=0.0575 +⌛️ [2/4] FRONTEND: Frontend time: 0.577s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.970s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09874929 15.25347539 + layer.39.0 7.31778236 18748.92517007 + ------------------------------------------------------------------------------------- + TOTAL 3.70826583 9382.08932273 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51864 +BPFP 0.0492 bits/point +EBPFP 0.0492 equivalent bits/point +MSE 9382.089323 +---------------------- -------------------------------------------------------- +Time: 1.598s Load: 0.051s, Pack+Encode: 0.577s, Decode+Unpack: 0.970s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9382.0893 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01558993-0.000522_bow _ bow_0.9033333.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01558993-0.000522_bow _ bow_0.9033333.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01580077-0.004583_African bush elephant _ African bush elephant_0.59939015.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01580077-0.004583_African bush elephant _ African bush elephant_0.59939015.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,796B, BPFP=0.0395 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,024B, BPFP=0.0589 +⌛️ [2/4] FRONTEND: Frontend time: 0.603s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.976s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10720986 14.86216233 + layer.39.0 24.46209533 20010.69193392 + ------------------------------------------------------------------------------------- + TOTAL 12.28465260 10012.77704812 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51820 +BPFP 0.0492 bits/point +EBPFP 0.0492 equivalent bits/point +MSE 10012.777048 +---------------------- -------------------------------------------------------- +Time: 1.630s Load: 0.051s, Pack+Encode: 0.603s, Decode+Unpack: 0.976s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10012.7770 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01580077-0.004583_African bush elephant _ African bush elephant_0.59939015.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01580077-0.004583_African bush elephant _ African bush elephant_0.59939015.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01614925-0.049724_white-headed capuchin _ white-headed capuchin_0.9309422.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01614925-0.049724_white-headed capuchin _ white-headed capuchin_0.9309422.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.087s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,256B, BPFP=0.0403 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 29,512B, BPFP=0.0560 +⌛️ [2/4] FRONTEND: Frontend time: 0.618s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.039s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09739119 14.92477811 + layer.39.0 8.81423010 19398.30126336 + ------------------------------------------------------------------------------------- + TOTAL 4.45581065 9706.61302074 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50768 +BPFP 0.0482 bits/point +EBPFP 0.0482 equivalent bits/point +MSE 9706.613021 +---------------------- -------------------------------------------------------- +Time: 1.744s Load: 0.087s, Pack+Encode: 0.618s, Decode+Unpack: 1.039s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9706.6130 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01614925-0.049724_white-headed capuchin _ white-headed capuchin_0.9309422.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01614925-0.049724_white-headed capuchin _ white-headed capuchin_0.9309422.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01631663-0.004606_American bullfrog _ American bullfrog_0.8789855.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01631663-0.004606_American bullfrog _ American bullfrog_0.8789855.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 18,392B, BPFP=0.0349 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 29,404B, BPFP=0.0558 +⌛️ [2/4] FRONTEND: Frontend time: 0.527s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.982s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09716670 14.81759635 + layer.39.0 20.45897868 18741.01652089 + ------------------------------------------------------------------------------------- + TOTAL 10.27807269 9377.91705862 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 47796 +BPFP 0.0454 bits/point +EBPFP 0.0454 equivalent bits/point +MSE 9377.917059 +---------------------- -------------------------------------------------------- +Time: 1.561s Load: 0.052s, Pack+Encode: 0.527s, Decode+Unpack: 0.982s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9377.9171 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01631663-0.004606_American bullfrog _ American bullfrog_0.8789855.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01631663-0.004606_American bullfrog _ American bullfrog_0.8789855.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01669191-0.029754_sandal _ sandal_0.38198605.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01669191-0.029754_sandal _ sandal_0.38198605.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,968B, BPFP=0.0398 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,612B, BPFP=0.0581 +⌛️ [2/4] FRONTEND: Frontend time: 0.583s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.015s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10877632 15.32484512 + layer.39.0 13.16500205 19194.48979592 + ------------------------------------------------------------------------------------- + TOTAL 6.63688918 9604.90732052 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51580 +BPFP 0.0490 bits/point +EBPFP 0.0490 equivalent bits/point +MSE 9604.907321 +---------------------- -------------------------------------------------------- +Time: 1.649s Load: 0.052s, Pack+Encode: 0.583s, Decode+Unpack: 1.015s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9604.9073 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01669191-0.029754_sandal _ sandal_0.38198605.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01669191-0.029754_sandal _ sandal_0.38198605.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770081-0.000571_syringe _ syringe_0.7369336.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770081-0.000571_syringe _ syringe_0.7369336.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.091s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 18,596B, BPFP=0.0353 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,056B, BPFP=0.0608 +⌛️ [2/4] FRONTEND: Frontend time: 0.593s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.023s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09508557 14.44564941 + layer.39.0 60.03878538 21757.67930029 + ------------------------------------------------------------------------------------- + TOTAL 30.06693547 10886.06247485 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50652 +BPFP 0.0481 bits/point +EBPFP 0.0481 equivalent bits/point +MSE 10886.062475 +---------------------- -------------------------------------------------------- +Time: 1.707s Load: 0.091s, Pack+Encode: 0.593s, Decode+Unpack: 1.023s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10886.0625 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770081-0.000571_syringe _ syringe_0.7369336.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01770081-0.000571_syringe _ syringe_0.7369336.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770393-0.000908_harvestman _ harvestman_0.8782497.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770393-0.000908_harvestman _ harvestman_0.8782497.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 18,600B, BPFP=0.0353 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 28,260B, BPFP=0.0536 +⌛️ [2/4] FRONTEND: Frontend time: 0.583s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.062s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11350316 14.72918565 + layer.39.0 19.73148992 19551.89115646 + ------------------------------------------------------------------------------------- + TOTAL 9.92249654 9783.31017106 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 46860 +BPFP 0.0445 bits/point +EBPFP 0.0445 equivalent bits/point +MSE 9783.310171 +---------------------- -------------------------------------------------------- +Time: 1.697s Load: 0.052s, Pack+Encode: 0.583s, Decode+Unpack: 1.062s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9783.3102 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770393-0.000908_harvestman _ harvestman_0.8782497.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01770393-0.000908_harvestman _ harvestman_0.8782497.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01784675-0.027853_syringe _ syringe_0.9584382.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01784675-0.027853_syringe _ syringe_0.9584382.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,800B, BPFP=0.0395 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 37,336B, BPFP=0.0709 +⌛️ [2/4] FRONTEND: Frontend time: 0.580s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.041s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11002613 15.24340986 + layer.39.0 26.08665877 26814.44314869 + ------------------------------------------------------------------------------------- + TOTAL 13.09834245 13414.84327928 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 58136 +BPFP 0.0552 bits/point +EBPFP 0.0552 equivalent bits/point +MSE 13414.843279 +---------------------- -------------------------------------------------------- +Time: 1.673s Load: 0.052s, Pack+Encode: 0.580s, Decode+Unpack: 1.041s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 13414.8433 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01784675-0.027853_syringe _ syringe_0.9584382.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01784675-0.027853_syringe _ syringe_0.9584382.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01819313-0.053742_koala _ koala_0.98647016.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01819313-0.053742_koala _ koala_0.98647016.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,036B, BPFP=0.0380 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,464B, BPFP=0.0597 +⌛️ [2/4] FRONTEND: Frontend time: 0.569s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.028s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14565475 15.38616261 + layer.39.0 25.01023445 22859.98833819 + ------------------------------------------------------------------------------------- + TOTAL 12.57794460 11437.68725040 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51500 +BPFP 0.0489 bits/point +EBPFP 0.0489 equivalent bits/point +MSE 11437.687250 +---------------------- -------------------------------------------------------- +Time: 1.649s Load: 0.052s, Pack+Encode: 0.569s, Decode+Unpack: 1.028s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 11437.6873 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01819313-0.053742_koala _ koala_0.98647016.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01819313-0.053742_koala _ koala_0.98647016.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01820546-0.012522_toucan _ toucan_0.63882655.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01820546-0.012522_toucan _ toucan_0.63882655.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 18,384B, BPFP=0.0349 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,120B, BPFP=0.0572 +⌛️ [2/4] FRONTEND: Frontend time: 0.583s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.031s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09696376 14.72457521 + layer.39.0 16.65489097 19851.68513120 + ------------------------------------------------------------------------------------- + TOTAL 8.37592737 9933.20485320 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 48504 +BPFP 0.0460 bits/point +EBPFP 0.0460 equivalent bits/point +MSE 9933.204853 +---------------------- -------------------------------------------------------- +Time: 1.666s Load: 0.052s, Pack+Encode: 0.583s, Decode+Unpack: 1.031s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9933.2049 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01820546-0.012522_toucan _ toucan_0.63882655.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01820546-0.012522_toucan _ toucan_0.63882655.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01833805-0.013248_toucan _ lorikeet_0.77773976.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01833805-0.013248_toucan _ lorikeet_0.77773976.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 17,908B, BPFP=0.0340 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 27,092B, BPFP=0.0514 +⌛️ [2/4] FRONTEND: Frontend time: 0.580s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.034s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09866240 14.50554619 + layer.39.0 7.67772963 17825.31389699 + ------------------------------------------------------------------------------------- + TOTAL 3.88819601 8919.90972159 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 45000 +BPFP 0.0427 bits/point +EBPFP 0.0427 equivalent bits/point +MSE 8919.909722 +---------------------- -------------------------------------------------------- +Time: 1.666s Load: 0.052s, Pack+Encode: 0.580s, Decode+Unpack: 1.034s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8919.9097 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01833805-0.013248_toucan _ lorikeet_0.77773976.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01833805-0.013248_toucan _ lorikeet_0.77773976.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01843383-0.013605_white-headed capuchin _ white-headed capuchin_0.9984768.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01843383-0.013605_white-headed capuchin _ white-headed capuchin_0.9984768.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,116B, BPFP=0.0382 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,832B, BPFP=0.0585 +⌛️ [2/4] FRONTEND: Frontend time: 0.511s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.974s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11910487 15.06582069 + layer.39.0 9.20068692 21256.66277940 + ------------------------------------------------------------------------------------- + TOTAL 4.65989589 10635.86430004 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50948 +BPFP 0.0484 bits/point +EBPFP 0.0484 equivalent bits/point +MSE 10635.864300 +---------------------- -------------------------------------------------------- +Time: 1.537s Load: 0.052s, Pack+Encode: 0.511s, Decode+Unpack: 0.974s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10635.8643 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01843383-0.013605_white-headed capuchin _ white-headed capuchin_0.9984768.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01843383-0.013605_white-headed capuchin _ white-headed capuchin_0.9984768.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01924916-0.000644_jay _ jay_0.82223135.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01924916-0.000644_jay _ jay_0.82223135.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,256B, BPFP=0.0384 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,216B, BPFP=0.0574 +⌛️ [2/4] FRONTEND: Frontend time: 0.608s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.028s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11488669 15.00348203 + layer.39.0 141.08750911 19307.63848397 + ------------------------------------------------------------------------------------- + TOTAL 70.60119790 9661.32098300 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50472 +BPFP 0.0479 bits/point +EBPFP 0.0479 equivalent bits/point +MSE 9661.320983 +---------------------- -------------------------------------------------------- +Time: 1.706s Load: 0.070s, Pack+Encode: 0.608s, Decode+Unpack: 1.028s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9661.3210 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01924916-0.000644_jay _ jay_0.82223135.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01924916-0.000644_jay _ jay_0.82223135.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01944390-0.002567_American robin _ American robin_0.5629079.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01944390-0.002567_American robin _ American robin_0.5629079.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.082s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,636B, BPFP=0.0373 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 27,748B, BPFP=0.0527 +⌛️ [2/4] FRONTEND: Frontend time: 0.605s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.000s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10732387 14.91248519 + layer.39.0 16.74672581 17962.89795918 + ------------------------------------------------------------------------------------- + TOTAL 8.42702484 8988.90522219 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 47384 +BPFP 0.0450 bits/point +EBPFP 0.0450 equivalent bits/point +MSE 8988.905222 +---------------------- -------------------------------------------------------- +Time: 1.687s Load: 0.082s, Pack+Encode: 0.605s, Decode+Unpack: 1.000s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8988.9052 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01944390-0.002567_American robin _ American robin_0.5629079.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01944390-0.002567_American robin _ American robin_0.5629079.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01985128-0.001579_centipede _ centipede_0.85936093.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01985128-0.001579_centipede _ centipede_0.85936093.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,008B, BPFP=0.0399 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,064B, BPFP=0.0590 +⌛️ [2/4] FRONTEND: Frontend time: 0.602s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.041s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09645609 15.07691877 + layer.39.0 23.47999613 18288.52866861 + ------------------------------------------------------------------------------------- + TOTAL 11.78822611 9151.80279369 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52072 +BPFP 0.0494 bits/point +EBPFP 0.0494 equivalent bits/point +MSE 9151.802794 +---------------------- -------------------------------------------------------- +Time: 1.693s Load: 0.050s, Pack+Encode: 0.602s, Decode+Unpack: 1.041s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9151.8028 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01985128-0.001579_centipede _ centipede_0.85936093.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01985128-0.001579_centipede _ centipede_0.85936093.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02037110-0.003503_snowmobile _ snowmobile_0.5200631.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02037110-0.003503_snowmobile _ snowmobile_0.5200631.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,568B, BPFP=0.0390 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,488B, BPFP=0.0579 +⌛️ [2/4] FRONTEND: Frontend time: 0.605s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.012s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09471867 14.49709878 + layer.39.0 17.04498261 16724.32653061 + ------------------------------------------------------------------------------------- + TOTAL 8.56985064 8369.41181470 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51056 +BPFP 0.0485 bits/point +EBPFP 0.0485 equivalent bits/point +MSE 8369.411815 +---------------------- -------------------------------------------------------- +Time: 1.669s Load: 0.052s, Pack+Encode: 0.605s, Decode+Unpack: 1.012s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8369.4118 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02037110-0.003503_snowmobile _ snowmobile_0.5200631.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02037110-0.003503_snowmobile _ snowmobile_0.5200631.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02123394-0.015363_marmot _ marmot_0.82052565.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02123394-0.015363_marmot _ marmot_0.82052565.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,140B, BPFP=0.0363 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 29,864B, BPFP=0.0567 +⌛️ [2/4] FRONTEND: Frontend time: 0.537s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.962s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10209646 14.68870149 + layer.39.0 11.38238543 18463.10787172 + ------------------------------------------------------------------------------------- + TOTAL 5.74224095 9238.89828660 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 49004 +BPFP 0.0465 bits/point +EBPFP 0.0465 equivalent bits/point +MSE 9238.898287 +---------------------- -------------------------------------------------------- +Time: 1.551s Load: 0.052s, Pack+Encode: 0.537s, Decode+Unpack: 0.962s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9238.8983 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02123394-0.015363_marmot _ marmot_0.82052565.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02123394-0.015363_marmot _ marmot_0.82052565.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02165456-0.000157_corn _ corn_0.9868978.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02165456-0.000157_corn _ corn_0.9868978.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,788B, BPFP=0.0376 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,208B, BPFP=0.0573 +⌛️ [2/4] FRONTEND: Frontend time: 0.597s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.005s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10346756 14.86809383 + layer.39.0 776.17699223 20670.29737609 + ------------------------------------------------------------------------------------- + TOTAL 388.14022989 10342.58273496 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 49996 +BPFP 0.0474 bits/point +EBPFP 0.0474 equivalent bits/point +MSE 10342.582735 +---------------------- -------------------------------------------------------- +Time: 1.673s Load: 0.071s, Pack+Encode: 0.597s, Decode+Unpack: 1.005s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10342.5827 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02165456-0.000157_corn _ corn_0.9868978.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02165456-0.000157_corn _ corn_0.9868978.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02219486-0.000060_cliff _ cliff_0.99684334.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02219486-0.000060_cliff _ cliff_0.99684334.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.073s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,788B, BPFP=0.0376 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,192B, BPFP=0.0592 +⌛️ [2/4] FRONTEND: Frontend time: 0.637s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.047s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09584527 14.67427892 + layer.39.0 31.94620460 19186.84936832 + ------------------------------------------------------------------------------------- + TOTAL 16.02102494 9600.76182362 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50980 +BPFP 0.0484 bits/point +EBPFP 0.0484 equivalent bits/point +MSE 9600.761824 +---------------------- -------------------------------------------------------- +Time: 1.757s Load: 0.073s, Pack+Encode: 0.637s, Decode+Unpack: 1.047s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9600.7618 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02219486-0.000060_cliff _ cliff_0.99684334.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02219486-0.000060_cliff _ cliff_0.99684334.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02226429-0.000085_stick insect _ stick insect_0.99900275.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02226429-0.000085_stick insect _ stick insect_0.99900275.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,176B, BPFP=0.0364 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,364B, BPFP=0.0614 +⌛️ [2/4] FRONTEND: Frontend time: 0.561s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.006s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09547379 14.79512592 + layer.39.0 19.16722850 22590.49173955 + ------------------------------------------------------------------------------------- + TOTAL 9.63135114 11302.64343274 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51540 +BPFP 0.0489 bits/point +EBPFP 0.0489 equivalent bits/point +MSE 11302.643433 +---------------------- -------------------------------------------------------- +Time: 1.618s Load: 0.051s, Pack+Encode: 0.561s, Decode+Unpack: 1.006s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 11302.6434 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02226429-0.000085_stick insect _ stick insect_0.99900275.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02226429-0.000085_stick insect _ stick insect_0.99900275.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02231487-0.000080_manhole cover _ manhole cover_0.7554716.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02231487-0.000080_manhole cover _ manhole cover_0.7554716.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 18,668B, BPFP=0.0354 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,072B, BPFP=0.0571 +⌛️ [2/4] FRONTEND: Frontend time: 0.548s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.016s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09512618 14.72831917 + layer.39.0 210.79875790 21704.98736638 + ------------------------------------------------------------------------------------- + TOTAL 105.44694204 10859.85784277 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 48740 +BPFP 0.0463 bits/point +EBPFP 0.0463 equivalent bits/point +MSE 10859.857843 +---------------------- -------------------------------------------------------- +Time: 1.616s Load: 0.052s, Pack+Encode: 0.548s, Decode+Unpack: 1.016s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10859.8578 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02231487-0.000080_manhole cover _ manhole cover_0.7554716.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02231487-0.000080_manhole cover _ manhole cover_0.7554716.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02233338-0.002901_manhole cover _ manhole cover_0.69458175.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02233338-0.002901_manhole cover _ manhole cover_0.69458175.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 18,772B, BPFP=0.0356 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,916B, BPFP=0.0606 +⌛️ [2/4] FRONTEND: Frontend time: 0.591s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.992s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09539769 14.58139065 + layer.39.0 58.97704841 22174.16132167 + ------------------------------------------------------------------------------------- + TOTAL 29.53622305 11094.37135616 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50688 +BPFP 0.0481 bits/point +EBPFP 0.0481 equivalent bits/point +MSE 11094.371356 +---------------------- -------------------------------------------------------- +Time: 1.635s Load: 0.052s, Pack+Encode: 0.591s, Decode+Unpack: 0.992s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 11094.3714 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02233338-0.002901_manhole cover _ manhole cover_0.69458175.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02233338-0.002901_manhole cover _ manhole cover_0.69458175.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02236044-0.000522_sundial _ sundial_0.96381366.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02236044-0.000522_sundial _ sundial_0.96381366.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,408B, BPFP=0.0368 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 29,572B, BPFP=0.0561 +⌛️ [2/4] FRONTEND: Frontend time: 0.623s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.071s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09795647 14.92170892 + layer.39.0 53.12385356 19117.89698737 + ------------------------------------------------------------------------------------- + TOTAL 26.61090502 9566.40934814 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 48980 +BPFP 0.0465 bits/point +EBPFP 0.0465 equivalent bits/point +MSE 9566.409348 +---------------------- -------------------------------------------------------- +Time: 1.746s Load: 0.052s, Pack+Encode: 0.623s, Decode+Unpack: 1.071s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9566.4093 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02236044-0.000522_sundial _ sundial_0.96381366.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02236044-0.000522_sundial _ sundial_0.96381366.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02259212-0.000032_chain _ chain_0.6590295.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02259212-0.000032_chain _ chain_0.6590295.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 18,668B, BPFP=0.0354 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,284B, BPFP=0.0613 +⌛️ [2/4] FRONTEND: Frontend time: 0.591s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.002s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09523673 14.66650628 + layer.39.0 80.66082058 22354.53255588 + ------------------------------------------------------------------------------------- + TOTAL 40.37802865 11184.59953108 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50952 +BPFP 0.0484 bits/point +EBPFP 0.0484 equivalent bits/point +MSE 11184.599531 +---------------------- -------------------------------------------------------- +Time: 1.644s Load: 0.051s, Pack+Encode: 0.591s, Decode+Unpack: 1.002s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 11184.5995 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02259212-0.000032_chain _ chain_0.6590295.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02259212-0.000032_chain _ chain_0.6590295.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02279972-0.000576_apron _ apron_0.7661352.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02279972-0.000576_apron _ apron_0.7661352.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 24,104B, BPFP=0.0458 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 33,484B, BPFP=0.0636 +⌛️ [2/4] FRONTEND: Frontend time: 0.603s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.008s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12772729 15.09096836 + layer.39.0 1038.59135083 23848.06608358 + ------------------------------------------------------------------------------------- + TOTAL 519.35953906 11931.57852597 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 57588 +BPFP 0.0547 bits/point +EBPFP 0.0547 equivalent bits/point +MSE 11931.578526 +---------------------- -------------------------------------------------------- +Time: 1.664s Load: 0.052s, Pack+Encode: 0.603s, Decode+Unpack: 1.008s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 11931.5785 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02279972-0.000576_apron _ apron_0.7661352.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02279972-0.000576_apron _ apron_0.7661352.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02280649-0.001599_custard apple _ leafhopper_0.9128452.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02280649-0.001599_custard apple _ leafhopper_0.9128452.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 18,980B, BPFP=0.0360 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 33,524B, BPFP=0.0636 +⌛️ [2/4] FRONTEND: Frontend time: 0.536s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.965s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09488542 14.75545888 + layer.39.0 1031.59973275 23057.47133139 + ------------------------------------------------------------------------------------- + TOTAL 515.84730909 11536.11339513 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52504 +BPFP 0.0498 bits/point +EBPFP 0.0498 equivalent bits/point +MSE 11536.113395 +---------------------- -------------------------------------------------------- +Time: 1.560s Load: 0.059s, Pack+Encode: 0.536s, Decode+Unpack: 0.965s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 11536.1134 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02280649-0.001599_custard apple _ leafhopper_0.9128452.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02280649-0.001599_custard apple _ leafhopper_0.9128452.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02317335-0.033681_flatworm _ flatworm_0.6070924.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02317335-0.033681_flatworm _ flatworm_0.6070924.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,520B, BPFP=0.0371 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,144B, BPFP=0.0610 +⌛️ [2/4] FRONTEND: Frontend time: 0.522s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.964s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09575805 14.81786492 + layer.39.0 62.35741238 19932.40427600 + ------------------------------------------------------------------------------------- + TOTAL 31.22658522 9973.61107046 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51664 +BPFP 0.0490 bits/point +EBPFP 0.0490 equivalent bits/point +MSE 9973.611070 +---------------------- -------------------------------------------------------- +Time: 1.546s Load: 0.060s, Pack+Encode: 0.522s, Decode+Unpack: 0.964s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9973.6111 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02317335-0.033681_flatworm _ flatworm_0.6070924.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02317335-0.033681_flatworm _ flatworm_0.6070924.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02325366-0.000350_flatworm _ flatworm_0.87634724.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02325366-0.000350_flatworm _ flatworm_0.87634724.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,164B, BPFP=0.0383 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 29,732B, BPFP=0.0564 +⌛️ [2/4] FRONTEND: Frontend time: 0.573s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.971s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09712043 14.76795679 + layer.39.0 30.59439155 17651.36637512 + ------------------------------------------------------------------------------------- + TOTAL 15.34575599 8833.06716596 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 49896 +BPFP 0.0474 bits/point +EBPFP 0.0474 equivalent bits/point +MSE 8833.067166 +---------------------- -------------------------------------------------------- +Time: 1.597s Load: 0.052s, Pack+Encode: 0.573s, Decode+Unpack: 0.971s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8833.0672 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02325366-0.000350_flatworm _ flatworm_0.87634724.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02325366-0.000350_flatworm _ flatworm_0.87634724.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02346627-0.011107_fountain _ skunk_0.28641737.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02346627-0.011107_fountain _ skunk_0.28641737.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,620B, BPFP=0.0410 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,708B, BPFP=0.0602 +⌛️ [2/4] FRONTEND: Frontend time: 0.574s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.996s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09705289 14.54599619 + layer.39.0 9.52721088 18538.58114674 + ------------------------------------------------------------------------------------- + TOTAL 4.81213189 9276.56357147 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 53328 +BPFP 0.0506 bits/point +EBPFP 0.0506 equivalent bits/point +MSE 9276.563571 +---------------------- -------------------------------------------------------- +Time: 1.621s Load: 0.050s, Pack+Encode: 0.574s, Decode+Unpack: 0.996s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9276.5636 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02346627-0.011107_fountain _ skunk_0.28641737.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02346627-0.011107_fountain _ skunk_0.28641737.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02445715-0.036432_shovel _ tarantula_0.26785344.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02445715-0.036432_shovel _ tarantula_0.26785344.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,408B, BPFP=0.0406 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,300B, BPFP=0.0575 +⌛️ [2/4] FRONTEND: Frontend time: 0.583s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.021s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09708806 15.03530335 + layer.39.0 8.00606437 18145.32361516 + ------------------------------------------------------------------------------------- + TOTAL 4.05157622 9080.17945926 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51708 +BPFP 0.0491 bits/point +EBPFP 0.0491 equivalent bits/point +MSE 9080.179459 +---------------------- -------------------------------------------------------- +Time: 1.656s Load: 0.052s, Pack+Encode: 0.583s, Decode+Unpack: 1.021s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9080.1795 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02445715-0.036432_shovel _ tarantula_0.26785344.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02445715-0.036432_shovel _ tarantula_0.26785344.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02454379-0.082010_koala _ koala_0.7052893.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02454379-0.082010_koala _ koala_0.7052893.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.079s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,432B, BPFP=0.0407 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 29,948B, BPFP=0.0568 +⌛️ [2/4] FRONTEND: Frontend time: 0.646s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.003s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14585212 15.49536584 + layer.39.0 44.19989826 18809.20116618 + ------------------------------------------------------------------------------------- + TOTAL 22.17287519 9412.34826601 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51380 +BPFP 0.0488 bits/point +EBPFP 0.0488 equivalent bits/point +MSE 9412.348266 +---------------------- -------------------------------------------------------- +Time: 1.728s Load: 0.079s, Pack+Encode: 0.646s, Decode+Unpack: 1.003s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9412.3483 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02454379-0.082010_koala _ koala_0.7052893.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02454379-0.082010_koala _ koala_0.7052893.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02782093-0.007542_American alligator _ American alligator_0.49872985.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02782093-0.007542_American alligator _ American alligator_0.49872985.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,064B, BPFP=0.0362 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 29,796B, BPFP=0.0566 +⌛️ [2/4] FRONTEND: Frontend time: 0.592s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.045s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09848133 14.70599015 + layer.39.0 9.18780844 19022.26433431 + ------------------------------------------------------------------------------------- + TOTAL 4.64314488 9518.48516223 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 48860 +BPFP 0.0464 bits/point +EBPFP 0.0464 equivalent bits/point +MSE 9518.485162 +---------------------- -------------------------------------------------------- +Time: 1.699s Load: 0.062s, Pack+Encode: 0.592s, Decode+Unpack: 1.045s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9518.4852 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02782093-0.007542_American alligator _ American alligator_0.49872985.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02782093-0.007542_American alligator _ American alligator_0.49872985.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02787622-0.004599_marimba _ accordion_0.25991488.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02787622-0.004599_marimba _ accordion_0.25991488.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,556B, BPFP=0.0390 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,668B, BPFP=0.0620 +⌛️ [2/4] FRONTEND: Frontend time: 0.610s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.040s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12856446 14.96340026 + layer.39.0 1004.59450923 24838.62973761 + ------------------------------------------------------------------------------------- + TOTAL 502.36153685 12426.79656893 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 53224 +BPFP 0.0505 bits/point +EBPFP 0.0505 equivalent bits/point +MSE 12426.796569 +---------------------- -------------------------------------------------------- +Time: 1.701s Load: 0.051s, Pack+Encode: 0.610s, Decode+Unpack: 1.040s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 12426.7966 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02787622-0.004599_marimba _ accordion_0.25991488.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02787622-0.004599_marimba _ accordion_0.25991488.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02793495-0.000130_flatworm _ stingray_0.5528234.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02793495-0.000130_flatworm _ stingray_0.5528234.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,652B, BPFP=0.0392 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,248B, BPFP=0.0593 +⌛️ [2/4] FRONTEND: Frontend time: 0.605s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.027s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09706621 14.83796370 + layer.39.0 8.05872662 16107.18464529 + ------------------------------------------------------------------------------------- + TOTAL 4.07789641 8061.01130449 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51900 +BPFP 0.0493 bits/point +EBPFP 0.0493 equivalent bits/point +MSE 8061.011304 +---------------------- -------------------------------------------------------- +Time: 1.693s Load: 0.061s, Pack+Encode: 0.605s, Decode+Unpack: 1.027s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8061.0113 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02793495-0.000130_flatworm _ stingray_0.5528234.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02793495-0.000130_flatworm _ stingray_0.5528234.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02797295-0.002775_hair dryer _ box turtle_0.2407761.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02797295-0.002775_hair dryer _ box turtle_0.2407761.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.057s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,644B, BPFP=0.0373 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,404B, BPFP=0.0596 +⌛️ [2/4] FRONTEND: Frontend time: 0.588s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.993s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11161610 15.02222083 + layer.39.0 373.09438776 22357.45189504 + ------------------------------------------------------------------------------------- + TOTAL 186.60300193 11186.23705794 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51048 +BPFP 0.0484 bits/point +EBPFP 0.0484 equivalent bits/point +MSE 11186.237058 +---------------------- -------------------------------------------------------- +Time: 1.638s Load: 0.057s, Pack+Encode: 0.588s, Decode+Unpack: 0.993s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 11186.2371 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02797295-0.002775_hair dryer _ box turtle_0.2407761.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02797295-0.002775_hair dryer _ box turtle_0.2407761.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02814860-0.006340_fountain _ fountain_0.7891514.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02814860-0.006340_fountain _ fountain_0.7891514.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,104B, BPFP=0.0382 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 29,348B, BPFP=0.0557 +⌛️ [2/4] FRONTEND: Frontend time: 0.589s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.978s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.04615183 14.52425652 + layer.39.0 7.48662090 17088.95626822 + ------------------------------------------------------------------------------------- + TOTAL 7.76638637 8551.74026237 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 49452 +BPFP 0.0469 bits/point +EBPFP 0.0469 equivalent bits/point +MSE 8551.740262 +---------------------- -------------------------------------------------------- +Time: 1.638s Load: 0.071s, Pack+Encode: 0.589s, Decode+Unpack: 0.978s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8551.7403 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02814860-0.006340_fountain _ fountain_0.7891514.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02814860-0.006340_fountain _ fountain_0.7891514.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02879718-0.003578_maraca _ maraca_0.6809677.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02879718-0.003578_maraca _ maraca_0.6809677.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,468B, BPFP=0.0370 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 29,752B, BPFP=0.0565 +⌛️ [2/4] FRONTEND: Frontend time: 0.522s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.976s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10989876 14.99800986 + layer.39.0 33.03751367 21060.02915452 + ------------------------------------------------------------------------------------- + TOTAL 16.57370621 10537.51358219 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 49220 +BPFP 0.0467 bits/point +EBPFP 0.0467 equivalent bits/point +MSE 10537.513582 +---------------------- -------------------------------------------------------- +Time: 1.549s Load: 0.051s, Pack+Encode: 0.522s, Decode+Unpack: 0.976s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10537.5136 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02879718-0.003578_maraca _ maraca_0.6809677.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02879718-0.003578_maraca _ maraca_0.6809677.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02883205-0.000262_syringe _ syringe_0.7098205.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02883205-0.000262_syringe _ syringe_0.7098205.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.053s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,144B, BPFP=0.0382 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,624B, BPFP=0.0581 +⌛️ [2/4] FRONTEND: Frontend time: 0.591s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.033s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09610580 14.58203125 + layer.39.0 8.14318931 19066.35762877 + ------------------------------------------------------------------------------------- + TOTAL 4.11964755 9540.46983001 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50768 +BPFP 0.0482 bits/point +EBPFP 0.0482 equivalent bits/point +MSE 9540.469830 +---------------------- -------------------------------------------------------- +Time: 1.676s Load: 0.053s, Pack+Encode: 0.591s, Decode+Unpack: 1.033s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9540.4698 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02883205-0.000262_syringe _ syringe_0.7098205.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02883205-0.000262_syringe _ syringe_0.7098205.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02906734-0.000163_stethoscope _ stethoscope_0.9290994.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02906734-0.000163_stethoscope _ stethoscope_0.9290994.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,276B, BPFP=0.0404 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,344B, BPFP=0.0614 +⌛️ [2/4] FRONTEND: Frontend time: 0.583s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.005s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12024398 14.83561483 + layer.39.0 47.23105336 20240.31681244 + ------------------------------------------------------------------------------------- + TOTAL 23.67564867 10127.57621363 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 53620 +BPFP 0.0509 bits/point +EBPFP 0.0509 equivalent bits/point +MSE 10127.576214 +---------------------- -------------------------------------------------------- +Time: 1.639s Load: 0.051s, Pack+Encode: 0.583s, Decode+Unpack: 1.005s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10127.5762 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02906734-0.000163_stethoscope _ stethoscope_0.9290994.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02906734-0.000163_stethoscope _ stethoscope_0.9290994.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02948072-0.002303_digital clock _ digital clock_0.928374.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02948072-0.002303_digital clock _ digital clock_0.928374.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,116B, BPFP=0.0363 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,036B, BPFP=0.0589 +⌛️ [2/4] FRONTEND: Frontend time: 0.567s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.992s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09670976 14.89942071 + layer.39.0 81.62974520 21626.17492711 + ------------------------------------------------------------------------------------- + TOTAL 40.86322748 10820.53717391 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50152 +BPFP 0.0476 bits/point +EBPFP 0.0476 equivalent bits/point +MSE 10820.537174 +---------------------- -------------------------------------------------------- +Time: 1.631s Load: 0.072s, Pack+Encode: 0.567s, Decode+Unpack: 0.992s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10820.5372 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02948072-0.002303_digital clock _ digital clock_0.928374.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02948072-0.002303_digital clock _ digital clock_0.928374.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02999410-0.000148_chest _ chest_0.9948565.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02999410-0.000148_chest _ chest_0.9948565.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,392B, BPFP=0.0368 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,636B, BPFP=0.0581 +⌛️ [2/4] FRONTEND: Frontend time: 0.590s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.981s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10256943 14.75125748 + layer.39.0 13.72598738 19716.90767736 + ------------------------------------------------------------------------------------- + TOTAL 6.91427841 9865.82946742 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50028 +BPFP 0.0475 bits/point +EBPFP 0.0475 equivalent bits/point +MSE 9865.829467 +---------------------- -------------------------------------------------------- +Time: 1.622s Load: 0.050s, Pack+Encode: 0.590s, Decode+Unpack: 0.981s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9865.8295 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02999410-0.000148_chest _ chest_0.9948565.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02999410-0.000148_chest _ chest_0.9948565.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03026506-0.001828_basketball _ basketball_0.6904969.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03026506-0.001828_basketball _ basketball_0.6904969.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,440B, BPFP=0.0388 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,324B, BPFP=0.0614 +⌛️ [2/4] FRONTEND: Frontend time: 0.596s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.039s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09484169 14.61462699 + layer.39.0 87.31533194 20244.40816327 + ------------------------------------------------------------------------------------- + TOTAL 43.70508681 10129.51139513 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52764 +BPFP 0.0501 bits/point +EBPFP 0.0501 equivalent bits/point +MSE 10129.511395 +---------------------- -------------------------------------------------------- +Time: 1.687s Load: 0.052s, Pack+Encode: 0.596s, Decode+Unpack: 1.039s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10129.5114 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03026506-0.001828_basketball _ basketball_0.6904969.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03026506-0.001828_basketball _ basketball_0.6904969.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03124043-0.001154_bow tie _ bow tie_0.9622156.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03124043-0.001154_bow tie _ bow tie_0.9622156.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,684B, BPFP=0.0374 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,572B, BPFP=0.0580 +⌛️ [2/4] FRONTEND: Frontend time: 0.618s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.042s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09893820 14.74959571 + layer.39.0 13.24554141 19539.17006803 + ------------------------------------------------------------------------------------- + TOTAL 6.67223981 9776.95983187 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50256 +BPFP 0.0477 bits/point +EBPFP 0.0477 equivalent bits/point +MSE 9776.959832 +---------------------- -------------------------------------------------------- +Time: 1.729s Load: 0.069s, Pack+Encode: 0.618s, Decode+Unpack: 1.042s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9776.9598 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03124043-0.001154_bow tie _ bow tie_0.9622156.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03124043-0.001154_bow tie _ bow tie_0.9622156.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03196217-0.015958_soap dispenser _ envelope_0.80274254.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03196217-0.015958_soap dispenser _ envelope_0.80274254.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,700B, BPFP=0.0412 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 29,984B, BPFP=0.0569 +⌛️ [2/4] FRONTEND: Frontend time: 0.585s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.045s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10340443 14.51445863 + layer.39.0 8.70910111 17742.27016521 + ------------------------------------------------------------------------------------- + TOTAL 4.40625277 8878.39231192 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51684 +BPFP 0.0491 bits/point +EBPFP 0.0491 equivalent bits/point +MSE 8878.392312 +---------------------- -------------------------------------------------------- +Time: 1.683s Load: 0.052s, Pack+Encode: 0.585s, Decode+Unpack: 1.045s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8878.3923 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03196217-0.015958_soap dispenser _ envelope_0.80274254.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03196217-0.015958_soap dispenser _ envelope_0.80274254.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03223299-0.001528_hot dog _ hot dog_0.9147216.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03223299-0.001528_hot dog _ hot dog_0.9147216.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,796B, BPFP=0.0414 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,872B, BPFP=0.0605 +⌛️ [2/4] FRONTEND: Frontend time: 0.629s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.042s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10130972 14.80183658 + layer.39.0 352.09596696 21425.96112731 + ------------------------------------------------------------------------------------- + TOTAL 176.09863834 10720.38148195 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 53668 +BPFP 0.0509 bits/point +EBPFP 0.0509 equivalent bits/point +MSE 10720.381482 +---------------------- -------------------------------------------------------- +Time: 1.742s Load: 0.070s, Pack+Encode: 0.629s, Decode+Unpack: 1.042s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10720.3815 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03223299-0.001528_hot dog _ hot dog_0.9147216.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03223299-0.001528_hot dog _ hot dog_0.9147216.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03255030-0.005469_bubble _ bubble_0.9381716.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03255030-0.005469_bubble _ bubble_0.9381716.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 18,544B, BPFP=0.0352 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,524B, BPFP=0.0579 +⌛️ [2/4] FRONTEND: Frontend time: 0.603s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.038s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09675161 14.73714430 + layer.39.0 42.23478499 20253.19922255 + ------------------------------------------------------------------------------------- + TOTAL 21.16576830 10133.96818342 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 49068 +BPFP 0.0466 bits/point +EBPFP 0.0466 equivalent bits/point +MSE 10133.968183 +---------------------- -------------------------------------------------------- +Time: 1.712s Load: 0.072s, Pack+Encode: 0.603s, Decode+Unpack: 1.038s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10133.9682 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03255030-0.005469_bubble _ bubble_0.9381716.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03255030-0.005469_bubble _ bubble_0.9381716.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03325584-0.000773_candle _ candle_0.810919.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03325584-0.000773_candle _ candle_0.810919.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,264B, BPFP=0.0366 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 33,692B, BPFP=0.0640 +⌛️ [2/4] FRONTEND: Frontend time: 0.681s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.984s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10394677 14.99594570 + layer.39.0 140.58187561 23446.58503401 + ------------------------------------------------------------------------------------- + TOTAL 70.34291119 11730.79048986 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52956 +BPFP 0.0503 bits/point +EBPFP 0.0503 equivalent bits/point +MSE 11730.790490 +---------------------- -------------------------------------------------------- +Time: 1.745s Load: 0.080s, Pack+Encode: 0.681s, Decode+Unpack: 0.984s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 11730.7905 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03325584-0.000773_candle _ candle_0.810919.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03325584-0.000773_candle _ candle_0.810919.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03355925-0.004997_spider web _ spider web_0.9142101.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03355925-0.004997_spider web _ spider web_0.9142101.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,172B, BPFP=0.0383 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 29,864B, BPFP=0.0567 +⌛️ [2/4] FRONTEND: Frontend time: 0.597s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.019s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09873271 14.68115472 + layer.39.0 6.60211199 16433.78425656 + ------------------------------------------------------------------------------------- + TOTAL 3.35042235 8224.23270564 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50036 +BPFP 0.0475 bits/point +EBPFP 0.0475 equivalent bits/point +MSE 8224.232706 +---------------------- -------------------------------------------------------- +Time: 1.666s Load: 0.051s, Pack+Encode: 0.597s, Decode+Unpack: 1.019s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8224.2327 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03355925-0.004997_spider web _ spider web_0.9142101.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03355925-0.004997_spider web _ spider web_0.9142101.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03384352-0.002273_viaduct _ viaduct_0.6161024.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03384352-0.002273_viaduct _ viaduct_0.6161024.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,292B, BPFP=0.0385 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,316B, BPFP=0.0594 +⌛️ [2/4] FRONTEND: Frontend time: 0.591s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.003s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09647940 14.86175804 + layer.39.0 175.50411504 19332.56559767 + ------------------------------------------------------------------------------------- + TOTAL 87.80029722 9673.71367785 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51608 +BPFP 0.0490 bits/point +EBPFP 0.0490 equivalent bits/point +MSE 9673.713678 +---------------------- -------------------------------------------------------- +Time: 1.655s Load: 0.061s, Pack+Encode: 0.591s, Decode+Unpack: 1.003s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9673.7137 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03384352-0.002273_viaduct _ viaduct_0.6161024.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03384352-0.002273_viaduct _ viaduct_0.6161024.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03388043-0.005154_candle _ candle_0.9636924.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03388043-0.005154_candle _ candle_0.9636924.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.081s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 18,916B, BPFP=0.0359 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 29,552B, BPFP=0.0561 +⌛️ [2/4] FRONTEND: Frontend time: 0.618s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.048s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09640297 14.77167798 + layer.39.0 7.87377147 18498.87852284 + ------------------------------------------------------------------------------------- + TOTAL 3.98508722 9256.82510041 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 48468 +BPFP 0.0460 bits/point +EBPFP 0.0460 equivalent bits/point +MSE 9256.825100 +---------------------- -------------------------------------------------------- +Time: 1.747s Load: 0.081s, Pack+Encode: 0.618s, Decode+Unpack: 1.048s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9256.8251 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03388043-0.005154_candle _ candle_0.9636924.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03388043-0.005154_candle _ candle_0.9636924.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03417042-0.001187_tank _ tank_0.70379025.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03417042-0.001187_tank _ tank_0.70379025.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,708B, BPFP=0.0374 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,768B, BPFP=0.0622 +⌛️ [2/4] FRONTEND: Frontend time: 0.601s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.014s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09848782 14.81074238 + layer.39.0 16.63742104 19131.73566569 + ------------------------------------------------------------------------------------- + TOTAL 8.36795443 9573.27320404 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52476 +BPFP 0.0498 bits/point +EBPFP 0.0498 equivalent bits/point +MSE 9573.273204 +---------------------- -------------------------------------------------------- +Time: 1.667s Load: 0.052s, Pack+Encode: 0.601s, Decode+Unpack: 1.014s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9573.2732 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03417042-0.001187_tank _ tank_0.70379025.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03417042-0.001187_tank _ tank_0.70379025.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03444034-0.002100_maraca _ maraca_0.502369.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03444034-0.002100_maraca _ maraca_0.502369.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 18,992B, BPFP=0.0360 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,084B, BPFP=0.0590 +⌛️ [2/4] FRONTEND: Frontend time: 0.589s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.038s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11197850 15.03813529 + layer.39.0 347.54634354 22786.61418853 + ------------------------------------------------------------------------------------- + TOTAL 173.82916102 11400.82616191 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50076 +BPFP 0.0475 bits/point +EBPFP 0.0475 equivalent bits/point +MSE 11400.826162 +---------------------- -------------------------------------------------------- +Time: 1.697s Load: 0.070s, Pack+Encode: 0.589s, Decode+Unpack: 1.038s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 11400.8262 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03444034-0.002100_maraca _ maraca_0.502369.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03444034-0.002100_maraca _ maraca_0.502369.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03445924-0.003505_baseball player _ baseball player_0.6723684.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03445924-0.003505_baseball player _ baseball player_0.6723684.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,776B, BPFP=0.0375 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,020B, BPFP=0.0608 +⌛️ [2/4] FRONTEND: Frontend time: 0.515s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.962s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09665277 14.60688282 + layer.39.0 26.28463618 19589.44995141 + ------------------------------------------------------------------------------------- + TOTAL 13.19064447 9802.02841711 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51796 +BPFP 0.0492 bits/point +EBPFP 0.0492 equivalent bits/point +MSE 9802.028417 +---------------------- -------------------------------------------------------- +Time: 1.527s Load: 0.051s, Pack+Encode: 0.515s, Decode+Unpack: 0.962s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9802.0284 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03445924-0.003505_baseball player _ baseball player_0.6723684.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03445924-0.003505_baseball player _ baseball player_0.6723684.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03452741-0.002771_chain _ chain_0.9575044.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03452741-0.002771_chain _ chain_0.9575044.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,264B, BPFP=0.0366 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,576B, BPFP=0.0599 +⌛️ [2/4] FRONTEND: Frontend time: 0.569s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.030s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12351380 14.86506639 + layer.39.0 42.82565370 20799.26919339 + ------------------------------------------------------------------------------------- + TOTAL 21.47458375 10407.06712989 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50840 +BPFP 0.0482 bits/point +EBPFP 0.0482 equivalent bits/point +MSE 10407.067130 +---------------------- -------------------------------------------------------- +Time: 1.651s Load: 0.052s, Pack+Encode: 0.569s, Decode+Unpack: 1.030s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10407.0671 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03452741-0.002771_chain _ chain_0.9575044.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03452741-0.002771_chain _ chain_0.9575044.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03483316-0.004974_lighter _ lighter_0.27796906.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03483316-0.004974_lighter _ lighter_0.27796906.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 22,988B, BPFP=0.0436 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,020B, BPFP=0.0608 +⌛️ [2/4] FRONTEND: Frontend time: 0.590s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.992s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12993333 15.31761912 + layer.39.0 87.07173986 20139.80369291 + ------------------------------------------------------------------------------------- + TOTAL 43.60083660 10077.56065601 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 55008 +BPFP 0.0522 bits/point +EBPFP 0.0522 equivalent bits/point +MSE 10077.560656 +---------------------- -------------------------------------------------------- +Time: 1.654s Load: 0.071s, Pack+Encode: 0.590s, Decode+Unpack: 0.992s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10077.5607 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03483316-0.004974_lighter _ lighter_0.27796906.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03483316-0.004974_lighter _ lighter_0.27796906.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03584829-0.104805_golf cart _ golf cart_0.73568964.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03584829-0.104805_golf cart _ golf cart_0.73568964.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,368B, BPFP=0.0387 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 34,300B, BPFP=0.0651 +⌛️ [2/4] FRONTEND: Frontend time: 0.566s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.002s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09917131 14.81179771 + layer.39.0 24.34873246 20664.03498542 + ------------------------------------------------------------------------------------- + TOTAL 12.22395189 10339.42339157 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 54668 +BPFP 0.0519 bits/point +EBPFP 0.0519 equivalent bits/point +MSE 10339.423392 +---------------------- -------------------------------------------------------- +Time: 1.620s Load: 0.052s, Pack+Encode: 0.566s, Decode+Unpack: 1.002s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10339.4234 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03584829-0.104805_golf cart _ golf cart_0.73568964.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03584829-0.104805_golf cart _ golf cart_0.73568964.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03590841-0.001115_rocking chair _ rocking chair_0.2192492.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03590841-0.001115_rocking chair _ rocking chair_0.2192492.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,368B, BPFP=0.0406 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,688B, BPFP=0.0601 +⌛️ [2/4] FRONTEND: Frontend time: 0.623s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.039s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11329899 15.03321831 + layer.39.0 19.97532495 20356.41593780 + ------------------------------------------------------------------------------------- + TOTAL 10.04431197 10185.72457806 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 53056 +BPFP 0.0504 bits/point +EBPFP 0.0504 equivalent bits/point +MSE 10185.724578 +---------------------- -------------------------------------------------------- +Time: 1.714s Load: 0.052s, Pack+Encode: 0.623s, Decode+Unpack: 1.039s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10185.7246 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03590841-0.001115_rocking chair _ rocking chair_0.2192492.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03590841-0.001115_rocking chair _ rocking chair_0.2192492.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03617480-0.003238_basketball _ basketball_0.67568874.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03617480-0.003238_basketball _ basketball_0.67568874.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,420B, BPFP=0.0369 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,432B, BPFP=0.0597 +⌛️ [2/4] FRONTEND: Frontend time: 0.598s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.037s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12967051 15.10615775 + layer.39.0 57.10576865 23614.86491740 + ------------------------------------------------------------------------------------- + TOTAL 28.61771958 11814.98553757 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50852 +BPFP 0.0483 bits/point +EBPFP 0.0483 equivalent bits/point +MSE 11814.985538 +---------------------- -------------------------------------------------------- +Time: 1.714s Load: 0.080s, Pack+Encode: 0.598s, Decode+Unpack: 1.037s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 11814.9855 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03617480-0.003238_basketball _ basketball_0.67568874.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03617480-0.003238_basketball _ basketball_0.67568874.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03666591-0.004622_torch _ torch_0.99906796.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03666591-0.004622_torch _ torch_0.99906796.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.081s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,484B, BPFP=0.0370 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 29,188B, BPFP=0.0554 +⌛️ [2/4] FRONTEND: Frontend time: 0.588s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.024s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.05477861 14.64245095 + layer.39.0 7.78975672 18774.90767736 + ------------------------------------------------------------------------------------- + TOTAL 7.92226767 9394.77506416 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 48672 +BPFP 0.0462 bits/point +EBPFP 0.0462 equivalent bits/point +MSE 9394.775064 +---------------------- -------------------------------------------------------- +Time: 1.693s Load: 0.081s, Pack+Encode: 0.588s, Decode+Unpack: 1.024s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9394.7751 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03666591-0.004622_torch _ torch_0.99906796.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03666591-0.004622_torch _ torch_0.99906796.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03670208-0.003899_hair dryer _ grand piano_0.7359066.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03670208-0.003899_hair dryer _ grand piano_0.7359066.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,712B, BPFP=0.0374 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,056B, BPFP=0.0608 +⌛️ [2/4] FRONTEND: Frontend time: 0.614s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.004s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11232473 14.86469817 + layer.39.0 36.60432231 21107.26336249 + ------------------------------------------------------------------------------------- + TOTAL 18.35832352 10561.06403033 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51768 +BPFP 0.0491 bits/point +EBPFP 0.0491 equivalent bits/point +MSE 10561.064030 +---------------------- -------------------------------------------------------- +Time: 1.689s Load: 0.072s, Pack+Encode: 0.614s, Decode+Unpack: 1.004s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10561.0640 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03670208-0.003899_hair dryer _ grand piano_0.7359066.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03670208-0.003899_hair dryer _ grand piano_0.7359066.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03717622-0.001175_sundial _ sundial_0.9998197.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03717622-0.001175_sundial _ sundial_0.9998197.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,776B, BPFP=0.0375 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,832B, BPFP=0.0604 +⌛️ [2/4] FRONTEND: Frontend time: 0.553s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.973s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13381931 15.09318532 + layer.39.0 773.52204810 24336.79689018 + ------------------------------------------------------------------------------------- + TOTAL 386.82793371 12175.94503775 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51608 +BPFP 0.0490 bits/point +EBPFP 0.0490 equivalent bits/point +MSE 12175.945038 +---------------------- -------------------------------------------------------- +Time: 1.597s Load: 0.070s, Pack+Encode: 0.553s, Decode+Unpack: 0.973s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 12175.9450 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03717622-0.001175_sundial _ sundial_0.9998197.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03717622-0.001175_sundial _ sundial_0.9998197.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03720891-0.001854_African bush elephant _ African bush elephant_0.722115.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03720891-0.001854_African bush elephant _ African bush elephant_0.722115.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 18,888B, BPFP=0.0359 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,712B, BPFP=0.0602 +⌛️ [2/4] FRONTEND: Frontend time: 0.531s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.983s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09642763 14.64666659 + layer.39.0 155.23232507 21277.47327502 + ------------------------------------------------------------------------------------- + TOTAL 77.66437635 10646.05997081 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50600 +BPFP 0.0480 bits/point +EBPFP 0.0480 equivalent bits/point +MSE 10646.059971 +---------------------- -------------------------------------------------------- +Time: 1.576s Load: 0.062s, Pack+Encode: 0.531s, Decode+Unpack: 0.983s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10646.0600 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03720891-0.001854_African bush elephant _ African bush elephant_0.722115.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03720891-0.001854_African bush elephant _ African bush elephant_0.722115.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03721384-0.003327_chain _ chain_0.5599652.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03721384-0.003327_chain _ chain_0.5599652.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,060B, BPFP=0.0400 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 33,528B, BPFP=0.0636 +⌛️ [2/4] FRONTEND: Frontend time: 0.592s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.036s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09561452 14.64492985 + layer.39.0 742.66502672 20602.97764820 + ------------------------------------------------------------------------------------- + TOTAL 371.38032062 10308.81128902 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 54588 +BPFP 0.0518 bits/point +EBPFP 0.0518 equivalent bits/point +MSE 10308.811289 +---------------------- -------------------------------------------------------- +Time: 1.680s Load: 0.052s, Pack+Encode: 0.592s, Decode+Unpack: 1.036s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10308.8113 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03721384-0.003327_chain _ chain_0.5599652.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03721384-0.003327_chain _ chain_0.5599652.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03724870-0.001366_Christmas stocking _ Christmas stocking_0.5709616.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03724870-0.001366_Christmas stocking _ Christmas stocking_0.5709616.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,684B, BPFP=0.0393 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,320B, BPFP=0.0594 +⌛️ [2/4] FRONTEND: Frontend time: 0.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.994s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10329660 14.85548773 + layer.39.0 513.92243683 21489.34888241 + ------------------------------------------------------------------------------------- + TOTAL 257.01286671 10752.10218507 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52004 +BPFP 0.0494 bits/point +EBPFP 0.0494 equivalent bits/point +MSE 10752.102185 +---------------------- -------------------------------------------------------- +Time: 1.635s Load: 0.061s, Pack+Encode: 0.581s, Decode+Unpack: 0.994s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10752.1022 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03724870-0.001366_Christmas stocking _ Christmas stocking_0.5709616.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03724870-0.001366_Christmas stocking _ Christmas stocking_0.5709616.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03775071-0.000145_Christmas stocking _ Christmas stocking_0.9986047.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03775071-0.000145_Christmas stocking _ Christmas stocking_0.9986047.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 18,668B, BPFP=0.0354 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,640B, BPFP=0.0601 +⌛️ [2/4] FRONTEND: Frontend time: 0.592s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.996s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09700392 14.81292707 + layer.39.0 284.92189018 23291.31972789 + ------------------------------------------------------------------------------------- + TOTAL 142.50944705 11653.06632748 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50308 +BPFP 0.0477 bits/point +EBPFP 0.0477 equivalent bits/point +MSE 11653.066327 +---------------------- -------------------------------------------------------- +Time: 1.659s Load: 0.070s, Pack+Encode: 0.592s, Decode+Unpack: 0.996s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 11653.0663 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03775071-0.000145_Christmas stocking _ Christmas stocking_0.9986047.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03775071-0.000145_Christmas stocking _ Christmas stocking_0.9986047.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03788195-0.005975_steam locomotive _ steam locomotive_0.42419377.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03788195-0.005975_steam locomotive _ steam locomotive_0.42419377.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,652B, BPFP=0.0373 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,456B, BPFP=0.0578 +⌛️ [2/4] FRONTEND: Frontend time: 0.591s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.044s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10790903 15.15886081 + layer.39.0 10.34781284 19991.53352770 + ------------------------------------------------------------------------------------- + TOTAL 5.22786094 10003.34619425 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50108 +BPFP 0.0476 bits/point +EBPFP 0.0476 equivalent bits/point +MSE 10003.346194 +---------------------- -------------------------------------------------------- +Time: 1.687s Load: 0.052s, Pack+Encode: 0.591s, Decode+Unpack: 1.044s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10003.3462 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03788195-0.005975_steam locomotive _ steam locomotive_0.42419377.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03788195-0.005975_steam locomotive _ steam locomotive_0.42419377.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03837869-0.002023_lighthouse _ lighthouse_0.5898798.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03837869-0.002023_lighthouse _ lighthouse_0.5898798.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,944B, BPFP=0.0398 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 29,036B, BPFP=0.0551 +⌛️ [2/4] FRONTEND: Frontend time: 0.625s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.984s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12703056 14.99021065 + layer.39.0 141.21340500 18695.86783285 + ------------------------------------------------------------------------------------- + TOTAL 70.67021778 9355.42902175 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 49980 +BPFP 0.0474 bits/point +EBPFP 0.0474 equivalent bits/point +MSE 9355.429022 +---------------------- -------------------------------------------------------- +Time: 1.679s Load: 0.070s, Pack+Encode: 0.625s, Decode+Unpack: 0.984s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9355.4290 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03837869-0.002023_lighthouse _ lighthouse_0.5898798.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03837869-0.002023_lighthouse _ lighthouse_0.5898798.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03840681-0.002188_ladybug _ ladybug_0.9972365.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03840681-0.002188_ladybug _ ladybug_0.9972365.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.053s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,028B, BPFP=0.0380 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,680B, BPFP=0.0601 +⌛️ [2/4] FRONTEND: Frontend time: 0.544s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.966s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09487485 14.49539431 + layer.39.0 29.40353574 18309.59961127 + ------------------------------------------------------------------------------------- + TOTAL 14.74920530 9162.04750279 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51708 +BPFP 0.0491 bits/point +EBPFP 0.0491 equivalent bits/point +MSE 9162.047503 +---------------------- -------------------------------------------------------- +Time: 1.563s Load: 0.053s, Pack+Encode: 0.544s, Decode+Unpack: 0.966s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9162.0475 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03840681-0.002188_ladybug _ ladybug_0.9972365.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03840681-0.002188_ladybug _ ladybug_0.9972365.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03888257-0.004083_rugby ball _ snail_0.41588444.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03888257-0.004083_rugby ball _ snail_0.41588444.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,964B, BPFP=0.0398 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,964B, BPFP=0.0588 +⌛️ [2/4] FRONTEND: Frontend time: 0.579s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.054s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10005040 14.87915775 + layer.39.0 7.47115060 18172.19047619 + ------------------------------------------------------------------------------------- + TOTAL 3.78560050 9093.53481697 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51928 +BPFP 0.0493 bits/point +EBPFP 0.0493 equivalent bits/point +MSE 9093.534817 +---------------------- -------------------------------------------------------- +Time: 1.685s Load: 0.052s, Pack+Encode: 0.579s, Decode+Unpack: 1.054s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9093.5348 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03888257-0.004083_rugby ball _ snail_0.41588444.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03888257-0.004083_rugby ball _ snail_0.41588444.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03891332-0.003727_syringe _ syringe_0.93799996.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03891332-0.003727_syringe _ syringe_0.93799996.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.053s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,392B, BPFP=0.0368 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 34,772B, BPFP=0.0660 +⌛️ [2/4] FRONTEND: Frontend time: 0.574s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.002s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09617506 14.66856095 + layer.39.0 18.45312310 22294.28377065 + ------------------------------------------------------------------------------------- + TOTAL 9.27464908 11154.47616580 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 54164 +BPFP 0.0514 bits/point +EBPFP 0.0514 equivalent bits/point +MSE 11154.476166 +---------------------- -------------------------------------------------------- +Time: 1.629s Load: 0.053s, Pack+Encode: 0.574s, Decode+Unpack: 1.002s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 11154.4762 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03891332-0.003727_syringe _ syringe_0.93799996.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03891332-0.003727_syringe _ syringe_0.93799996.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03982430-0.005102_couch _ couch_0.9976859.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03982430-0.005102_couch _ couch_0.9976859.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.053s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,660B, BPFP=0.0373 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,960B, BPFP=0.0626 +⌛️ [2/4] FRONTEND: Frontend time: 0.580s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.968s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09691652 14.48530126 + layer.39.0 169.89398081 19791.86394558 + ------------------------------------------------------------------------------------- + TOTAL 84.99544866 9903.17462342 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52620 +BPFP 0.0499 bits/point +EBPFP 0.0499 equivalent bits/point +MSE 9903.174623 +---------------------- -------------------------------------------------------- +Time: 1.600s Load: 0.053s, Pack+Encode: 0.580s, Decode+Unpack: 0.968s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9903.1746 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03982430-0.005102_couch _ couch_0.9976859.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03982430-0.005102_couch _ couch_0.9976859.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04033901-0.007476_envelope _ envelope_0.9990971.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04033901-0.007476_envelope _ envelope_0.9990971.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,216B, BPFP=0.0365 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,264B, BPFP=0.0574 +⌛️ [2/4] FRONTEND: Frontend time: 0.546s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.962s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10364226 14.73160380 + layer.39.0 7.34252906 18096.77356657 + ------------------------------------------------------------------------------------- + TOTAL 3.72308566 9055.75258519 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 49480 +BPFP 0.0470 bits/point +EBPFP 0.0470 equivalent bits/point +MSE 9055.752585 +---------------------- -------------------------------------------------------- +Time: 1.560s Load: 0.052s, Pack+Encode: 0.546s, Decode+Unpack: 0.962s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9055.7526 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04033901-0.007476_envelope _ envelope_0.9990971.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04033901-0.007476_envelope _ envelope_0.9990971.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04039381-0.000054_hair dryer _ hair dryer_0.5667548.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04039381-0.000054_hair dryer _ hair dryer_0.5667548.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 18,652B, BPFP=0.0354 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,860B, BPFP=0.0586 +⌛️ [2/4] FRONTEND: Frontend time: 0.612s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.033s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09588603 14.68065837 + layer.39.0 26.21653304 19082.00583090 + ------------------------------------------------------------------------------------- + TOTAL 13.15620954 9548.34324464 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 49512 +BPFP 0.0470 bits/point +EBPFP 0.0470 equivalent bits/point +MSE 9548.343245 +---------------------- -------------------------------------------------------- +Time: 1.716s Load: 0.071s, Pack+Encode: 0.612s, Decode+Unpack: 1.033s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9548.3432 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04039381-0.000054_hair dryer _ hair dryer_0.5667548.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04039381-0.000054_hair dryer _ hair dryer_0.5667548.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04118538-0.005804_cowboy boot _ cowboy boot_0.21208441.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04118538-0.005804_cowboy boot _ cowboy boot_0.21208441.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,664B, BPFP=0.0373 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,176B, BPFP=0.0573 +⌛️ [2/4] FRONTEND: Frontend time: 0.593s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.998s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09664223 14.66812534 + layer.39.0 8.64007266 19468.33041788 + ------------------------------------------------------------------------------------- + TOTAL 4.36835744 9741.49927161 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 49840 +BPFP 0.0473 bits/point +EBPFP 0.0473 equivalent bits/point +MSE 9741.499272 +---------------------- -------------------------------------------------------- +Time: 1.652s Load: 0.061s, Pack+Encode: 0.593s, Decode+Unpack: 0.998s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9741.4993 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04118538-0.005804_cowboy boot _ cowboy boot_0.21208441.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04118538-0.005804_cowboy boot _ cowboy boot_0.21208441.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04146614-0.008793_marimba _ marimba_0.54555196.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04146614-0.008793_marimba _ marimba_0.54555196.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,412B, BPFP=0.0406 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 35,284B, BPFP=0.0670 +⌛️ [2/4] FRONTEND: Frontend time: 0.522s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.967s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09774729 14.86186718 + layer.39.0 155.07908163 22756.61418853 + ------------------------------------------------------------------------------------- + TOTAL 77.58841446 11385.73802786 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 56696 +BPFP 0.0538 bits/point +EBPFP 0.0538 equivalent bits/point +MSE 11385.738028 +---------------------- -------------------------------------------------------- +Time: 1.560s Load: 0.071s, Pack+Encode: 0.522s, Decode+Unpack: 0.967s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 11385.7380 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04146614-0.008793_marimba _ marimba_0.54555196.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04146614-0.008793_marimba _ marimba_0.54555196.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04179913-0.006024_guacamole _ custard apple_0.5550306.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04179913-0.006024_guacamole _ custard apple_0.5550306.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,376B, BPFP=0.0368 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,696B, BPFP=0.0583 +⌛️ [2/4] FRONTEND: Frontend time: 0.578s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.014s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11409367 15.02865912 + layer.39.0 68.43204871 19326.01554908 + ------------------------------------------------------------------------------------- + TOTAL 34.27307119 9670.52210410 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50072 +BPFP 0.0475 bits/point +EBPFP 0.0475 equivalent bits/point +MSE 9670.522104 +---------------------- -------------------------------------------------------- +Time: 1.645s Load: 0.052s, Pack+Encode: 0.578s, Decode+Unpack: 1.014s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9670.5221 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04179913-0.006024_guacamole _ custard apple_0.5550306.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04179913-0.006024_guacamole _ custard apple_0.5550306.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04208210-0.007213_doormat _ doormat_0.81736016.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04208210-0.007213_doormat _ doormat_0.81736016.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,456B, BPFP=0.0388 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 33,044B, BPFP=0.0627 +⌛️ [2/4] FRONTEND: Frontend time: 0.582s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.038s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10601767 14.95861045 + layer.39.0 349.44518343 21410.46452867 + ------------------------------------------------------------------------------------- + TOTAL 174.77560055 10712.71156956 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 53500 +BPFP 0.0508 bits/point +EBPFP 0.0508 equivalent bits/point +MSE 10712.711570 +---------------------- -------------------------------------------------------- +Time: 1.691s Load: 0.071s, Pack+Encode: 0.582s, Decode+Unpack: 1.038s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10712.7116 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04208210-0.007213_doormat _ doormat_0.81736016.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04208210-0.007213_doormat _ doormat_0.81736016.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04270147-0.000435_rotary dial telephone _ rotary dial telephone_0.9268941.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04270147-0.000435_rotary dial telephone _ rotary dial telephone_0.9268941.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 18,416B, BPFP=0.0350 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,248B, BPFP=0.0574 +⌛️ [2/4] FRONTEND: Frontend time: 0.598s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.989s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09464848 14.51647249 + layer.39.0 229.78908528 21025.10592809 + ------------------------------------------------------------------------------------- + TOTAL 114.94186688 10519.81120029 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 48664 +BPFP 0.0462 bits/point +EBPFP 0.0462 equivalent bits/point +MSE 10519.811200 +---------------------- -------------------------------------------------------- +Time: 1.657s Load: 0.071s, Pack+Encode: 0.598s, Decode+Unpack: 0.989s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10519.8112 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04270147-0.000435_rotary dial telephone _ rotary dial telephone_0.9268941.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04270147-0.000435_rotary dial telephone _ rotary dial telephone_0.9268941.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04317175-0.006894_bow tie _ bow tie_0.95877784.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04317175-0.006894_bow tie _ bow tie_0.95877784.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,356B, BPFP=0.0367 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,680B, BPFP=0.0601 +⌛️ [2/4] FRONTEND: Frontend time: 0.589s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.032s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09706025 14.90388404 + layer.39.0 10.87108806 19872.28765792 + ------------------------------------------------------------------------------------- + TOTAL 5.48407415 9943.59577098 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51036 +BPFP 0.0484 bits/point +EBPFP 0.0484 equivalent bits/point +MSE 9943.595771 +---------------------- -------------------------------------------------------- +Time: 1.691s Load: 0.070s, Pack+Encode: 0.589s, Decode+Unpack: 1.032s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9943.5958 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04317175-0.006894_bow tie _ bow tie_0.95877784.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04317175-0.006894_bow tie _ bow tie_0.95877784.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04347754-0.001405_viaduct _ viaduct_0.8445672.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04347754-0.001405_viaduct _ viaduct_0.8445672.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,388B, BPFP=0.0368 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,300B, BPFP=0.0613 +⌛️ [2/4] FRONTEND: Frontend time: 0.585s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.019s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09586499 14.60289495 + layer.39.0 267.55718537 21628.35374150 + ------------------------------------------------------------------------------------- + TOTAL 133.82652518 10821.47831822 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51688 +BPFP 0.0491 bits/point +EBPFP 0.0491 equivalent bits/point +MSE 10821.478318 +---------------------- -------------------------------------------------------- +Time: 1.675s Load: 0.071s, Pack+Encode: 0.585s, Decode+Unpack: 1.019s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10821.4783 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04347754-0.001405_viaduct _ viaduct_0.8445672.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04347754-0.001405_viaduct _ viaduct_0.8445672.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04355338-0.000791_envelope _ envelope_0.9969598.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04355338-0.000791_envelope _ envelope_0.9969598.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,040B, BPFP=0.0380 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,352B, BPFP=0.0614 +⌛️ [2/4] FRONTEND: Frontend time: 0.556s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.037s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10273007 14.73675235 + layer.39.0 331.89978134 22009.99416910 + ------------------------------------------------------------------------------------- + TOTAL 166.00125571 11012.36546072 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52392 +BPFP 0.0497 bits/point +EBPFP 0.0497 equivalent bits/point +MSE 11012.365461 +---------------------- -------------------------------------------------------- +Time: 1.644s Load: 0.051s, Pack+Encode: 0.556s, Decode+Unpack: 1.037s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 11012.3655 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04355338-0.000791_envelope _ envelope_0.9969598.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04355338-0.000791_envelope _ envelope_0.9969598.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04366367-0.002021_parachute _ parachute_0.9226023.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04366367-0.002021_parachute _ parachute_0.9226023.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,960B, BPFP=0.0417 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,572B, BPFP=0.0580 +⌛️ [2/4] FRONTEND: Frontend time: 0.522s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.967s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09577132 14.69722197 + layer.39.0 47.60657343 17743.26141885 + ------------------------------------------------------------------------------------- + TOTAL 23.85117238 8878.97932041 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52532 +BPFP 0.0499 bits/point +EBPFP 0.0499 equivalent bits/point +MSE 8878.979320 +---------------------- -------------------------------------------------------- +Time: 1.540s Load: 0.050s, Pack+Encode: 0.522s, Decode+Unpack: 0.967s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8878.9793 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04366367-0.002021_parachute _ parachute_0.9226023.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04366367-0.002021_parachute _ parachute_0.9226023.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04376876-0.004615_lighter _ lighter_0.69176143.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04376876-0.004615_lighter _ lighter_0.69176143.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 18,860B, BPFP=0.0358 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 29,204B, BPFP=0.0554 +⌛️ [2/4] FRONTEND: Frontend time: 0.624s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.043s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09912059 14.84281425 + layer.39.0 173.01079628 20340.31875607 + ------------------------------------------------------------------------------------- + TOTAL 86.55495844 10177.58078516 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 48064 +BPFP 0.0456 bits/point +EBPFP 0.0456 equivalent bits/point +MSE 10177.580785 +---------------------- -------------------------------------------------------- +Time: 1.738s Load: 0.071s, Pack+Encode: 0.624s, Decode+Unpack: 1.043s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10177.5808 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04376876-0.004615_lighter _ lighter_0.69176143.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04376876-0.004615_lighter _ lighter_0.69176143.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04442312-0.001690_washing machine _ washing machine_0.8494714.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04442312-0.001690_washing machine _ washing machine_0.8494714.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,436B, BPFP=0.0369 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,616B, BPFP=0.0581 +⌛️ [2/4] FRONTEND: Frontend time: 0.525s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.969s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.08302300 14.63455114 + layer.39.0 28.24609944 18581.81729835 + ------------------------------------------------------------------------------------- + TOTAL 18.16456122 9298.22592474 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50052 +BPFP 0.0475 bits/point +EBPFP 0.0475 equivalent bits/point +MSE 9298.225925 +---------------------- -------------------------------------------------------- +Time: 1.545s Load: 0.050s, Pack+Encode: 0.525s, Decode+Unpack: 0.969s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9298.2259 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04442312-0.001690_washing machine _ washing machine_0.8494714.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04442312-0.001690_washing machine _ washing machine_0.8494714.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04456115-0.002573_hair dryer _ envelope_0.34823084.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04456115-0.002573_hair dryer _ envelope_0.34823084.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,512B, BPFP=0.0370 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,612B, BPFP=0.0581 +⌛️ [2/4] FRONTEND: Frontend time: 0.586s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.037s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09444211 14.45726566 + layer.39.0 8.80792942 18593.99028183 + ------------------------------------------------------------------------------------- + TOTAL 4.45118577 9304.22377374 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50124 +BPFP 0.0476 bits/point +EBPFP 0.0476 equivalent bits/point +MSE 9304.223774 +---------------------- -------------------------------------------------------- +Time: 1.694s Load: 0.071s, Pack+Encode: 0.586s, Decode+Unpack: 1.037s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9304.2238 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04456115-0.002573_hair dryer _ envelope_0.34823084.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04456115-0.002573_hair dryer _ envelope_0.34823084.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04509417-0.000350_sundial _ sundial_0.99936897.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04509417-0.000350_sundial _ sundial_0.99936897.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,740B, BPFP=0.0394 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,612B, BPFP=0.0600 +⌛️ [2/4] FRONTEND: Frontend time: 0.531s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.984s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10319057 14.84637789 + layer.39.0 8.14296913 19727.87755102 + ------------------------------------------------------------------------------------- + TOTAL 4.12307985 9871.36196446 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52352 +BPFP 0.0497 bits/point +EBPFP 0.0497 equivalent bits/point +MSE 9871.361964 +---------------------- -------------------------------------------------------- +Time: 1.586s Load: 0.071s, Pack+Encode: 0.531s, Decode+Unpack: 0.984s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9871.3620 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04509417-0.000350_sundial _ sundial_0.99936897.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04509417-0.000350_sundial _ sundial_0.99936897.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04532670-0.000670_spider web _ spider web_0.70711935.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04532670-0.000670_spider web _ spider web_0.70711935.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,100B, BPFP=0.0363 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 29,528B, BPFP=0.0560 +⌛️ [2/4] FRONTEND: Frontend time: 0.574s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.025s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09618602 14.87150279 + layer.39.0 175.41615039 20826.81049563 + ------------------------------------------------------------------------------------- + TOTAL 87.75616821 10420.84099921 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 48628 +BPFP 0.0461 bits/point +EBPFP 0.0461 equivalent bits/point +MSE 10420.840999 +---------------------- -------------------------------------------------------- +Time: 1.668s Load: 0.069s, Pack+Encode: 0.574s, Decode+Unpack: 1.025s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10420.8410 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04532670-0.000670_spider web _ spider web_0.70711935.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04532670-0.000670_spider web _ spider web_0.70711935.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04540053-0.000734_balance beam _ balance beam_0.99765223.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04540053-0.000734_balance beam _ balance beam_0.99765223.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 18,976B, BPFP=0.0360 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 29,144B, BPFP=0.0553 +⌛️ [2/4] FRONTEND: Frontend time: 0.518s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.969s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09941827 14.67721905 + layer.39.0 8.11341412 17087.78036929 + ------------------------------------------------------------------------------------- + TOTAL 4.10641619 8551.22879417 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 48120 +BPFP 0.0457 bits/point +EBPFP 0.0457 equivalent bits/point +MSE 8551.228794 +---------------------- -------------------------------------------------------- +Time: 1.539s Load: 0.052s, Pack+Encode: 0.518s, Decode+Unpack: 0.969s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8551.2288 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04540053-0.000734_balance beam _ balance beam_0.99765223.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04540053-0.000734_balance beam _ balance beam_0.99765223.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04606251-0.003483_cliff _ cliff_0.9972174.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04606251-0.003483_cliff _ cliff_0.9972174.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,332B, BPFP=0.0405 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,956B, BPFP=0.0626 +⌛️ [2/4] FRONTEND: Frontend time: 0.586s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.023s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09940710 14.85654022 + layer.39.0 906.86880466 21092.75218659 + ------------------------------------------------------------------------------------- + TOTAL 453.48410588 10553.80436340 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 54288 +BPFP 0.0515 bits/point +EBPFP 0.0515 equivalent bits/point +MSE 10553.804363 +---------------------- -------------------------------------------------------- +Time: 1.679s Load: 0.070s, Pack+Encode: 0.586s, Decode+Unpack: 1.023s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10553.8044 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04606251-0.003483_cliff _ cliff_0.9972174.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04606251-0.003483_cliff _ cliff_0.9972174.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07583066-0.003935_maraca _ maraca_0.5370013.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07583066-0.003935_maraca _ maraca_0.5370013.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,516B, BPFP=0.0408 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 34,180B, BPFP=0.0649 +⌛️ [2/4] FRONTEND: Frontend time: 0.616s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.042s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12045678 14.76523115 + layer.39.0 38.29438092 20674.12244898 + ------------------------------------------------------------------------------------- + TOTAL 19.20741885 10344.44384006 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 55696 +BPFP 0.0529 bits/point +EBPFP 0.0529 equivalent bits/point +MSE 10344.443840 +---------------------- -------------------------------------------------------- +Time: 1.728s Load: 0.071s, Pack+Encode: 0.616s, Decode+Unpack: 1.042s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10344.4438 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07583066-0.003935_maraca _ maraca_0.5370013.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n07583066-0.003935_maraca _ maraca_0.5370013.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07695742-0.003226_ant _ ant_0.64413536.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07695742-0.003226_ant _ ant_0.64413536.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.053s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,440B, BPFP=0.0407 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,580B, BPFP=0.0599 +⌛️ [2/4] FRONTEND: Frontend time: 0.543s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.994s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.16263347 15.44415562 + layer.39.0 172.10254191 21930.00194363 + ------------------------------------------------------------------------------------- + TOTAL 86.13258769 10972.72304963 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 53020 +BPFP 0.0503 bits/point +EBPFP 0.0503 equivalent bits/point +MSE 10972.723050 +---------------------- -------------------------------------------------------- +Time: 1.589s Load: 0.053s, Pack+Encode: 0.543s, Decode+Unpack: 0.994s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10972.7230 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07695742-0.003226_ant _ ant_0.64413536.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n07695742-0.003226_ant _ ant_0.64413536.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07718472-0.001330_spatula _ spatula_0.5388231.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07718472-0.001330_spatula _ spatula_0.5388231.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,628B, BPFP=0.0392 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 34,100B, BPFP=0.0647 +⌛️ [2/4] FRONTEND: Frontend time: 0.596s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.025s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09672572 14.72438825 + layer.39.0 34.52145211 20698.94460641 + ------------------------------------------------------------------------------------- + TOTAL 17.30908891 10356.83449733 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 54728 +BPFP 0.0519 bits/point +EBPFP 0.0519 equivalent bits/point +MSE 10356.834497 +---------------------- -------------------------------------------------------- +Time: 1.692s Load: 0.072s, Pack+Encode: 0.596s, Decode+Unpack: 1.025s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10356.8345 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07718472-0.001330_spatula _ spatula_0.5388231.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n07718472-0.001330_spatula _ spatula_0.5388231.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07831146-0.002710_guacamole _ guacamole_0.99510396.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07831146-0.002710_guacamole _ guacamole_0.99510396.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,360B, BPFP=0.0367 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,872B, BPFP=0.0624 +⌛️ [2/4] FRONTEND: Frontend time: 0.618s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.983s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09717902 14.79887083 + layer.39.0 26.55584533 20429.56656948 + ------------------------------------------------------------------------------------- + TOTAL 13.32651218 10222.18272016 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52232 +BPFP 0.0496 bits/point +EBPFP 0.0496 equivalent bits/point +MSE 10222.182720 +---------------------- -------------------------------------------------------- +Time: 1.661s Load: 0.060s, Pack+Encode: 0.618s, Decode+Unpack: 0.983s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10222.1827 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07831146-0.002710_guacamole _ guacamole_0.99510396.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n07831146-0.002710_guacamole _ guacamole_0.99510396.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n09229709-0.002628_stethoscope _ stethoscope_0.9726458.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n09229709-0.002628_stethoscope _ stethoscope_0.9726458.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,008B, BPFP=0.0380 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,080B, BPFP=0.0571 +⌛️ [2/4] FRONTEND: Frontend time: 0.535s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.972s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10247729 14.59198763 + layer.39.0 58.71458181 19767.58406220 + ------------------------------------------------------------------------------------- + TOTAL 29.40852955 9891.08802491 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50088 +BPFP 0.0475 bits/point +EBPFP 0.0475 equivalent bits/point +MSE 9891.088025 +---------------------- -------------------------------------------------------- +Time: 1.558s Load: 0.051s, Pack+Encode: 0.535s, Decode+Unpack: 0.972s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9891.0880 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n09229709-0.002628_stethoscope _ stethoscope_0.9726458.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n09229709-0.002628_stethoscope _ stethoscope_0.9726458.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12057211-0.000404_nail _ newt_0.31321314.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12057211-0.000404_nail _ newt_0.31321314.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,808B, BPFP=0.0414 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,856B, BPFP=0.0605 +⌛️ [2/4] FRONTEND: Frontend time: 0.529s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.962s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11577855 15.38790505 + layer.39.0 8.72387956 18823.02429543 + ------------------------------------------------------------------------------------- + TOTAL 4.41982905 9419.20610024 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 53664 +BPFP 0.0509 bits/point +EBPFP 0.0509 equivalent bits/point +MSE 9419.206100 +---------------------- -------------------------------------------------------- +Time: 1.542s Load: 0.050s, Pack+Encode: 0.529s, Decode+Unpack: 0.962s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9419.2061 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12057211-0.000404_nail _ newt_0.31321314.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n12057211-0.000404_nail _ newt_0.31321314.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12144580-0.002806_banana _ banana_0.999156.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12144580-0.002806_banana _ banana_0.999156.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,040B, BPFP=0.0380 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,844B, BPFP=0.0623 +⌛️ [2/4] FRONTEND: Frontend time: 0.522s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.965s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09629347 14.89274895 + layer.39.0 105.38953930 22361.85228377 + ------------------------------------------------------------------------------------- + TOTAL 52.74291638 11188.37251636 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52884 +BPFP 0.0502 bits/point +EBPFP 0.0502 equivalent bits/point +MSE 11188.372516 +---------------------- -------------------------------------------------------- +Time: 1.537s Load: 0.050s, Pack+Encode: 0.522s, Decode+Unpack: 0.965s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 11188.3725 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12144580-0.002806_banana _ banana_0.999156.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n12144580-0.002806_banana _ banana_0.999156.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12267677-0.004617_pomegranate _ pomegranate_0.9159373.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12267677-0.004617_pomegranate _ pomegranate_0.9159373.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,196B, BPFP=0.0364 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,136B, BPFP=0.0591 +⌛️ [2/4] FRONTEND: Frontend time: 0.583s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.012s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10323383 14.88328892 + layer.39.0 78.12042942 20430.90184645 + ------------------------------------------------------------------------------------- + TOTAL 39.11183162 10222.89256769 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50332 +BPFP 0.0478 bits/point +EBPFP 0.0478 equivalent bits/point +MSE 10222.892568 +---------------------- -------------------------------------------------------- +Time: 1.646s Load: 0.052s, Pack+Encode: 0.583s, Decode+Unpack: 1.012s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10222.8926 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12267677-0.004617_pomegranate _ pomegranate_0.9159373.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n12267677-0.004617_pomegranate _ pomegranate_0.9159373.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 0.0486 bits/point +Avg EBPFP 0.0486 equivalent bits/point +Avg MSE 10099.494227 +Avg Time 1.652s +------------------------ ---------------------------- diff --git a/lambda0.001/hyperprior-featurecoding-8bit-individual/cls_in1kr/dtufc_hyperprior-featurecoding_dinov3-total_individual.log b/lambda0.001/hyperprior-featurecoding-8bit-individual/cls_in1kr/dtufc_hyperprior-featurecoding_dinov3-total_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..3ad65deaabd4e3c647537685528e0835454d1e04 --- /dev/null +++ b/lambda0.001/hyperprior-featurecoding-8bit-individual/cls_in1kr/dtufc_hyperprior-featurecoding_dinov3-total_individual.log @@ -0,0 +1,9344 @@ +Experiment: dtufc_hyperprior-featurecoding_dinov3-total_individual +Log file: output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/dtufc_hyperprior-featurecoding_dinov3-total_individual.log +DTUFCCodecConfig: + arch: hyperprior-featurecoding + handler: dinov3-total + checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.001_epochs600_lr0.0001_bs360_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.001_epochs600_lr0.0001_bs360_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 405 +Loaded hyperprior-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.9' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.19' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.29' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.39' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json +Loaded per-key mappings: model=dinov3-total + Keys: ['layer.9', 'layer.19', 'layer.29', 'layer.39'] +---------------- ------------------------------------------------------------------------------------------------------------------------------ +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +Checkpoint codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.001_epochs600_lr0.0001_bs360_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-DINOv3/imagenet1k-r +Output output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr +---------------- ------------------------------------------------------------------------------------------------------------------------------ +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01443537-graffiti_3.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01443537-graffiti_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.088s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,180B, BPFP=0.0383 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,592B, BPFP=0.0581 +⌛️ [2/4] FRONTEND: Frontend time: 0.786s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.039s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09690064 14.92102181 + layer.39.0 23.14008974 18386.49951409 + ------------------------------------------------------------------------------------- + TOTAL 11.61849519 9200.71026795 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50772 +BPFP 0.0482 bits/point +EBPFP 0.0482 equivalent bits/point +MSE 9200.710268 +---------------------- -------------------------------------------------------- +Time: 1.913s Load: 0.088s, Pack+Encode: 0.786s, Decode+Unpack: 1.039s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9200.7103 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01443537-graffiti_3.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01443537-graffiti_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01494475-misc_31.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01494475-misc_31.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.077s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,000B, BPFP=0.0361 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,984B, BPFP=0.0626 +⌛️ [2/4] FRONTEND: Frontend time: 0.577s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.005s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09558801 14.69084916 + layer.39.0 281.54433916 22397.31972789 + ------------------------------------------------------------------------------------- + TOTAL 140.81996359 11206.00528853 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51984 +BPFP 0.0493 bits/point +EBPFP 0.0493 equivalent bits/point +MSE 11206.005289 +---------------------- -------------------------------------------------------- +Time: 1.659s Load: 0.077s, Pack+Encode: 0.577s, Decode+Unpack: 1.005s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 11206.0053 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01494475-misc_31.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01494475-misc_31.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01531178-cartoon_22.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01531178-cartoon_22.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 18,556B, BPFP=0.0352 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 28,356B, BPFP=0.0538 +⌛️ [2/4] FRONTEND: Frontend time: 0.584s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.042s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10319715 14.85429289 + layer.39.0 12.97479918 18506.93683188 + ------------------------------------------------------------------------------------- + TOTAL 6.53899817 9260.89556238 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 46912 +BPFP 0.0445 bits/point +EBPFP 0.0445 equivalent bits/point +MSE 9260.895562 +---------------------- -------------------------------------------------------- +Time: 1.695s Load: 0.069s, Pack+Encode: 0.584s, Decode+Unpack: 1.042s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9260.8956 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01531178-cartoon_22.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01531178-cartoon_22.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01534433-painting_9.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01534433-painting_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,932B, BPFP=0.0397 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,588B, BPFP=0.0600 +⌛️ [2/4] FRONTEND: Frontend time: 0.567s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.046s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10660143 15.01516566 + layer.39.0 8.42910859 19901.40913508 + ------------------------------------------------------------------------------------- + TOTAL 4.26785501 9958.21215037 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52520 +BPFP 0.0498 bits/point +EBPFP 0.0498 equivalent bits/point +MSE 9958.212150 +---------------------- -------------------------------------------------------- +Time: 1.663s Load: 0.050s, Pack+Encode: 0.567s, Decode+Unpack: 1.046s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9958.2122 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01534433-painting_9.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01534433-painting_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01632777-toy_21.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01632777-toy_21.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,132B, BPFP=0.0382 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 35,152B, BPFP=0.0667 +⌛️ [2/4] FRONTEND: Frontend time: 0.567s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.050s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09516629 14.55398521 + layer.39.0 31.73491595 22915.68124393 + ------------------------------------------------------------------------------------- + TOTAL 15.91504112 11465.11761457 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 55284 +BPFP 0.0525 bits/point +EBPFP 0.0525 equivalent bits/point +MSE 11465.117615 +---------------------- -------------------------------------------------------- +Time: 1.669s Load: 0.051s, Pack+Encode: 0.567s, Decode+Unpack: 1.050s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 11465.1176 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01632777-toy_21.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01632777-toy_21.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01748264-misc_18.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01748264-misc_18.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 23,568B, BPFP=0.0447 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 34,276B, BPFP=0.0651 +⌛️ [2/4] FRONTEND: Frontend time: 0.588s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.046s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.16139180 14.98949128 + layer.39.0 362.83485180 21127.33333333 + ------------------------------------------------------------------------------------- + TOTAL 181.49812180 10571.16141230 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 57844 +BPFP 0.0549 bits/point +EBPFP 0.0549 equivalent bits/point +MSE 10571.161412 +---------------------- -------------------------------------------------------- +Time: 1.694s Load: 0.060s, Pack+Encode: 0.588s, Decode+Unpack: 1.046s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10571.1614 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01748264-misc_18.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01748264-misc_18.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01784675-painting_2.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01784675-painting_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,372B, BPFP=0.0387 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 35,916B, BPFP=0.0682 +⌛️ [2/4] FRONTEND: Frontend time: 0.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.017s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13866578 15.30708193 + layer.39.0 232.10166120 26665.73566569 + ------------------------------------------------------------------------------------- + TOTAL 116.12016349 13340.52137381 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 56288 +BPFP 0.0534 bits/point +EBPFP 0.0534 equivalent bits/point +MSE 13340.521374 +---------------------- -------------------------------------------------------- +Time: 1.666s Load: 0.069s, Pack+Encode: 0.581s, Decode+Unpack: 1.017s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 13340.5214 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01784675-painting_2.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01784675-painting_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01820546-painting_29.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01820546-painting_29.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,980B, BPFP=0.0398 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 33,516B, BPFP=0.0636 +⌛️ [2/4] FRONTEND: Frontend time: 0.516s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.028s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10398871 15.13472292 + layer.39.0 202.99580904 20700.54810496 + ------------------------------------------------------------------------------------- + TOTAL 101.54989888 10357.84141394 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 54496 +BPFP 0.0517 bits/point +EBPFP 0.0517 equivalent bits/point +MSE 10357.841414 +---------------------- -------------------------------------------------------- +Time: 1.596s Load: 0.052s, Pack+Encode: 0.516s, Decode+Unpack: 1.028s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10357.8414 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01820546-painting_29.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01820546-painting_29.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01833805-painting_23.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01833805-painting_23.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,204B, BPFP=0.0365 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 29,936B, BPFP=0.0568 +⌛️ [2/4] FRONTEND: Frontend time: 0.563s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.031s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09675035 14.68348271 + layer.39.0 56.43029868 18464.29931973 + ------------------------------------------------------------------------------------- + TOTAL 28.26352451 9239.49140122 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 49140 +BPFP 0.0466 bits/point +EBPFP 0.0466 equivalent bits/point +MSE 9239.491401 +---------------------- -------------------------------------------------------- +Time: 1.645s Load: 0.051s, Pack+Encode: 0.563s, Decode+Unpack: 1.031s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9239.4914 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01833805-painting_23.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01833805-painting_23.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01860187-painting_2.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01860187-painting_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,436B, BPFP=0.0369 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 29,992B, BPFP=0.0569 +⌛️ [2/4] FRONTEND: Frontend time: 0.592s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.009s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09532418 14.79449576 + layer.39.0 11.39113179 18632.74635569 + ------------------------------------------------------------------------------------- + TOTAL 5.74322799 9323.77042572 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 49428 +BPFP 0.0469 bits/point +EBPFP 0.0469 equivalent bits/point +MSE 9323.770426 +---------------------- -------------------------------------------------------- +Time: 1.651s Load: 0.050s, Pack+Encode: 0.592s, Decode+Unpack: 1.009s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9323.7704 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01860187-painting_2.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01860187-painting_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01944390-deviantart_6.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01944390-deviantart_6.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,524B, BPFP=0.0409 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 35,524B, BPFP=0.0674 +⌛️ [2/4] FRONTEND: Frontend time: 0.577s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.024s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10713051 14.65908573 + layer.39.0 82.30322218 20279.06705539 + ------------------------------------------------------------------------------------- + TOTAL 41.20517635 10146.86307056 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 57048 +BPFP 0.0541 bits/point +EBPFP 0.0541 equivalent bits/point +MSE 10146.863071 +---------------------- -------------------------------------------------------- +Time: 1.671s Load: 0.070s, Pack+Encode: 0.577s, Decode+Unpack: 1.024s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10146.8631 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01944390-deviantart_6.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01944390-deviantart_6.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01983481-misc_6.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01983481-misc_6.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 22,036B, BPFP=0.0418 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 33,380B, BPFP=0.0634 +⌛️ [2/4] FRONTEND: Frontend time: 0.558s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.044s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10315659 14.88404625 + layer.39.0 236.29731535 20475.66569485 + ------------------------------------------------------------------------------------- + TOTAL 118.20023597 10245.27487055 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 55416 +BPFP 0.0526 bits/point +EBPFP 0.0526 equivalent bits/point +MSE 10245.274871 +---------------------- -------------------------------------------------------- +Time: 1.654s Load: 0.052s, Pack+Encode: 0.558s, Decode+Unpack: 1.044s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10245.2749 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01983481-misc_6.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01983481-misc_6.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02051845-cartoon_2.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02051845-cartoon_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,648B, BPFP=0.0373 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 29,020B, BPFP=0.0551 +⌛️ [2/4] FRONTEND: Frontend time: 0.575s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.036s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11657756 14.81477295 + layer.39.0 123.57765428 20126.49562682 + ------------------------------------------------------------------------------------- + TOTAL 61.84711592 10070.65519989 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 48668 +BPFP 0.0462 bits/point +EBPFP 0.0462 equivalent bits/point +MSE 10070.655200 +---------------------- -------------------------------------------------------- +Time: 1.680s Load: 0.070s, Pack+Encode: 0.575s, Decode+Unpack: 1.036s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10070.6552 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02051845-cartoon_2.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02051845-cartoon_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02056570-art_2.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02056570-art_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,680B, BPFP=0.0374 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,784B, BPFP=0.0603 +⌛️ [2/4] FRONTEND: Frontend time: 0.555s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.034s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09569211 14.63955638 + layer.39.0 33.39981930 19728.09718173 + ------------------------------------------------------------------------------------- + TOTAL 16.74775571 9871.36836906 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51464 +BPFP 0.0488 bits/point +EBPFP 0.0488 equivalent bits/point +MSE 9871.368369 +---------------------- -------------------------------------------------------- +Time: 1.640s Load: 0.050s, Pack+Encode: 0.555s, Decode+Unpack: 1.034s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9871.3684 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02056570-art_2.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02056570-art_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02085620-misc_90.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02085620-misc_90.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,340B, BPFP=0.0367 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 29,484B, BPFP=0.0560 +⌛️ [2/4] FRONTEND: Frontend time: 0.584s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.045s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09843166 14.78538971 + layer.39.0 72.76188958 19039.69290573 + ------------------------------------------------------------------------------------- + TOTAL 36.43016062 9527.23914772 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 48824 +BPFP 0.0463 bits/point +EBPFP 0.0463 equivalent bits/point +MSE 9527.239148 +---------------------- -------------------------------------------------------- +Time: 1.701s Load: 0.071s, Pack+Encode: 0.584s, Decode+Unpack: 1.045s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9527.2391 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02085620-misc_90.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02085620-misc_90.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088094-misc_39.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088094-misc_39.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,432B, BPFP=0.0407 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,944B, BPFP=0.0587 +⌛️ [2/4] FRONTEND: Frontend time: 0.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.042s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09820385 14.80932546 + layer.39.0 12.32374423 18239.37026239 + ------------------------------------------------------------------------------------- + TOTAL 6.21097404 9127.08979393 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52376 +BPFP 0.0497 bits/point +EBPFP 0.0497 equivalent bits/point +MSE 9127.089794 +---------------------- -------------------------------------------------------- +Time: 1.693s Load: 0.069s, Pack+Encode: 0.581s, Decode+Unpack: 1.042s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9127.0898 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088094-misc_39.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02088094-misc_39.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088466-sketch_11.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088466-sketch_11.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 18,340B, BPFP=0.0348 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,488B, BPFP=0.0579 +⌛️ [2/4] FRONTEND: Frontend time: 0.516s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.964s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09459993 14.57797695 + layer.39.0 16.33682960 17915.03984451 + ------------------------------------------------------------------------------------- + TOTAL 8.21571477 8964.80891073 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 48828 +BPFP 0.0463 bits/point +EBPFP 0.0463 equivalent bits/point +MSE 8964.808911 +---------------------- -------------------------------------------------------- +Time: 1.531s Load: 0.051s, Pack+Encode: 0.516s, Decode+Unpack: 0.964s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8964.8089 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088466-sketch_11.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02088466-sketch_11.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02094433-misc_20.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02094433-misc_20.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,524B, BPFP=0.0371 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,580B, BPFP=0.0618 +⌛️ [2/4] FRONTEND: Frontend time: 0.591s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.042s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09538842 14.76977136 + layer.39.0 94.83275632 20057.20310982 + ------------------------------------------------------------------------------------- + TOTAL 47.46407237 10035.98644059 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52104 +BPFP 0.0494 bits/point +EBPFP 0.0494 equivalent bits/point +MSE 10035.986441 +---------------------- -------------------------------------------------------- +Time: 1.703s Load: 0.070s, Pack+Encode: 0.591s, Decode+Unpack: 1.042s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10035.9864 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02094433-misc_20.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02094433-misc_20.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02097298-misc_9.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02097298-misc_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,928B, BPFP=0.0416 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,664B, BPFP=0.0601 +⌛️ [2/4] FRONTEND: Frontend time: 0.520s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.979s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11199322 15.25814372 + layer.39.0 26.16675018 19902.02526725 + ------------------------------------------------------------------------------------- + TOTAL 13.13937170 9958.64170548 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 53592 +BPFP 0.0509 bits/point +EBPFP 0.0509 equivalent bits/point +MSE 9958.641705 +---------------------- -------------------------------------------------------- +Time: 1.550s Load: 0.051s, Pack+Encode: 0.520s, Decode+Unpack: 0.979s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9958.6417 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02097298-misc_9.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02097298-misc_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02106662-misc_55.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02106662-misc_55.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.058s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,952B, BPFP=0.0379 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 29,508B, BPFP=0.0560 +⌛️ [2/4] FRONTEND: Frontend time: 0.586s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.042s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09642073 14.72355784 + layer.39.0 14.86428154 16674.86491740 + ------------------------------------------------------------------------------------- + TOTAL 7.48035113 8344.79423762 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 49460 +BPFP 0.0469 bits/point +EBPFP 0.0469 equivalent bits/point +MSE 8344.794238 +---------------------- -------------------------------------------------------- +Time: 1.687s Load: 0.058s, Pack+Encode: 0.586s, Decode+Unpack: 1.042s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8344.7942 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02106662-misc_55.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02106662-misc_55.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02109525-sketch_23.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02109525-sketch_23.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 18,680B, BPFP=0.0355 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 29,380B, BPFP=0.0558 +⌛️ [2/4] FRONTEND: Frontend time: 0.586s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.035s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09568003 14.51188104 + layer.39.0 14.01675815 18153.19533528 + ------------------------------------------------------------------------------------- + TOTAL 7.05621909 9083.85360816 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 48060 +BPFP 0.0456 bits/point +EBPFP 0.0456 equivalent bits/point +MSE 9083.853608 +---------------------- -------------------------------------------------------- +Time: 1.691s Load: 0.070s, Pack+Encode: 0.586s, Decode+Unpack: 1.035s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9083.8536 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02109525-sketch_23.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02109525-sketch_23.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110185-painting_33.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110185-painting_33.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 18,536B, BPFP=0.0352 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,376B, BPFP=0.0577 +⌛️ [2/4] FRONTEND: Frontend time: 0.534s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.993s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09599521 14.59596506 + layer.39.0 22.05506522 19012.94071914 + ------------------------------------------------------------------------------------- + TOTAL 11.07553021 9513.76834210 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 48912 +BPFP 0.0464 bits/point +EBPFP 0.0464 equivalent bits/point +MSE 9513.768342 +---------------------- -------------------------------------------------------- +Time: 1.597s Load: 0.070s, Pack+Encode: 0.534s, Decode+Unpack: 0.993s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9513.7683 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110185-painting_33.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02110185-painting_33.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110341-misc_162.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110341-misc_162.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,760B, BPFP=0.0375 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 29,404B, BPFP=0.0558 +⌛️ [2/4] FRONTEND: Frontend time: 0.577s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.026s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11124049 14.80302858 + layer.39.0 14.33747210 18090.93100097 + ------------------------------------------------------------------------------------- + TOTAL 7.22435629 9052.86701477 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 49164 +BPFP 0.0467 bits/point +EBPFP 0.0467 equivalent bits/point +MSE 9052.867015 +---------------------- -------------------------------------------------------- +Time: 1.672s Load: 0.070s, Pack+Encode: 0.577s, Decode+Unpack: 1.026s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9052.8670 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110341-misc_162.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02110341-misc_162.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02165456-tattoo_37.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02165456-tattoo_37.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,516B, BPFP=0.0370 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,588B, BPFP=0.0600 +⌛️ [2/4] FRONTEND: Frontend time: 0.554s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.044s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09780899 14.78170649 + layer.39.0 88.96013271 21322.62390671 + ------------------------------------------------------------------------------------- + TOTAL 44.52897085 10668.70280660 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51104 +BPFP 0.0485 bits/point +EBPFP 0.0485 equivalent bits/point +MSE 10668.702807 +---------------------- -------------------------------------------------------- +Time: 1.650s Load: 0.052s, Pack+Encode: 0.554s, Decode+Unpack: 1.044s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10668.7028 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02165456-tattoo_37.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02165456-tattoo_37.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02219486-misc_3.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02219486-misc_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,864B, BPFP=0.0377 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 29,076B, BPFP=0.0552 +⌛️ [2/4] FRONTEND: Frontend time: 0.601s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.051s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10021695 14.73998383 + layer.39.0 75.73793580 17798.62390671 + ------------------------------------------------------------------------------------- + TOTAL 37.91907638 8906.68194527 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 48940 +BPFP 0.0464 bits/point +EBPFP 0.0464 equivalent bits/point +MSE 8906.681945 +---------------------- -------------------------------------------------------- +Time: 1.721s Load: 0.070s, Pack+Encode: 0.601s, Decode+Unpack: 1.051s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8906.6819 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02219486-misc_3.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02219486-misc_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02226429-tattoo_0.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02226429-tattoo_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,384B, BPFP=0.0368 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,380B, BPFP=0.0596 +⌛️ [2/4] FRONTEND: Frontend time: 0.522s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.010s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09506506 14.62603445 + layer.39.0 201.13660107 20215.67735666 + ------------------------------------------------------------------------------------- + TOTAL 100.61583306 10115.15169556 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50764 +BPFP 0.0482 bits/point +EBPFP 0.0482 equivalent bits/point +MSE 10115.151696 +---------------------- -------------------------------------------------------- +Time: 1.603s Load: 0.070s, Pack+Encode: 0.522s, Decode+Unpack: 1.010s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10115.1517 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02226429-tattoo_0.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02226429-tattoo_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02233338-tattoo_5.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02233338-tattoo_5.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,700B, BPFP=0.0374 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,992B, BPFP=0.0626 +⌛️ [2/4] FRONTEND: Frontend time: 0.592s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.030s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09502332 14.68046097 + layer.39.0 172.43500972 20907.53935860 + ------------------------------------------------------------------------------------- + TOTAL 86.26501652 10461.10990978 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52692 +BPFP 0.0500 bits/point +EBPFP 0.0500 equivalent bits/point +MSE 10461.109910 +---------------------- -------------------------------------------------------- +Time: 1.693s Load: 0.070s, Pack+Encode: 0.592s, Decode+Unpack: 1.030s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10461.1099 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02233338-tattoo_5.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02233338-tattoo_5.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02279972-painting_2.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02279972-painting_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.083s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,496B, BPFP=0.0389 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,632B, BPFP=0.0600 +⌛️ [2/4] FRONTEND: Frontend time: 0.596s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.056s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11337867 15.12496868 + layer.39.0 361.17623299 21396.83770651 + ------------------------------------------------------------------------------------- + TOTAL 180.64480583 10705.98133760 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52128 +BPFP 0.0495 bits/point +EBPFP 0.0495 equivalent bits/point +MSE 10705.981338 +---------------------- -------------------------------------------------------- +Time: 1.735s Load: 0.083s, Pack+Encode: 0.596s, Decode+Unpack: 1.056s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10705.9813 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02279972-painting_2.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02279972-painting_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02317335-sculpture_0.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02317335-sculpture_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 18,524B, BPFP=0.0352 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,788B, BPFP=0.0584 +⌛️ [2/4] FRONTEND: Frontend time: 0.574s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.052s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09546056 14.67586571 + layer.39.0 1163.18707483 21785.01068999 + ------------------------------------------------------------------------------------- + TOTAL 581.64126769 10899.84327785 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 49312 +BPFP 0.0468 bits/point +EBPFP 0.0468 equivalent bits/point +MSE 10899.843278 +---------------------- -------------------------------------------------------- +Time: 1.696s Load: 0.070s, Pack+Encode: 0.574s, Decode+Unpack: 1.052s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10899.8433 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02317335-sculpture_0.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02317335-sculpture_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02346627-painting_9.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02346627-painting_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,340B, BPFP=0.0386 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,236B, BPFP=0.0593 +⌛️ [2/4] FRONTEND: Frontend time: 0.557s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.044s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13205896 15.09093420 + layer.39.0 503.01482021 20528.53838678 + ------------------------------------------------------------------------------------- + TOTAL 251.57343959 10271.81466049 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51576 +BPFP 0.0489 bits/point +EBPFP 0.0489 equivalent bits/point +MSE 10271.814660 +---------------------- -------------------------------------------------------- +Time: 1.654s Load: 0.052s, Pack+Encode: 0.557s, Decode+Unpack: 1.044s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10271.8147 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02346627-painting_9.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02346627-painting_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02391049-deviantart_0.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02391049-deviantart_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,488B, BPFP=0.0370 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 29,744B, BPFP=0.0565 +⌛️ [2/4] FRONTEND: Frontend time: 0.573s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.054s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10116939 14.80360749 + layer.39.0 17.42674737 17595.64625850 + ------------------------------------------------------------------------------------- + TOTAL 8.76395838 8805.22493300 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 49232 +BPFP 0.0467 bits/point +EBPFP 0.0467 equivalent bits/point +MSE 8805.224933 +---------------------- -------------------------------------------------------- +Time: 1.696s Load: 0.069s, Pack+Encode: 0.573s, Decode+Unpack: 1.054s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8805.2249 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02391049-deviantart_0.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02391049-deviantart_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02395406-sculpture_31.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02395406-sculpture_31.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 22,120B, BPFP=0.0420 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,196B, BPFP=0.0611 +⌛️ [2/4] FRONTEND: Frontend time: 0.590s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.035s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11469608 15.21294757 + layer.39.0 30.55020044 20739.13702624 + ------------------------------------------------------------------------------------- + TOTAL 15.33244826 10377.17498690 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 54316 +BPFP 0.0515 bits/point +EBPFP 0.0515 equivalent bits/point +MSE 10377.174987 +---------------------- -------------------------------------------------------- +Time: 1.695s Load: 0.070s, Pack+Encode: 0.590s, Decode+Unpack: 1.035s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10377.1750 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02395406-sculpture_31.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02395406-sculpture_31.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02445715-art_3.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02445715-art_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 18,912B, BPFP=0.0359 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,624B, BPFP=0.0600 +⌛️ [2/4] FRONTEND: Frontend time: 0.557s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.042s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09587883 14.82818669 + layer.39.0 77.63827138 20240.64528669 + ------------------------------------------------------------------------------------- + TOTAL 38.86707511 10127.73673669 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50536 +BPFP 0.0480 bits/point +EBPFP 0.0480 equivalent bits/point +MSE 10127.736737 +---------------------- -------------------------------------------------------- +Time: 1.650s Load: 0.051s, Pack+Encode: 0.557s, Decode+Unpack: 1.042s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10127.7367 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02445715-art_3.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02445715-art_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02672831-sculpture_2.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02672831-sculpture_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,664B, BPFP=0.0392 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 35,380B, BPFP=0.0672 +⌛️ [2/4] FRONTEND: Frontend time: 0.588s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.036s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11638676 15.10105381 + layer.39.0 42.74346681 23031.47910593 + ------------------------------------------------------------------------------------- + TOTAL 21.42992678 11523.29007987 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 56044 +BPFP 0.0532 bits/point +EBPFP 0.0532 equivalent bits/point +MSE 11523.290080 +---------------------- -------------------------------------------------------- +Time: 1.694s Load: 0.070s, Pack+Encode: 0.588s, Decode+Unpack: 1.036s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 11523.2901 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02672831-sculpture_2.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02672831-sculpture_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02701002-videogame_1.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02701002-videogame_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,052B, BPFP=0.0400 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,672B, BPFP=0.0620 +⌛️ [2/4] FRONTEND: Frontend time: 0.568s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.047s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10320827 14.63019505 + layer.39.0 160.61054422 19531.55490768 + ------------------------------------------------------------------------------------- + TOTAL 80.35687624 9773.09255136 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 53724 +BPFP 0.0510 bits/point +EBPFP 0.0510 equivalent bits/point +MSE 9773.092551 +---------------------- -------------------------------------------------------- +Time: 1.667s Load: 0.052s, Pack+Encode: 0.568s, Decode+Unpack: 1.047s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9773.0926 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02701002-videogame_1.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02701002-videogame_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02749479-misc_35.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02749479-misc_35.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,640B, BPFP=0.0392 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 33,280B, BPFP=0.0632 +⌛️ [2/4] FRONTEND: Frontend time: 0.591s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.049s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09764870 14.52575601 + layer.39.0 172.65676628 19397.28668610 + ------------------------------------------------------------------------------------- + TOTAL 86.37720749 9705.90622105 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 53920 +BPFP 0.0512 bits/point +EBPFP 0.0512 equivalent bits/point +MSE 9705.906221 +---------------------- -------------------------------------------------------- +Time: 1.709s Load: 0.070s, Pack+Encode: 0.591s, Decode+Unpack: 1.049s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9705.9062 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02749479-misc_35.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02749479-misc_35.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02769748-cartoon_11.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02769748-cartoon_11.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,828B, BPFP=0.0376 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 29,480B, BPFP=0.0560 +⌛️ [2/4] FRONTEND: Frontend time: 0.564s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.020s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12263774 14.61555895 + layer.39.0 11.02823964 18564.02721088 + ------------------------------------------------------------------------------------- + TOTAL 5.57543869 9289.32138492 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 49308 +BPFP 0.0468 bits/point +EBPFP 0.0468 equivalent bits/point +MSE 9289.321385 +---------------------- -------------------------------------------------------- +Time: 1.635s Load: 0.052s, Pack+Encode: 0.564s, Decode+Unpack: 1.020s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9289.3214 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02769748-cartoon_11.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02769748-cartoon_11.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02793495-sketch_11.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02793495-sketch_11.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,344B, BPFP=0.0405 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,196B, BPFP=0.0611 +⌛️ [2/4] FRONTEND: Frontend time: 0.590s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.062s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09793751 14.61753390 + layer.39.0 182.75789602 20063.20116618 + ------------------------------------------------------------------------------------- + TOTAL 91.42791676 10038.90935004 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 53540 +BPFP 0.0508 bits/point +EBPFP 0.0508 equivalent bits/point +MSE 10038.909350 +---------------------- -------------------------------------------------------- +Time: 1.722s Load: 0.070s, Pack+Encode: 0.590s, Decode+Unpack: 1.062s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10038.9094 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02793495-sketch_11.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02793495-sketch_11.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02797295-misc_13.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02797295-misc_13.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,824B, BPFP=0.0414 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,952B, BPFP=0.0625 +⌛️ [2/4] FRONTEND: Frontend time: 0.539s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.019s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.17140635 15.59077761 + layer.39.0 172.50999150 23580.48979592 + ------------------------------------------------------------------------------------- + TOTAL 86.34069892 11798.04028676 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 54776 +BPFP 0.0520 bits/point +EBPFP 0.0520 equivalent bits/point +MSE 11798.040287 +---------------------- -------------------------------------------------------- +Time: 1.610s Load: 0.052s, Pack+Encode: 0.539s, Decode+Unpack: 1.019s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 11798.0403 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02797295-misc_13.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02797295-misc_13.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02802426-art_1.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02802426-art_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,472B, BPFP=0.0389 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,428B, BPFP=0.0597 +⌛️ [2/4] FRONTEND: Frontend time: 0.571s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.036s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.16523854 15.10594137 + layer.39.0 477.65184645 21194.33430515 + ------------------------------------------------------------------------------------- + TOTAL 238.90854250 10604.72012326 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51900 +BPFP 0.0493 bits/point +EBPFP 0.0493 equivalent bits/point +MSE 10604.720123 +---------------------- -------------------------------------------------------- +Time: 1.658s Load: 0.052s, Pack+Encode: 0.571s, Decode+Unpack: 1.036s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10604.7201 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02802426-art_1.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02802426-art_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02814860-sticker_8.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02814860-sticker_8.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,904B, BPFP=0.0378 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 28,884B, BPFP=0.0548 +⌛️ [2/4] FRONTEND: Frontend time: 0.568s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.002s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12757226 15.04255876 + layer.39.0 19.27598852 19047.24975705 + ------------------------------------------------------------------------------------- + TOTAL 9.70178039 9531.14615791 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 48788 +BPFP 0.0463 bits/point +EBPFP 0.0463 equivalent bits/point +MSE 9531.146158 +---------------------- -------------------------------------------------------- +Time: 1.641s Load: 0.071s, Pack+Encode: 0.568s, Decode+Unpack: 1.002s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9531.1462 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02814860-sticker_8.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02814860-sticker_8.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02841315-graphic_4.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02841315-graphic_4.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,568B, BPFP=0.0390 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 33,876B, BPFP=0.0643 +⌛️ [2/4] FRONTEND: Frontend time: 0.568s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.041s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11826141 14.96461218 + layer.39.0 55.46440340 21345.52575316 + ------------------------------------------------------------------------------------- + TOTAL 27.79133240 10680.24518267 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 54444 +BPFP 0.0517 bits/point +EBPFP 0.0517 equivalent bits/point +MSE 10680.245183 +---------------------- -------------------------------------------------------- +Time: 1.660s Load: 0.051s, Pack+Encode: 0.568s, Decode+Unpack: 1.041s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10680.2452 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02841315-graphic_4.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02841315-graphic_4.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02843684-cartoon_9.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02843684-cartoon_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,828B, BPFP=0.0414 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,004B, BPFP=0.0570 +⌛️ [2/4] FRONTEND: Frontend time: 0.540s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.028s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12386809 15.16396855 + layer.39.0 312.00962707 20244.75996113 + ------------------------------------------------------------------------------------- + TOTAL 156.06674758 10129.96196484 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51832 +BPFP 0.0492 bits/point +EBPFP 0.0492 equivalent bits/point +MSE 10129.961965 +---------------------- -------------------------------------------------------- +Time: 1.640s Load: 0.071s, Pack+Encode: 0.540s, Decode+Unpack: 1.028s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10129.9620 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02843684-cartoon_9.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02843684-cartoon_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02883205-sketch_15.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02883205-sketch_15.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,852B, BPFP=0.0377 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,300B, BPFP=0.0594 +⌛️ [2/4] FRONTEND: Frontend time: 0.571s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.018s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09796664 14.56467235 + layer.39.0 103.64267493 19325.49465500 + ------------------------------------------------------------------------------------- + TOTAL 51.87032078 9670.02966368 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51152 +BPFP 0.0485 bits/point +EBPFP 0.0485 equivalent bits/point +MSE 9670.029664 +---------------------- -------------------------------------------------------- +Time: 1.659s Load: 0.070s, Pack+Encode: 0.571s, Decode+Unpack: 1.018s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9670.0297 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02883205-sketch_15.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02883205-sketch_15.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02906734-graffiti_3.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02906734-graffiti_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 22,460B, BPFP=0.0426 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,300B, BPFP=0.0594 +⌛️ [2/4] FRONTEND: Frontend time: 0.541s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.022s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.17339475 15.38395799 + layer.39.0 166.12656402 22302.10301263 + ------------------------------------------------------------------------------------- + TOTAL 83.14997939 11158.74348531 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 53760 +BPFP 0.0510 bits/point +EBPFP 0.0510 equivalent bits/point +MSE 11158.743485 +---------------------- -------------------------------------------------------- +Time: 1.633s Load: 0.070s, Pack+Encode: 0.541s, Decode+Unpack: 1.022s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 11158.7435 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02906734-graffiti_3.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02906734-graffiti_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02909870-sketch_0.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02909870-sketch_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 22,028B, BPFP=0.0418 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,940B, BPFP=0.0587 +⌛️ [2/4] FRONTEND: Frontend time: 0.598s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.030s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.15317524 14.76891437 + layer.39.0 167.75886783 20313.96307094 + ------------------------------------------------------------------------------------- + TOTAL 83.95602154 10164.36599266 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52968 +BPFP 0.0503 bits/point +EBPFP 0.0503 equivalent bits/point +MSE 10164.365993 +---------------------- -------------------------------------------------------- +Time: 1.699s Load: 0.071s, Pack+Encode: 0.598s, Decode+Unpack: 1.030s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10164.3660 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02909870-sketch_0.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02909870-sketch_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02939185-painting_0.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02939185-painting_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 18,936B, BPFP=0.0359 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,232B, BPFP=0.0593 +⌛️ [2/4] FRONTEND: Frontend time: 0.507s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.965s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09512242 14.68007756 + layer.39.0 131.28711127 19895.84645287 + ------------------------------------------------------------------------------------- + TOTAL 65.69111684 9955.26326521 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50168 +BPFP 0.0476 bits/point +EBPFP 0.0476 equivalent bits/point +MSE 9955.263265 +---------------------- -------------------------------------------------------- +Time: 1.523s Load: 0.051s, Pack+Encode: 0.507s, Decode+Unpack: 0.965s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9955.2633 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02939185-painting_0.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02939185-painting_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02948072-misc_10.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02948072-misc_10.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 18,956B, BPFP=0.0360 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 29,976B, BPFP=0.0569 +⌛️ [2/4] FRONTEND: Frontend time: 0.501s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.965s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09566823 14.66323019 + layer.39.0 102.81622783 20658.37512148 + ------------------------------------------------------------------------------------- + TOTAL 51.45594803 10336.51917583 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 48932 +BPFP 0.0464 bits/point +EBPFP 0.0464 equivalent bits/point +MSE 10336.519176 +---------------------- -------------------------------------------------------- +Time: 1.517s Load: 0.051s, Pack+Encode: 0.501s, Decode+Unpack: 0.965s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10336.5192 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02948072-misc_10.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02948072-misc_10.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02950826-art_1.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02950826-art_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,184B, BPFP=0.0364 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,996B, BPFP=0.0626 +⌛️ [2/4] FRONTEND: Frontend time: 0.556s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.046s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09506074 14.80777852 + layer.39.0 1071.96149174 22484.04859086 + ------------------------------------------------------------------------------------- + TOTAL 536.02827624 11249.42818469 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52180 +BPFP 0.0495 bits/point +EBPFP 0.0495 equivalent bits/point +MSE 11249.428185 +---------------------- -------------------------------------------------------- +Time: 1.653s Load: 0.051s, Pack+Encode: 0.556s, Decode+Unpack: 1.046s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 11249.4282 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02950826-art_1.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02950826-art_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02951358-misc_1.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02951358-misc_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,024B, BPFP=0.0399 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,440B, BPFP=0.0597 +⌛️ [2/4] FRONTEND: Frontend time: 0.552s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.049s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09568294 14.68019619 + layer.39.0 598.97078474 18972.30320700 + ------------------------------------------------------------------------------------- + TOTAL 299.53323384 9493.49170159 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52464 +BPFP 0.0498 bits/point +EBPFP 0.0498 equivalent bits/point +MSE 9493.491702 +---------------------- -------------------------------------------------------- +Time: 1.652s Load: 0.051s, Pack+Encode: 0.552s, Decode+Unpack: 1.049s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9493.4917 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02951358-misc_1.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02951358-misc_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02966193-cartoon_22.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02966193-cartoon_22.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 22,132B, BPFP=0.0420 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 33,116B, BPFP=0.0629 +⌛️ [2/4] FRONTEND: Frontend time: 0.521s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.970s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10376222 15.33139349 + layer.39.0 767.85532070 23284.33041788 + ------------------------------------------------------------------------------------- + TOTAL 383.97954146 11649.83090569 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 55248 +BPFP 0.0524 bits/point +EBPFP 0.0524 equivalent bits/point +MSE 11649.830906 +---------------------- -------------------------------------------------------- +Time: 1.543s Load: 0.051s, Pack+Encode: 0.521s, Decode+Unpack: 0.970s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 11649.8309 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02966193-cartoon_22.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02966193-cartoon_22.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02980441-graphic_3.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02980441-graphic_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,332B, BPFP=0.0405 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,196B, BPFP=0.0573 +⌛️ [2/4] FRONTEND: Frontend time: 0.604s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.036s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09509088 14.60861956 + layer.39.0 13.13791359 17285.92419825 + ------------------------------------------------------------------------------------- + TOTAL 6.61650224 8650.26640891 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51528 +BPFP 0.0489 bits/point +EBPFP 0.0489 equivalent bits/point +MSE 8650.266409 +---------------------- -------------------------------------------------------- +Time: 1.710s Load: 0.070s, Pack+Encode: 0.604s, Decode+Unpack: 1.036s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8650.2664 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02980441-graphic_3.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02980441-graphic_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03124170-painting_15.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03124170-painting_15.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,044B, BPFP=0.0361 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 34,604B, BPFP=0.0657 +⌛️ [2/4] FRONTEND: Frontend time: 0.614s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.052s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10783903 15.09672524 + layer.39.0 326.57091229 24521.21088435 + ------------------------------------------------------------------------------------- + TOTAL 163.33937566 12268.15380480 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 53648 +BPFP 0.0509 bits/point +EBPFP 0.0509 equivalent bits/point +MSE 12268.153805 +---------------------- -------------------------------------------------------- +Time: 1.737s Load: 0.070s, Pack+Encode: 0.614s, Decode+Unpack: 1.052s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 12268.1538 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03124170-painting_15.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03124170-painting_15.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03345487-toy_17.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03345487-toy_17.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,392B, BPFP=0.0406 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,380B, BPFP=0.0615 +⌛️ [2/4] FRONTEND: Frontend time: 0.599s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.057s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10662318 14.78823683 + layer.39.0 198.63900024 19464.17687075 + ------------------------------------------------------------------------------------- + TOTAL 99.37281171 9739.48255379 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 53772 +BPFP 0.0510 bits/point +EBPFP 0.0510 equivalent bits/point +MSE 9739.482554 +---------------------- -------------------------------------------------------- +Time: 1.727s Load: 0.071s, Pack+Encode: 0.599s, Decode+Unpack: 1.057s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9739.4826 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03345487-toy_17.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03345487-toy_17.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03372029-sketch_14.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03372029-sketch_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.068s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,156B, BPFP=0.0383 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,944B, BPFP=0.0587 +⌛️ [2/4] FRONTEND: Frontend time: 0.584s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.050s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12162214 14.90792790 + layer.39.0 228.06095117 21273.19144801 + ------------------------------------------------------------------------------------- + TOTAL 114.09128665 10644.04968796 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51100 +BPFP 0.0485 bits/point +EBPFP 0.0485 equivalent bits/point +MSE 10644.049688 +---------------------- -------------------------------------------------------- +Time: 1.702s Load: 0.068s, Pack+Encode: 0.584s, Decode+Unpack: 1.050s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10644.0497 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03372029-sketch_14.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03372029-sketch_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03424325-misc_4.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03424325-misc_4.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 18,944B, BPFP=0.0360 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,040B, BPFP=0.0570 +⌛️ [2/4] FRONTEND: Frontend time: 0.597s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.035s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10761499 14.99416910 + layer.39.0 21.03287666 20240.27988338 + ------------------------------------------------------------------------------------- + TOTAL 10.57024582 10127.63702624 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 48984 +BPFP 0.0465 bits/point +EBPFP 0.0465 equivalent bits/point +MSE 10127.637026 +---------------------- -------------------------------------------------------- +Time: 1.702s Load: 0.070s, Pack+Encode: 0.597s, Decode+Unpack: 1.035s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10127.6370 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03424325-misc_4.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03424325-misc_4.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03467068-sketch_17.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03467068-sketch_17.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,848B, BPFP=0.0377 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,108B, BPFP=0.0590 +⌛️ [2/4] FRONTEND: Frontend time: 0.560s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.030s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09564773 14.78851585 + layer.39.0 208.14688107 21820.57531584 + ------------------------------------------------------------------------------------- + TOTAL 104.12126440 10917.68191585 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50956 +BPFP 0.0484 bits/point +EBPFP 0.0484 equivalent bits/point +MSE 10917.681916 +---------------------- -------------------------------------------------------- +Time: 1.641s Load: 0.052s, Pack+Encode: 0.560s, Decode+Unpack: 1.030s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10917.6819 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03467068-sketch_17.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03467068-sketch_17.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03481172-sketch_9.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03481172-sketch_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,064B, BPFP=0.0400 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,716B, BPFP=0.0583 +⌛️ [2/4] FRONTEND: Frontend time: 0.510s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.013s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14641065 14.69218067 + layer.39.0 516.28267736 21011.37026239 + ------------------------------------------------------------------------------------- + TOTAL 258.21454400 10513.03122153 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51780 +BPFP 0.0491 bits/point +EBPFP 0.0491 equivalent bits/point +MSE 10513.031222 +---------------------- -------------------------------------------------------- +Time: 1.574s Load: 0.052s, Pack+Encode: 0.510s, Decode+Unpack: 1.013s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10513.0312 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03481172-sketch_9.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03481172-sketch_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03494278-deviantart_3.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03494278-deviantart_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,068B, BPFP=0.0381 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,796B, BPFP=0.0585 +⌛️ [2/4] FRONTEND: Frontend time: 0.590s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.032s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09714438 14.34155867 + layer.39.0 11.38600982 17685.23420797 + ------------------------------------------------------------------------------------- + TOTAL 5.74157710 8849.78788332 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50864 +BPFP 0.0483 bits/point +EBPFP 0.0483 equivalent bits/point +MSE 8849.787883 +---------------------- -------------------------------------------------------- +Time: 1.681s Load: 0.060s, Pack+Encode: 0.590s, Decode+Unpack: 1.032s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8849.7879 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03494278-deviantart_3.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03494278-deviantart_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03495258-painting_3.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03495258-painting_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,932B, BPFP=0.0397 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 33,684B, BPFP=0.0639 +⌛️ [2/4] FRONTEND: Frontend time: 0.587s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.034s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10398556 15.05870004 + layer.39.0 359.17207240 23906.66277940 + ------------------------------------------------------------------------------------- + TOTAL 179.63802898 11960.86073972 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 54616 +BPFP 0.0518 bits/point +EBPFP 0.0518 equivalent bits/point +MSE 11960.860740 +---------------------- -------------------------------------------------------- +Time: 1.692s Load: 0.070s, Pack+Encode: 0.587s, Decode+Unpack: 1.034s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 11960.8607 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03495258-painting_3.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03495258-painting_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03498962-sketch_8.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03498962-sketch_8.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,512B, BPFP=0.0408 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,956B, BPFP=0.0588 +⌛️ [2/4] FRONTEND: Frontend time: 0.598s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.004s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.16074808 14.94167768 + layer.39.0 476.99061589 20850.93877551 + ------------------------------------------------------------------------------------- + TOTAL 238.57568198 10432.94022659 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52468 +BPFP 0.0498 bits/point +EBPFP 0.0498 equivalent bits/point +MSE 10432.940227 +---------------------- -------------------------------------------------------- +Time: 1.662s Load: 0.060s, Pack+Encode: 0.598s, Decode+Unpack: 1.004s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10432.9402 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03498962-sketch_8.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03498962-sketch_8.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03602883-misc_12.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03602883-misc_12.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.058s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,228B, BPFP=0.0384 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 29,520B, BPFP=0.0560 +⌛️ [2/4] FRONTEND: Frontend time: 0.575s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.048s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.09080038 14.47802402 + layer.39.0 100.93773536 18347.82118562 + ------------------------------------------------------------------------------------- + TOTAL 54.51426787 9181.14960482 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 49748 +BPFP 0.0472 bits/point +EBPFP 0.0472 equivalent bits/point +MSE 9181.149605 +---------------------- -------------------------------------------------------- +Time: 1.681s Load: 0.058s, Pack+Encode: 0.575s, Decode+Unpack: 1.048s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9181.1496 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03602883-misc_12.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03602883-misc_12.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03630383-toy_1.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03630383-toy_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 18,536B, BPFP=0.0352 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,116B, BPFP=0.0572 +⌛️ [2/4] FRONTEND: Frontend time: 0.534s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.013s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09574974 14.52389968 + layer.39.0 14.66923857 18334.99125364 + ------------------------------------------------------------------------------------- + TOTAL 7.38249415 9174.75757666 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 48652 +BPFP 0.0462 bits/point +EBPFP 0.0462 equivalent bits/point +MSE 9174.757577 +---------------------- -------------------------------------------------------- +Time: 1.618s Load: 0.070s, Pack+Encode: 0.534s, Decode+Unpack: 1.013s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9174.7576 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03630383-toy_1.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03630383-toy_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03649909-toy_22.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03649909-toy_22.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,260B, BPFP=0.0385 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 29,716B, BPFP=0.0564 +⌛️ [2/4] FRONTEND: Frontend time: 0.594s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.046s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09878858 14.63416014 + layer.39.0 29.68475348 17377.04956268 + ------------------------------------------------------------------------------------- + TOTAL 14.89177103 8695.84186141 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 49976 +BPFP 0.0474 bits/point +EBPFP 0.0474 equivalent bits/point +MSE 8695.841861 +---------------------- -------------------------------------------------------- +Time: 1.711s Load: 0.070s, Pack+Encode: 0.594s, Decode+Unpack: 1.046s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8695.8419 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03649909-toy_22.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03649909-toy_22.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03676483-sculpture_3.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03676483-sculpture_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 17.290s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 18,696B, BPFP=0.0355 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,124B, BPFP=0.0610 +⌛️ [2/4] FRONTEND: Frontend time: 0.594s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.047s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09491264 14.53800527 + layer.39.0 32.22669916 21584.34207969 + ------------------------------------------------------------------------------------- + TOTAL 16.16080590 10799.44004248 + (elements=8,429,568) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50820 +BPFP 0.0482 bits/point +EBPFP 0.0482 equivalent bits/point +MSE 10799.440042 +---------------------- --------------------------------------------------------- +Time: 18.930s Load: 17.290s, Pack+Encode: 0.594s, Decode+Unpack: 1.047s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10799.4400 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03676483-sculpture_3.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03676483-sculpture_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03710193-sketch_14.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03710193-sketch_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,244B, BPFP=0.0384 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 29,768B, BPFP=0.0565 +⌛️ [2/4] FRONTEND: Frontend time: 0.588s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.049s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.47394152 14.58926863 + layer.39.0 335.99814747 20650.35179786 + ------------------------------------------------------------------------------------- + TOTAL 168.23604450 10332.47053325 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50012 +BPFP 0.0475 bits/point +EBPFP 0.0475 equivalent bits/point +MSE 10332.470533 +---------------------- -------------------------------------------------------- +Time: 1.708s Load: 0.071s, Pack+Encode: 0.588s, Decode+Unpack: 1.049s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10332.4705 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03710193-sketch_14.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03710193-sketch_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03773504-graphic_4.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03773504-graphic_4.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,192B, BPFP=0.0364 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 29,408B, BPFP=0.0558 +⌛️ [2/4] FRONTEND: Frontend time: 0.561s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.019s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09681199 14.61161379 + layer.39.0 18.83313593 17473.54324587 + ------------------------------------------------------------------------------------- + TOTAL 9.46497396 8744.07742983 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 48600 +BPFP 0.0461 bits/point +EBPFP 0.0461 equivalent bits/point +MSE 8744.077430 +---------------------- -------------------------------------------------------- +Time: 1.630s Load: 0.050s, Pack+Encode: 0.561s, Decode+Unpack: 1.019s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8744.0774 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03773504-graphic_4.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03773504-graphic_4.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03775071-painting_1.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03775071-painting_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,896B, BPFP=0.0397 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,960B, BPFP=0.0607 +⌛️ [2/4] FRONTEND: Frontend time: 0.589s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.048s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11048905 15.05306559 + layer.39.0 386.73560496 21057.30806608 + ------------------------------------------------------------------------------------- + TOTAL 193.42304701 10536.18056584 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52856 +BPFP 0.0502 bits/point +EBPFP 0.0502 equivalent bits/point +MSE 10536.180566 +---------------------- -------------------------------------------------------- +Time: 1.696s Load: 0.060s, Pack+Encode: 0.589s, Decode+Unpack: 1.048s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10536.1806 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03775071-painting_1.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03775071-painting_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03888257-cartoon_30.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03888257-cartoon_30.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,740B, BPFP=0.0375 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 29,532B, BPFP=0.0561 +⌛️ [2/4] FRONTEND: Frontend time: 0.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.025s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13203045 15.03647542 + layer.39.0 375.96832483 20040.63362488 + ------------------------------------------------------------------------------------- + TOTAL 188.05017764 10027.83505015 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 49272 +BPFP 0.0468 bits/point +EBPFP 0.0468 equivalent bits/point +MSE 10027.835050 +---------------------- -------------------------------------------------------- +Time: 1.667s Load: 0.060s, Pack+Encode: 0.581s, Decode+Unpack: 1.025s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10027.8351 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03888257-cartoon_30.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03888257-cartoon_30.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03930630-toy_2.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03930630-toy_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,772B, BPFP=0.0375 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,836B, BPFP=0.0604 +⌛️ [2/4] FRONTEND: Frontend time: 0.588s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.057s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09699417 14.66146118 + layer.39.0 46.17573949 18218.38289602 + ------------------------------------------------------------------------------------- + TOTAL 23.13636683 9116.52217860 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51608 +BPFP 0.0490 bits/point +EBPFP 0.0490 equivalent bits/point +MSE 9116.522179 +---------------------- -------------------------------------------------------- +Time: 1.715s Load: 0.070s, Pack+Encode: 0.588s, Decode+Unpack: 1.057s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9116.5222 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03930630-toy_2.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03930630-toy_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04086273-sticker_5.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04086273-sticker_5.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,348B, BPFP=0.0405 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,524B, BPFP=0.0617 +⌛️ [2/4] FRONTEND: Frontend time: 0.559s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.022s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10161624 14.71474695 + layer.39.0 24.98063198 19180.28571429 + ------------------------------------------------------------------------------------- + TOTAL 12.54112411 9597.50023062 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 53872 +BPFP 0.0511 bits/point +EBPFP 0.0511 equivalent bits/point +MSE 9597.500231 +---------------------- -------------------------------------------------------- +Time: 1.633s Load: 0.052s, Pack+Encode: 0.559s, Decode+Unpack: 1.022s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9597.5002 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04086273-sticker_5.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04086273-sticker_5.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04118538-sculpture_0.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04118538-sculpture_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,432B, BPFP=0.0369 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,064B, BPFP=0.0590 +⌛️ [2/4] FRONTEND: Frontend time: 0.590s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.034s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09846411 14.74194549 + layer.39.0 11.87055944 21160.82798834 + ------------------------------------------------------------------------------------- + TOTAL 5.98451177 10587.78496692 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50496 +BPFP 0.0479 bits/point +EBPFP 0.0479 equivalent bits/point +MSE 10587.784967 +---------------------- -------------------------------------------------------- +Time: 1.684s Load: 0.061s, Pack+Encode: 0.590s, Decode+Unpack: 1.034s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10587.7850 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04118538-sculpture_0.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04118538-sculpture_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04133789-cartoon_7.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04133789-cartoon_7.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 22,000B, BPFP=0.0418 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,164B, BPFP=0.0592 +⌛️ [2/4] FRONTEND: Frontend time: 0.554s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.041s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13739287 15.36488798 + layer.39.0 370.52532799 22784.26044704 + ------------------------------------------------------------------------------------- + TOTAL 185.33136043 11399.81266751 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 53164 +BPFP 0.0505 bits/point +EBPFP 0.0505 equivalent bits/point +MSE 11399.812668 +---------------------- -------------------------------------------------------- +Time: 1.647s Load: 0.052s, Pack+Encode: 0.554s, Decode+Unpack: 1.041s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 11399.8127 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04133789-cartoon_7.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04133789-cartoon_7.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04141076-cartoon_14.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04141076-cartoon_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,348B, BPFP=0.0386 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 29,168B, BPFP=0.0554 +⌛️ [2/4] FRONTEND: Frontend time: 0.585s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.053s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11960477 14.61154356 + layer.39.0 53.25505649 18641.90864917 + ------------------------------------------------------------------------------------- + TOTAL 26.68733063 9328.26009637 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 49516 +BPFP 0.0470 bits/point +EBPFP 0.0470 equivalent bits/point +MSE 9328.260096 +---------------------- -------------------------------------------------------- +Time: 1.708s Load: 0.070s, Pack+Encode: 0.585s, Decode+Unpack: 1.053s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9328.2601 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04141076-cartoon_14.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04141076-cartoon_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04146614-misc_4.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04146614-misc_4.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,168B, BPFP=0.0402 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 33,608B, BPFP=0.0638 +⌛️ [2/4] FRONTEND: Frontend time: 0.585s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.035s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10047569 14.81731543 + layer.39.0 167.29959305 19654.82215743 + ------------------------------------------------------------------------------------- + TOTAL 83.70003437 9834.81973643 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 54776 +BPFP 0.0520 bits/point +EBPFP 0.0520 equivalent bits/point +MSE 9834.819736 +---------------------- -------------------------------------------------------- +Time: 1.689s Load: 0.069s, Pack+Encode: 0.585s, Decode+Unpack: 1.035s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9834.8197 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04146614-misc_4.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04146614-misc_4.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04147183-art_9.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04147183-art_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,332B, BPFP=0.0405 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,440B, BPFP=0.0597 +⌛️ [2/4] FRONTEND: Frontend time: 0.590s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.048s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11332939 14.94742506 + layer.39.0 22.95352360 19087.47716229 + ------------------------------------------------------------------------------------- + TOTAL 11.53342649 9551.21229368 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52772 +BPFP 0.0501 bits/point +EBPFP 0.0501 equivalent bits/point +MSE 9551.212294 +---------------------- -------------------------------------------------------- +Time: 1.708s Load: 0.070s, Pack+Encode: 0.590s, Decode+Unpack: 1.048s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9551.2123 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04147183-art_9.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04147183-art_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04192698-videogame_16.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04192698-videogame_16.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 22,880B, BPFP=0.0434 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 29,412B, BPFP=0.0558 +⌛️ [2/4] FRONTEND: Frontend time: 0.592s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.049s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09706018 14.70152492 + layer.39.0 404.66927843 20859.59378037 + ------------------------------------------------------------------------------------- + TOTAL 202.38316930 10437.14765264 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52292 +BPFP 0.0496 bits/point +EBPFP 0.0496 equivalent bits/point +MSE 10437.147653 +---------------------- -------------------------------------------------------- +Time: 1.703s Load: 0.061s, Pack+Encode: 0.592s, Decode+Unpack: 1.049s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10437.1477 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04192698-videogame_16.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04192698-videogame_16.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04254680-deviantart_17.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04254680-deviantart_17.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,396B, BPFP=0.0387 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 29,964B, BPFP=0.0569 +⌛️ [2/4] FRONTEND: Frontend time: 0.589s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.035s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10685510 14.56905028 + layer.39.0 151.81593173 20189.65986395 + ------------------------------------------------------------------------------------- + TOTAL 75.96139341 10102.11445711 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50360 +BPFP 0.0478 bits/point +EBPFP 0.0478 equivalent bits/point +MSE 10102.114457 +---------------------- -------------------------------------------------------- +Time: 1.693s Load: 0.070s, Pack+Encode: 0.589s, Decode+Unpack: 1.035s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10102.1145 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04254680-deviantart_17.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04254680-deviantart_17.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04266014-painting_13.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04266014-painting_13.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 22,804B, BPFP=0.0433 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 33,436B, BPFP=0.0635 +⌛️ [2/4] FRONTEND: Frontend time: 0.595s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.047s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09568562 14.78863258 + layer.39.0 29.62437363 20327.94169096 + ------------------------------------------------------------------------------------- + TOTAL 14.86002963 10171.36516177 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 56240 +BPFP 0.0534 bits/point +EBPFP 0.0534 equivalent bits/point +MSE 10171.365162 +---------------------- -------------------------------------------------------- +Time: 1.712s Load: 0.070s, Pack+Encode: 0.595s, Decode+Unpack: 1.047s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10171.3652 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04266014-painting_13.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04266014-painting_13.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04310018-art_3.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04310018-art_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 22,952B, BPFP=0.0436 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 33,704B, BPFP=0.0640 +⌛️ [2/4] FRONTEND: Frontend time: 0.556s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.048s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13375617 14.84853411 + layer.39.0 75.24515610 18872.55587949 + ------------------------------------------------------------------------------------- + TOTAL 37.68945614 9443.70220680 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 56656 +BPFP 0.0538 bits/point +EBPFP 0.0538 equivalent bits/point +MSE 9443.702207 +---------------------- -------------------------------------------------------- +Time: 1.654s Load: 0.050s, Pack+Encode: 0.556s, Decode+Unpack: 1.048s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9443.7022 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04310018-art_3.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04310018-art_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04347754-sculpture_0.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04347754-sculpture_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 22,612B, BPFP=0.0429 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,192B, BPFP=0.0573 +⌛️ [2/4] FRONTEND: Frontend time: 0.554s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.037s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14257451 15.46218169 + layer.39.0 394.23636419 19621.87172012 + ------------------------------------------------------------------------------------- + TOTAL 197.18946935 9818.66695090 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52804 +BPFP 0.0501 bits/point +EBPFP 0.0501 equivalent bits/point +MSE 9818.666951 +---------------------- -------------------------------------------------------- +Time: 1.643s Load: 0.051s, Pack+Encode: 0.554s, Decode+Unpack: 1.037s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9818.6670 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04347754-sculpture_0.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04347754-sculpture_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04409515-deviantart_1.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04409515-deviantart_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,284B, BPFP=0.0404 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 33,228B, BPFP=0.0631 +⌛️ [2/4] FRONTEND: Frontend time: 0.595s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.005s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09627266 14.78952753 + layer.39.0 9.33068077 20876.04859086 + ------------------------------------------------------------------------------------- + TOTAL 4.71347671 10445.41905920 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 54512 +BPFP 0.0517 bits/point +EBPFP 0.0517 equivalent bits/point +MSE 10445.419059 +---------------------- -------------------------------------------------------- +Time: 1.671s Load: 0.070s, Pack+Encode: 0.595s, Decode+Unpack: 1.005s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10445.4191 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04409515-deviantart_1.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04409515-deviantart_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04487394-deviantart_8.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04487394-deviantart_8.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,272B, BPFP=0.0366 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,748B, BPFP=0.0603 +⌛️ [2/4] FRONTEND: Frontend time: 0.514s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.008s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09911632 14.92171556 + layer.39.0 99.63155977 21279.63265306 + ------------------------------------------------------------------------------------- + TOTAL 49.86533804 10647.27718431 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51020 +BPFP 0.0484 bits/point +EBPFP 0.0484 equivalent bits/point +MSE 10647.277184 +---------------------- -------------------------------------------------------- +Time: 1.573s Load: 0.051s, Pack+Encode: 0.514s, Decode+Unpack: 1.008s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10647.2772 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04487394-deviantart_8.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04487394-deviantart_8.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04522168-painting_32.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04522168-painting_32.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,076B, BPFP=0.0381 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 28,800B, BPFP=0.0547 +⌛️ [2/4] FRONTEND: Frontend time: 0.546s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.006s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11740584 14.89200111 + layer.39.0 10.95138066 17471.93197279 + ------------------------------------------------------------------------------------- + TOTAL 5.53439325 8743.41198695 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 48876 +BPFP 0.0464 bits/point +EBPFP 0.0464 equivalent bits/point +MSE 8743.411987 +---------------------- -------------------------------------------------------- +Time: 1.603s Load: 0.051s, Pack+Encode: 0.546s, Decode+Unpack: 1.006s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8743.4120 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04522168-painting_32.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04522168-painting_32.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04591713-painting_14.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04591713-painting_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 16.960s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,056B, BPFP=0.0381 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,356B, BPFP=0.0576 +⌛️ [2/4] FRONTEND: Frontend time: 0.558s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.045s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11212821 15.26075642 + layer.39.0 165.22564383 20987.75704568 + ------------------------------------------------------------------------------------- + TOTAL 82.66888602 10501.50890105 + (elements=8,429,568) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50412 +BPFP 0.0478 bits/point +EBPFP 0.0478 equivalent bits/point +MSE 10501.508901 +---------------------- --------------------------------------------------------- +Time: 18.563s Load: 16.960s, Pack+Encode: 0.558s, Decode+Unpack: 1.045s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10501.5089 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04591713-painting_14.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04591713-painting_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07693725-sketch_15.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07693725-sketch_15.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,204B, BPFP=0.0402 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,264B, BPFP=0.0612 +⌛️ [2/4] FRONTEND: Frontend time: 0.598s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.045s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10569874 14.83989310 + layer.39.0 214.96065658 20549.38969874 + ------------------------------------------------------------------------------------- + TOTAL 107.53317766 10282.11479592 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 53468 +BPFP 0.0507 bits/point +EBPFP 0.0507 equivalent bits/point +MSE 10282.114796 +---------------------- -------------------------------------------------------- +Time: 1.714s Load: 0.071s, Pack+Encode: 0.598s, Decode+Unpack: 1.045s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10282.1148 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07693725-sketch_15.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07693725-sketch_15.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07695742-videogame_1.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07695742-videogame_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,616B, BPFP=0.0410 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,544B, BPFP=0.0580 +⌛️ [2/4] FRONTEND: Frontend time: 0.539s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.008s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12460778 15.17617510 + layer.39.0 438.29433916 20719.03206997 + ------------------------------------------------------------------------------------- + TOTAL 219.20947347 10367.10412254 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52160 +BPFP 0.0495 bits/point +EBPFP 0.0495 equivalent bits/point +MSE 10367.104123 +---------------------- -------------------------------------------------------- +Time: 1.617s Load: 0.071s, Pack+Encode: 0.539s, Decode+Unpack: 1.008s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10367.1041 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07695742-videogame_1.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07695742-videogame_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697313-deviantart_7.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697313-deviantart_7.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,380B, BPFP=0.0406 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 33,952B, BPFP=0.0644 +⌛️ [2/4] FRONTEND: Frontend time: 0.602s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.014s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09520741 14.71461788 + layer.39.0 14.69109212 20151.23615160 + ------------------------------------------------------------------------------------- + TOTAL 7.39314977 10082.97538474 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 55332 +BPFP 0.0525 bits/point +EBPFP 0.0525 equivalent bits/point +MSE 10082.975385 +---------------------- -------------------------------------------------------- +Time: 1.686s Load: 0.070s, Pack+Encode: 0.602s, Decode+Unpack: 1.014s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10082.9754 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697313-deviantart_7.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07697313-deviantart_7.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697537-deviantart_16.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697537-deviantart_16.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 23,036B, BPFP=0.0437 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,832B, BPFP=0.0604 +⌛️ [2/4] FRONTEND: Frontend time: 0.591s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.018s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09755328 14.65122483 + layer.39.0 90.32537658 19905.91642371 + ------------------------------------------------------------------------------------- + TOTAL 45.21146493 9960.28382427 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 54868 +BPFP 0.0521 bits/point +EBPFP 0.0521 equivalent bits/point +MSE 9960.283824 +---------------------- -------------------------------------------------------- +Time: 1.681s Load: 0.072s, Pack+Encode: 0.591s, Decode+Unpack: 1.018s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9960.2838 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697537-deviantart_16.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07697537-deviantart_16.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714571-deviantart_0.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714571-deviantart_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,400B, BPFP=0.0368 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,884B, BPFP=0.0624 +⌛️ [2/4] FRONTEND: Frontend time: 0.567s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.017s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09528512 14.66835691 + layer.39.0 45.81401467 21179.42079689 + ------------------------------------------------------------------------------------- + TOTAL 22.95464989 10597.04457690 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52284 +BPFP 0.0496 bits/point +EBPFP 0.0496 equivalent bits/point +MSE 10597.044577 +---------------------- -------------------------------------------------------- +Time: 1.636s Load: 0.052s, Pack+Encode: 0.567s, Decode+Unpack: 1.017s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10597.0446 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714571-deviantart_0.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07714571-deviantart_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714990-toy_14.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714990-toy_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 18,976B, BPFP=0.0360 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,956B, BPFP=0.0626 +⌛️ [2/4] FRONTEND: Frontend time: 0.589s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.048s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09793257 14.72513325 + layer.39.0 322.50334062 23110.34013605 + ------------------------------------------------------------------------------------- + TOTAL 161.30063660 11562.53263465 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51932 +BPFP 0.0493 bits/point +EBPFP 0.0493 equivalent bits/point +MSE 11562.532635 +---------------------- -------------------------------------------------------- +Time: 1.707s Load: 0.070s, Pack+Encode: 0.589s, Decode+Unpack: 1.048s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 11562.5326 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714990-toy_14.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07714990-toy_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07718472-cartoon_1.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07718472-cartoon_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 24,032B, BPFP=0.0456 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,084B, BPFP=0.0590 +⌛️ [2/4] FRONTEND: Frontend time: 0.588s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.053s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11235230 14.82402230 + layer.39.0 14.49942963 18164.01749271 + ------------------------------------------------------------------------------------- + TOTAL 7.30589096 9089.42075751 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 55116 +BPFP 0.0523 bits/point +EBPFP 0.0523 equivalent bits/point +MSE 9089.420758 +---------------------- -------------------------------------------------------- +Time: 1.702s Load: 0.062s, Pack+Encode: 0.588s, Decode+Unpack: 1.053s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9089.4208 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07718472-cartoon_1.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07718472-cartoon_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07742313-deviantart_8.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07742313-deviantart_8.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,588B, BPFP=0.0391 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,152B, BPFP=0.0610 +⌛️ [2/4] FRONTEND: Frontend time: 0.596s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.048s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09669835 14.66854672 + layer.39.0 8.77690150 18386.74635569 + ------------------------------------------------------------------------------------- + TOTAL 4.43679992 9200.70745120 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52740 +BPFP 0.0501 bits/point +EBPFP 0.0501 equivalent bits/point +MSE 9200.707451 +---------------------- -------------------------------------------------------- +Time: 1.704s Load: 0.060s, Pack+Encode: 0.596s, Decode+Unpack: 1.048s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9200.7075 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07742313-deviantart_8.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07742313-deviantart_8.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07749582-sticker_0.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07749582-sticker_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,848B, BPFP=0.0377 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 33,300B, BPFP=0.0632 +⌛️ [2/4] FRONTEND: Frontend time: 0.593s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.048s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09550123 14.78804133 + layer.39.0 34.64631545 22128.79689018 + ------------------------------------------------------------------------------------- + TOTAL 17.37090834 11071.79246576 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 53148 +BPFP 0.0504 bits/point +EBPFP 0.0504 equivalent bits/point +MSE 11071.792466 +---------------------- -------------------------------------------------------- +Time: 1.711s Load: 0.070s, Pack+Encode: 0.593s, Decode+Unpack: 1.048s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 11071.7925 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07749582-sticker_0.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07749582-sticker_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07753275-painting_2.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07753275-painting_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,412B, BPFP=0.0406 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 34,528B, BPFP=0.0655 +⌛️ [2/4] FRONTEND: Frontend time: 0.594s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.030s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10429548 15.36458903 + layer.39.0 540.43106171 25987.17395530 + ------------------------------------------------------------------------------------- + TOTAL 270.26767859 13001.26927216 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 55940 +BPFP 0.0531 bits/point +EBPFP 0.0531 equivalent bits/point +MSE 13001.269272 +---------------------- -------------------------------------------------------- +Time: 1.695s Load: 0.070s, Pack+Encode: 0.594s, Decode+Unpack: 1.030s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 13001.2693 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07753275-painting_2.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07753275-painting_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07768694-painting_12.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07768694-painting_12.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,832B, BPFP=0.0414 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 33,412B, BPFP=0.0634 +⌛️ [2/4] FRONTEND: Frontend time: 0.593s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.065s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09821300 15.13197924 + layer.39.0 635.68343052 24012.99125364 + ------------------------------------------------------------------------------------- + TOTAL 317.89082176 12014.06161644 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 55244 +BPFP 0.0524 bits/point +EBPFP 0.0524 equivalent bits/point +MSE 12014.061616 +---------------------- -------------------------------------------------------- +Time: 1.728s Load: 0.070s, Pack+Encode: 0.593s, Decode+Unpack: 1.065s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 12014.0616 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07768694-painting_12.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07768694-painting_12.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07920052-painting_1.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07920052-painting_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,120B, BPFP=0.0382 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,372B, BPFP=0.0595 +⌛️ [2/4] FRONTEND: Frontend time: 0.584s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.048s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09582097 14.75644303 + layer.39.0 9.59182155 21805.60738581 + ------------------------------------------------------------------------------------- + TOTAL 4.84382126 10910.18191442 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51492 +BPFP 0.0489 bits/point +EBPFP 0.0489 equivalent bits/point +MSE 10910.181914 +---------------------- -------------------------------------------------------- +Time: 1.691s Load: 0.059s, Pack+Encode: 0.584s, Decode+Unpack: 1.048s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10910.1819 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07920052-painting_1.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07920052-painting_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09472597-painting_15.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09472597-painting_15.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,844B, BPFP=0.0377 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,724B, BPFP=0.0621 +⌛️ [2/4] FRONTEND: Frontend time: 0.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.044s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09164813 14.75405050 + layer.39.0 9.11265014 19824.99514091 + ------------------------------------------------------------------------------------- + TOTAL 4.60214913 9919.87459571 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52568 +BPFP 0.0499 bits/point +EBPFP 0.0499 equivalent bits/point +MSE 9919.874596 +---------------------- -------------------------------------------------------- +Time: 1.695s Load: 0.070s, Pack+Encode: 0.581s, Decode+Unpack: 1.044s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9919.8746 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09472597-painting_15.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n09472597-painting_15.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09835506-videogame_3.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09835506-videogame_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,388B, BPFP=0.0387 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,872B, BPFP=0.0624 +⌛️ [2/4] FRONTEND: Frontend time: 0.593s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.055s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09585661 14.85834909 + layer.39.0 12.34450164 21380.75607386 + ------------------------------------------------------------------------------------- + TOTAL 6.22017912 10697.80721147 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 53260 +BPFP 0.0505 bits/point +EBPFP 0.0505 equivalent bits/point +MSE 10697.807211 +---------------------- -------------------------------------------------------- +Time: 1.718s Load: 0.070s, Pack+Encode: 0.593s, Decode+Unpack: 1.055s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10697.8072 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09835506-videogame_3.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n09835506-videogame_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n12267677-misc_105.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n12267677-misc_105.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,608B, BPFP=0.0372 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 29,452B, BPFP=0.0559 +⌛️ [2/4] FRONTEND: Frontend time: 0.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.032s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10166193 14.40233900 + layer.39.0 219.41089650 19113.60544218 + ------------------------------------------------------------------------------------- + TOTAL 109.75627921 9564.00389059 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 49060 +BPFP 0.0466 bits/point +EBPFP 0.0466 equivalent bits/point +MSE 9564.003891 +---------------------- -------------------------------------------------------- +Time: 1.683s Load: 0.071s, Pack+Encode: 0.581s, Decode+Unpack: 1.032s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9564.0039 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n12267677-misc_105.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n12267677-misc_105.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 0.0494 bits/point +Avg EBPFP 0.0494 equivalent bits/point +Avg MSE 10156.020100 +Avg Time 2.011s +------------------------ ---------------------------- diff --git a/lambda0.001/hyperprior-featurecoding-8bit-individual/cls_in1kval/dtufc_hyperprior-featurecoding_dinov3-total_individual.log b/lambda0.001/hyperprior-featurecoding-8bit-individual/cls_in1kval/dtufc_hyperprior-featurecoding_dinov3-total_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..57e68d6daa00572d6f1f2c7210eed79d952a756c --- /dev/null +++ b/lambda0.001/hyperprior-featurecoding-8bit-individual/cls_in1kval/dtufc_hyperprior-featurecoding_dinov3-total_individual.log @@ -0,0 +1,9344 @@ +Experiment: dtufc_hyperprior-featurecoding_dinov3-total_individual +Log file: output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/dtufc_hyperprior-featurecoding_dinov3-total_individual.log +DTUFCCodecConfig: + arch: hyperprior-featurecoding + handler: dinov3-total + checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.001_epochs600_lr0.0001_bs360_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.001_epochs600_lr0.0001_bs360_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 405 +Loaded hyperprior-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.9' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.19' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.29' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.39' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json +Loaded per-key mappings: model=dinov3-total + Keys: ['layer.9', 'layer.19', 'layer.29', 'layer.39'] +---------------- ------------------------------------------------------------------------------------------------------------------------------ +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +Checkpoint codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.001_epochs600_lr0.0001_bs360_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-DINOv3/imagenet1k-val +Output output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval +---------------- ------------------------------------------------------------------------------------------------------------------------------ +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02825657-ILSVRC2012_val_00001103.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02825657-ILSVRC2012_val_00001103.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.091s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,756B, BPFP=0.0394 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 28,892B, BPFP=0.0548 +⌛️ [2/4] FRONTEND: Frontend time: 0.793s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.070s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10264289 14.67250896 + layer.39.0 9.47367932 16668.18270165 + ------------------------------------------------------------------------------------- + TOTAL 4.78816110 8341.42760531 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 49648 +BPFP 0.0471 bits/point +EBPFP 0.0471 equivalent bits/point +MSE 8341.427605 +---------------------- -------------------------------------------------------- +Time: 1.953s Load: 0.091s, Pack+Encode: 0.793s, Decode+Unpack: 1.070s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8341.4276 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02825657-ILSVRC2012_val_00001103.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02825657-ILSVRC2012_val_00001103.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02834397-ILSVRC2012_val_00001252.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02834397-ILSVRC2012_val_00001252.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 23,296B, BPFP=0.0442 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,628B, BPFP=0.0600 +⌛️ [2/4] FRONTEND: Frontend time: 0.592s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.030s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14789204 15.66762330 + layer.39.0 415.43227648 21319.43245870 + ------------------------------------------------------------------------------------- + TOTAL 207.79008426 10667.55004100 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 54924 +BPFP 0.0521 bits/point +EBPFP 0.0521 equivalent bits/point +MSE 10667.550041 +---------------------- -------------------------------------------------------- +Time: 1.702s Load: 0.080s, Pack+Encode: 0.592s, Decode+Unpack: 1.030s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10667.5500 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02834397-ILSVRC2012_val_00001252.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02834397-ILSVRC2012_val_00001252.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02840245-ILSVRC2012_val_00003446.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02840245-ILSVRC2012_val_00003446.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,632B, BPFP=0.0392 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,888B, BPFP=0.0624 +⌛️ [2/4] FRONTEND: Frontend time: 0.529s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.979s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10761288 14.88585797 + layer.39.0 28.71820525 19638.51311953 + ------------------------------------------------------------------------------------- + TOTAL 14.41290906 9826.69948875 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 53520 +BPFP 0.0508 bits/point +EBPFP 0.0508 equivalent bits/point +MSE 9826.699489 +---------------------- -------------------------------------------------------- +Time: 1.560s Load: 0.052s, Pack+Encode: 0.529s, Decode+Unpack: 0.979s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9826.6995 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02840245-ILSVRC2012_val_00003446.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02840245-ILSVRC2012_val_00003446.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02843684-ILSVRC2012_val_00000514.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02843684-ILSVRC2012_val_00000514.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,148B, BPFP=0.0382 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,520B, BPFP=0.0579 +⌛️ [2/4] FRONTEND: Frontend time: 0.548s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.003s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11482661 14.78212407 + layer.39.0 84.54469600 19260.89407191 + ------------------------------------------------------------------------------------- + TOTAL 42.32976130 9637.83809799 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50668 +BPFP 0.0481 bits/point +EBPFP 0.0481 equivalent bits/point +MSE 9637.838098 +---------------------- -------------------------------------------------------- +Time: 1.602s Load: 0.051s, Pack+Encode: 0.548s, Decode+Unpack: 1.003s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9637.8381 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02843684-ILSVRC2012_val_00000514.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02843684-ILSVRC2012_val_00000514.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02859443-ILSVRC2012_val_00000193.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02859443-ILSVRC2012_val_00000193.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 22,124B, BPFP=0.0420 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 29,856B, BPFP=0.0567 +⌛️ [2/4] FRONTEND: Frontend time: 0.510s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.977s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11417333 14.84499609 + layer.39.0 9.67809406 15318.20019436 + ------------------------------------------------------------------------------------- + TOTAL 4.89613370 7666.52259523 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51980 +BPFP 0.0493 bits/point +EBPFP 0.0493 equivalent bits/point +MSE 7666.522595 +---------------------- -------------------------------------------------------- +Time: 1.538s Load: 0.051s, Pack+Encode: 0.510s, Decode+Unpack: 0.977s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 7666.5226 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02859443-ILSVRC2012_val_00000193.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02859443-ILSVRC2012_val_00000193.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02860847-ILSVRC2012_val_00000601.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02860847-ILSVRC2012_val_00000601.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,872B, BPFP=0.0415 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,856B, BPFP=0.0624 +⌛️ [2/4] FRONTEND: Frontend time: 0.546s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.992s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12653054 15.01740065 + layer.39.0 266.35249636 20029.94752187 + ------------------------------------------------------------------------------------- + TOTAL 133.23951345 10022.48246126 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 54728 +BPFP 0.0519 bits/point +EBPFP 0.0519 equivalent bits/point +MSE 10022.482461 +---------------------- -------------------------------------------------------- +Time: 1.607s Load: 0.069s, Pack+Encode: 0.546s, Decode+Unpack: 0.992s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10022.4825 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02860847-ILSVRC2012_val_00000601.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02860847-ILSVRC2012_val_00000601.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02865351-ILSVRC2012_val_00000763.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02865351-ILSVRC2012_val_00000763.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,608B, BPFP=0.0372 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,460B, BPFP=0.0616 +⌛️ [2/4] FRONTEND: Frontend time: 0.529s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.988s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09467571 14.57176453 + layer.39.0 15.47581086 20524.53449951 + ------------------------------------------------------------------------------------- + TOTAL 7.78524328 10269.55313202 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52068 +BPFP 0.0494 bits/point +EBPFP 0.0494 equivalent bits/point +MSE 10269.553132 +---------------------- -------------------------------------------------------- +Time: 1.588s Load: 0.071s, Pack+Encode: 0.529s, Decode+Unpack: 0.988s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10269.5531 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02865351-ILSVRC2012_val_00000763.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02865351-ILSVRC2012_val_00000763.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02869837-ILSVRC2012_val_00000906.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02869837-ILSVRC2012_val_00000906.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,088B, BPFP=0.0381 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,916B, BPFP=0.0587 +⌛️ [2/4] FRONTEND: Frontend time: 0.561s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.040s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09659988 14.95565514 + layer.39.0 16.39405483 20554.77551020 + ------------------------------------------------------------------------------------- + TOTAL 8.24532736 10284.86558267 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51004 +BPFP 0.0484 bits/point +EBPFP 0.0484 equivalent bits/point +MSE 10284.865583 +---------------------- -------------------------------------------------------- +Time: 1.651s Load: 0.051s, Pack+Encode: 0.561s, Decode+Unpack: 1.040s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10284.8656 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02869837-ILSVRC2012_val_00000906.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02869837-ILSVRC2012_val_00000906.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02870880-ILSVRC2012_val_00003274.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02870880-ILSVRC2012_val_00003274.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 22,016B, BPFP=0.0418 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,844B, BPFP=0.0623 +⌛️ [2/4] FRONTEND: Frontend time: 0.543s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.977s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10254154 15.19715079 + layer.39.0 9.36513093 19156.14771623 + ------------------------------------------------------------------------------------- + TOTAL 4.73383623 9585.67243351 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 54860 +BPFP 0.0521 bits/point +EBPFP 0.0521 equivalent bits/point +MSE 9585.672434 +---------------------- -------------------------------------------------------- +Time: 1.569s Load: 0.050s, Pack+Encode: 0.543s, Decode+Unpack: 0.977s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9585.6724 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02870880-ILSVRC2012_val_00003274.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02870880-ILSVRC2012_val_00003274.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02871525-ILSVRC2012_val_00000879.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02871525-ILSVRC2012_val_00000879.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,284B, BPFP=0.0404 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,188B, BPFP=0.0592 +⌛️ [2/4] FRONTEND: Frontend time: 0.597s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.031s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.17072899 15.32276672 + layer.39.0 20.29403547 21182.06802721 + ------------------------------------------------------------------------------------- + TOTAL 10.23238223 10598.69539696 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52472 +BPFP 0.0498 bits/point +EBPFP 0.0498 equivalent bits/point +MSE 10598.695397 +---------------------- -------------------------------------------------------- +Time: 1.678s Load: 0.050s, Pack+Encode: 0.597s, Decode+Unpack: 1.031s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10598.6954 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02871525-ILSVRC2012_val_00000879.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02871525-ILSVRC2012_val_00000879.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02877765-ILSVRC2012_val_00000634.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02877765-ILSVRC2012_val_00000634.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,580B, BPFP=0.0372 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,704B, BPFP=0.0621 +⌛️ [2/4] FRONTEND: Frontend time: 0.547s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.983s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10908128 14.74990510 + layer.39.0 364.97770894 22411.69484937 + ------------------------------------------------------------------------------------- + TOTAL 182.54339511 11213.22237723 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52284 +BPFP 0.0496 bits/point +EBPFP 0.0496 equivalent bits/point +MSE 11213.222377 +---------------------- -------------------------------------------------------- +Time: 1.581s Load: 0.051s, Pack+Encode: 0.547s, Decode+Unpack: 0.983s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 11213.2224 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02877765-ILSVRC2012_val_00000634.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02877765-ILSVRC2012_val_00000634.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02879718-ILSVRC2012_val_00001354.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02879718-ILSVRC2012_val_00001354.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,972B, BPFP=0.0379 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 29,900B, BPFP=0.0568 +⌛️ [2/4] FRONTEND: Frontend time: 0.592s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.034s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10948122 15.07089901 + layer.39.0 55.92460444 19853.24198251 + ------------------------------------------------------------------------------------- + TOTAL 28.01704283 9934.15644076 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 49872 +BPFP 0.0473 bits/point +EBPFP 0.0473 equivalent bits/point +MSE 9934.156441 +---------------------- -------------------------------------------------------- +Time: 1.695s Load: 0.070s, Pack+Encode: 0.592s, Decode+Unpack: 1.034s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9934.1564 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02879718-ILSVRC2012_val_00001354.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02879718-ILSVRC2012_val_00001354.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02883205-ILSVRC2012_val_00000126.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02883205-ILSVRC2012_val_00000126.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 18,408B, BPFP=0.0349 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 29,088B, BPFP=0.0552 +⌛️ [2/4] FRONTEND: Frontend time: 0.564s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.982s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.06711708 14.60910832 + layer.39.0 7.82069686 18393.43828960 + ------------------------------------------------------------------------------------- + TOTAL 7.94390697 9204.02369896 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 47496 +BPFP 0.0451 bits/point +EBPFP 0.0451 equivalent bits/point +MSE 9204.023699 +---------------------- -------------------------------------------------------- +Time: 1.595s Load: 0.050s, Pack+Encode: 0.564s, Decode+Unpack: 0.982s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9204.0237 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02883205-ILSVRC2012_val_00000126.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02883205-ILSVRC2012_val_00000126.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892201-ILSVRC2012_val_00001145.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892201-ILSVRC2012_val_00001145.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,868B, BPFP=0.0415 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,808B, BPFP=0.0604 +⌛️ [2/4] FRONTEND: Frontend time: 0.527s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.977s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11297333 15.16930215 + layer.39.0 15.09638643 20872.52866861 + ------------------------------------------------------------------------------------- + TOTAL 7.60467988 10443.84898538 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 53676 +BPFP 0.0509 bits/point +EBPFP 0.0509 equivalent bits/point +MSE 10443.848985 +---------------------- -------------------------------------------------------- +Time: 1.564s Load: 0.059s, Pack+Encode: 0.527s, Decode+Unpack: 0.977s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10443.8490 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892201-ILSVRC2012_val_00001145.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02892201-ILSVRC2012_val_00001145.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892767-ILSVRC2012_val_00000808.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892767-ILSVRC2012_val_00000808.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 18,404B, BPFP=0.0349 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,208B, BPFP=0.0592 +⌛️ [2/4] FRONTEND: Frontend time: 0.528s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.988s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09598007 14.70510185 + layer.39.0 31.15013059 19140.09718173 + ------------------------------------------------------------------------------------- + TOTAL 15.62305533 9577.40114179 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 49612 +BPFP 0.0471 bits/point +EBPFP 0.0471 equivalent bits/point +MSE 9577.401142 +---------------------- -------------------------------------------------------- +Time: 1.567s Load: 0.051s, Pack+Encode: 0.528s, Decode+Unpack: 0.988s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9577.4011 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892767-ILSVRC2012_val_00000808.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02892767-ILSVRC2012_val_00000808.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02895154-ILSVRC2012_val_00000080.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02895154-ILSVRC2012_val_00000080.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,524B, BPFP=0.0371 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,716B, BPFP=0.0621 +⌛️ [2/4] FRONTEND: Frontend time: 0.543s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.986s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09530723 14.80199792 + layer.39.0 971.40427600 23545.60932945 + ------------------------------------------------------------------------------------- + TOTAL 485.74979162 11780.20566368 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52240 +BPFP 0.0496 bits/point +EBPFP 0.0496 equivalent bits/point +MSE 11780.205664 +---------------------- -------------------------------------------------------- +Time: 1.581s Load: 0.052s, Pack+Encode: 0.543s, Decode+Unpack: 0.986s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 11780.2057 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02895154-ILSVRC2012_val_00000080.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02895154-ILSVRC2012_val_00000080.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02906734-ILSVRC2012_val_00002937.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02906734-ILSVRC2012_val_00002937.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,200B, BPFP=0.0402 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 33,740B, BPFP=0.0640 +⌛️ [2/4] FRONTEND: Frontend time: 0.557s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.045s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09767962 14.92685936 + layer.39.0 32.09536716 20075.10981535 + ------------------------------------------------------------------------------------- + TOTAL 16.09652339 10045.01833736 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 54940 +BPFP 0.0521 bits/point +EBPFP 0.0521 equivalent bits/point +MSE 10045.018337 +---------------------- -------------------------------------------------------- +Time: 1.652s Load: 0.050s, Pack+Encode: 0.557s, Decode+Unpack: 1.045s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10045.0183 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02906734-ILSVRC2012_val_00002937.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02906734-ILSVRC2012_val_00002937.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02910353-ILSVRC2012_val_00000558.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02910353-ILSVRC2012_val_00000558.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.081s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 22,472B, BPFP=0.0427 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 33,580B, BPFP=0.0637 +⌛️ [2/4] FRONTEND: Frontend time: 0.585s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.055s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11017090 14.70932223 + layer.39.0 483.40066205 21728.81243926 + ------------------------------------------------------------------------------------- + TOTAL 241.75541648 10871.76088075 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 56052 +BPFP 0.0532 bits/point +EBPFP 0.0532 equivalent bits/point +MSE 10871.760881 +---------------------- -------------------------------------------------------- +Time: 1.722s Load: 0.081s, Pack+Encode: 0.585s, Decode+Unpack: 1.055s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10871.7609 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02910353-ILSVRC2012_val_00000558.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02910353-ILSVRC2012_val_00000558.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02916936-ILSVRC2012_val_00000366.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02916936-ILSVRC2012_val_00000366.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,544B, BPFP=0.0390 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,500B, BPFP=0.0617 +⌛️ [2/4] FRONTEND: Frontend time: 0.515s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.982s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10657579 14.51660346 + layer.39.0 435.18944363 20994.44897959 + ------------------------------------------------------------------------------------- + TOTAL 217.64800971 10504.48279153 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 53044 +BPFP 0.0503 bits/point +EBPFP 0.0503 equivalent bits/point +MSE 10504.482792 +---------------------- -------------------------------------------------------- +Time: 1.547s Load: 0.050s, Pack+Encode: 0.515s, Decode+Unpack: 0.982s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10504.4828 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02916936-ILSVRC2012_val_00000366.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02916936-ILSVRC2012_val_00000366.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02917067-ILSVRC2012_val_00000562.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02917067-ILSVRC2012_val_00000562.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,236B, BPFP=0.0403 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 35,564B, BPFP=0.0675 +⌛️ [2/4] FRONTEND: Frontend time: 0.571s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.986s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10760244 15.01422517 + layer.39.0 37.55795979 23331.61516035 + ------------------------------------------------------------------------------------- + TOTAL 18.83278111 11673.31469276 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 56800 +BPFP 0.0539 bits/point +EBPFP 0.0539 equivalent bits/point +MSE 11673.314693 +---------------------- -------------------------------------------------------- +Time: 1.627s Load: 0.071s, Pack+Encode: 0.571s, Decode+Unpack: 0.986s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 11673.3147 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02917067-ILSVRC2012_val_00000562.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02917067-ILSVRC2012_val_00000562.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02930766-ILSVRC2012_val_00000056.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02930766-ILSVRC2012_val_00000056.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.079s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,536B, BPFP=0.0390 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 33,852B, BPFP=0.0643 +⌛️ [2/4] FRONTEND: Frontend time: 0.595s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.051s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10591127 14.84950308 + layer.39.0 18.32421875 18401.89698737 + ------------------------------------------------------------------------------------- + TOTAL 9.21506501 9208.37324522 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 54388 +BPFP 0.0516 bits/point +EBPFP 0.0516 equivalent bits/point +MSE 9208.373245 +---------------------- -------------------------------------------------------- +Time: 1.724s Load: 0.079s, Pack+Encode: 0.595s, Decode+Unpack: 1.051s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9208.3732 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02930766-ILSVRC2012_val_00000056.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02930766-ILSVRC2012_val_00000056.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02939185-ILSVRC2012_val_00000302.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02939185-ILSVRC2012_val_00000302.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,652B, BPFP=0.0373 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,628B, BPFP=0.0619 +⌛️ [2/4] FRONTEND: Frontend time: 0.534s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.993s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09694758 14.81842486 + layer.39.0 25.52453269 20830.48590865 + ------------------------------------------------------------------------------------- + TOTAL 12.81074014 10422.65216675 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52280 +BPFP 0.0496 bits/point +EBPFP 0.0496 equivalent bits/point +MSE 10422.652167 +---------------------- -------------------------------------------------------- +Time: 1.588s Load: 0.060s, Pack+Encode: 0.534s, Decode+Unpack: 0.993s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10422.6522 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02939185-ILSVRC2012_val_00000302.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02939185-ILSVRC2012_val_00000302.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02950826-ILSVRC2012_val_00000392.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02950826-ILSVRC2012_val_00000392.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,792B, BPFP=0.0395 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,596B, BPFP=0.0619 +⌛️ [2/4] FRONTEND: Frontend time: 0.528s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.053s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10873010 14.92862268 + layer.39.0 707.96944849 21169.50826045 + ------------------------------------------------------------------------------------- + TOTAL 354.03908930 10592.21844156 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 53388 +BPFP 0.0507 bits/point +EBPFP 0.0507 equivalent bits/point +MSE 10592.218442 +---------------------- -------------------------------------------------------- +Time: 1.631s Load: 0.051s, Pack+Encode: 0.528s, Decode+Unpack: 1.053s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10592.2184 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02950826-ILSVRC2012_val_00000392.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02950826-ILSVRC2012_val_00000392.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951358-ILSVRC2012_val_00000347.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951358-ILSVRC2012_val_00000347.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,356B, BPFP=0.0405 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,092B, BPFP=0.0609 +⌛️ [2/4] FRONTEND: Frontend time: 0.558s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.036s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12200860 14.89745999 + layer.39.0 237.66299198 19548.85908649 + ------------------------------------------------------------------------------------- + TOTAL 118.89250029 9781.87827324 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 53448 +BPFP 0.0507 bits/point +EBPFP 0.0507 equivalent bits/point +MSE 9781.878273 +---------------------- -------------------------------------------------------- +Time: 1.645s Load: 0.051s, Pack+Encode: 0.558s, Decode+Unpack: 1.036s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9781.8783 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951358-ILSVRC2012_val_00000347.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02951358-ILSVRC2012_val_00000347.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951585-ILSVRC2012_val_00000101.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951585-ILSVRC2012_val_00000101.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,552B, BPFP=0.0390 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 33,976B, BPFP=0.0645 +⌛️ [2/4] FRONTEND: Frontend time: 0.596s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.982s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.07385432 14.45303010 + layer.39.0 181.90962099 19338.92711370 + ------------------------------------------------------------------------------------- + TOTAL 94.99173765 9676.69007190 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 54528 +BPFP 0.0517 bits/point +EBPFP 0.0517 equivalent bits/point +MSE 9676.690072 +---------------------- -------------------------------------------------------- +Time: 1.629s Load: 0.051s, Pack+Encode: 0.596s, Decode+Unpack: 0.982s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9676.6901 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951585-ILSVRC2012_val_00000101.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02951585-ILSVRC2012_val_00000101.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02963159-ILSVRC2012_val_00000061.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02963159-ILSVRC2012_val_00000061.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,252B, BPFP=0.0384 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 29,684B, BPFP=0.0563 +⌛️ [2/4] FRONTEND: Frontend time: 0.594s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.038s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11232698 14.87640743 + layer.39.0 24.77479842 19378.35179786 + ------------------------------------------------------------------------------------- + TOTAL 12.44356270 9696.61410264 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 49936 +BPFP 0.0474 bits/point +EBPFP 0.0474 equivalent bits/point +MSE 9696.614103 +---------------------- -------------------------------------------------------- +Time: 1.702s Load: 0.070s, Pack+Encode: 0.594s, Decode+Unpack: 1.038s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9696.6141 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02963159-ILSVRC2012_val_00000061.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02963159-ILSVRC2012_val_00000061.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02965783-ILSVRC2012_val_00000213.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02965783-ILSVRC2012_val_00000213.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,240B, BPFP=0.0365 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,712B, BPFP=0.0602 +⌛️ [2/4] FRONTEND: Frontend time: 0.529s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.981s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09516161 14.58798743 + layer.39.0 223.32294704 21322.09523810 + ------------------------------------------------------------------------------------- + TOTAL 111.70905432 10668.34161276 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50952 +BPFP 0.0484 bits/point +EBPFP 0.0484 equivalent bits/point +MSE 10668.341613 +---------------------- -------------------------------------------------------- +Time: 1.570s Load: 0.060s, Pack+Encode: 0.529s, Decode+Unpack: 0.981s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10668.3416 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02965783-ILSVRC2012_val_00000213.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02965783-ILSVRC2012_val_00000213.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966193-ILSVRC2012_val_00000074.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966193-ILSVRC2012_val_00000074.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,788B, BPFP=0.0376 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 33,204B, BPFP=0.0630 +⌛️ [2/4] FRONTEND: Frontend time: 0.510s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.961s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12190965 15.24931100 + layer.39.0 378.75431244 24536.87074830 + ------------------------------------------------------------------------------------- + TOTAL 189.43811104 12276.06002965 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52992 +BPFP 0.0503 bits/point +EBPFP 0.0503 equivalent bits/point +MSE 12276.060030 +---------------------- -------------------------------------------------------- +Time: 1.523s Load: 0.052s, Pack+Encode: 0.510s, Decode+Unpack: 0.961s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 12276.0600 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966193-ILSVRC2012_val_00000074.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02966193-ILSVRC2012_val_00000074.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966687-ILSVRC2012_val_00001041.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966687-ILSVRC2012_val_00001041.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,168B, BPFP=0.0383 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,412B, BPFP=0.0577 +⌛️ [2/4] FRONTEND: Frontend time: 0.505s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.972s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12487827 14.81806612 + layer.39.0 254.07423773 21365.66763848 + ------------------------------------------------------------------------------------- + TOTAL 127.09955800 10690.24285230 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50580 +BPFP 0.0480 bits/point +EBPFP 0.0480 equivalent bits/point +MSE 10690.242852 +---------------------- -------------------------------------------------------- +Time: 1.528s Load: 0.051s, Pack+Encode: 0.505s, Decode+Unpack: 0.972s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10690.2429 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966687-ILSVRC2012_val_00001041.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02966687-ILSVRC2012_val_00001041.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02971356-ILSVRC2012_val_00000019.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02971356-ILSVRC2012_val_00000019.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,156B, BPFP=0.0383 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,940B, BPFP=0.0587 +⌛️ [2/4] FRONTEND: Frontend time: 0.533s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.982s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09754465 14.62818688 + layer.39.0 24.51746044 17705.63265306 + ------------------------------------------------------------------------------------- + TOTAL 12.30750255 8860.13041997 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51096 +BPFP 0.0485 bits/point +EBPFP 0.0485 equivalent bits/point +MSE 8860.130420 +---------------------- -------------------------------------------------------- +Time: 1.586s Load: 0.072s, Pack+Encode: 0.533s, Decode+Unpack: 0.982s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8860.1304 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02971356-ILSVRC2012_val_00000019.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02971356-ILSVRC2012_val_00000019.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02978881-ILSVRC2012_val_00000353.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02978881-ILSVRC2012_val_00000353.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,728B, BPFP=0.0393 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,076B, BPFP=0.0609 +⌛️ [2/4] FRONTEND: Frontend time: 0.605s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.045s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09975241 14.52517804 + layer.39.0 226.62124939 20856.16520894 + ------------------------------------------------------------------------------------- + TOTAL 113.36050090 10435.34519349 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52804 +BPFP 0.0501 bits/point +EBPFP 0.0501 equivalent bits/point +MSE 10435.345193 +---------------------- -------------------------------------------------------- +Time: 1.720s Load: 0.070s, Pack+Encode: 0.605s, Decode+Unpack: 1.045s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10435.3452 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02978881-ILSVRC2012_val_00000353.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02978881-ILSVRC2012_val_00000353.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02980441-ILSVRC2012_val_00000122.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02980441-ILSVRC2012_val_00000122.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 18,900B, BPFP=0.0359 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 28,344B, BPFP=0.0538 +⌛️ [2/4] FRONTEND: Frontend time: 0.542s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.967s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10186533 14.73708546 + layer.39.0 8.25151846 17199.67735666 + ------------------------------------------------------------------------------------- + TOTAL 4.17669190 8607.20722106 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 47244 +BPFP 0.0448 bits/point +EBPFP 0.0448 equivalent bits/point +MSE 8607.207221 +---------------------- -------------------------------------------------------- +Time: 1.559s Load: 0.050s, Pack+Encode: 0.542s, Decode+Unpack: 0.967s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8607.2072 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02980441-ILSVRC2012_val_00000122.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02980441-ILSVRC2012_val_00000122.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02988304-ILSVRC2012_val_00003491.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02988304-ILSVRC2012_val_00003491.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 22,248B, BPFP=0.0422 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 34,616B, BPFP=0.0657 +⌛️ [2/4] FRONTEND: Frontend time: 0.504s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.968s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10176498 14.62353278 + layer.39.0 516.16180758 22823.16812439 + ------------------------------------------------------------------------------------- + TOTAL 258.13178628 11418.89582859 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 56864 +BPFP 0.0540 bits/point +EBPFP 0.0540 equivalent bits/point +MSE 11418.895829 +---------------------- -------------------------------------------------------- +Time: 1.523s Load: 0.052s, Pack+Encode: 0.504s, Decode+Unpack: 0.968s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 11418.8958 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02988304-ILSVRC2012_val_00003491.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02988304-ILSVRC2012_val_00003491.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992211-ILSVRC2012_val_00000108.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992211-ILSVRC2012_val_00000108.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,088B, BPFP=0.0400 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 36,072B, BPFP=0.0685 +⌛️ [2/4] FRONTEND: Frontend time: 0.541s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.012s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10107529 15.00947807 + layer.39.0 89.13089923 24944.45869776 + ------------------------------------------------------------------------------------- + TOTAL 44.61598726 12479.73408792 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 57160 +BPFP 0.0542 bits/point +EBPFP 0.0542 equivalent bits/point +MSE 12479.734088 +---------------------- -------------------------------------------------------- +Time: 1.605s Load: 0.052s, Pack+Encode: 0.541s, Decode+Unpack: 1.012s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 12479.7341 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992211-ILSVRC2012_val_00000108.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02992211-ILSVRC2012_val_00000108.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992529-ILSVRC2012_val_00000089.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992529-ILSVRC2012_val_00000089.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,168B, BPFP=0.0364 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 33,028B, BPFP=0.0627 +⌛️ [2/4] FRONTEND: Frontend time: 0.526s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.992s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11197385 14.79396999 + layer.39.0 964.25631681 22789.96695821 + ------------------------------------------------------------------------------------- + TOTAL 482.18414533 11402.38046410 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52196 +BPFP 0.0495 bits/point +EBPFP 0.0495 equivalent bits/point +MSE 11402.380464 +---------------------- -------------------------------------------------------- +Time: 1.588s Load: 0.070s, Pack+Encode: 0.526s, Decode+Unpack: 0.992s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 11402.3805 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992529-ILSVRC2012_val_00000089.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02992529-ILSVRC2012_val_00000089.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02999410-ILSVRC2012_val_00000376.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02999410-ILSVRC2012_val_00000376.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,444B, BPFP=0.0369 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 28,632B, BPFP=0.0543 +⌛️ [2/4] FRONTEND: Frontend time: 0.576s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.037s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10398186 14.93434729 + layer.39.0 145.78410471 17645.68124393 + ------------------------------------------------------------------------------------- + TOTAL 72.94404329 8830.30779561 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 48076 +BPFP 0.0456 bits/point +EBPFP 0.0456 equivalent bits/point +MSE 8830.307796 +---------------------- -------------------------------------------------------- +Time: 1.683s Load: 0.071s, Pack+Encode: 0.576s, Decode+Unpack: 1.037s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8830.3078 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02999410-ILSVRC2012_val_00000376.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02999410-ILSVRC2012_val_00000376.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000134-ILSVRC2012_val_00001094.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000134-ILSVRC2012_val_00001094.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,276B, BPFP=0.0366 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,564B, BPFP=0.0599 +⌛️ [2/4] FRONTEND: Frontend time: 0.528s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.967s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09696872 14.77678382 + layer.39.0 22.81329530 21005.46161322 + ------------------------------------------------------------------------------------- + TOTAL 11.45513201 10510.11919852 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50840 +BPFP 0.0482 bits/point +EBPFP 0.0482 equivalent bits/point +MSE 10510.119199 +---------------------- -------------------------------------------------------- +Time: 1.547s Load: 0.052s, Pack+Encode: 0.528s, Decode+Unpack: 0.967s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10510.1192 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000134-ILSVRC2012_val_00001094.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03000134-ILSVRC2012_val_00001094.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000247-ILSVRC2012_val_00002280.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000247-ILSVRC2012_val_00002280.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 24,752B, BPFP=0.0470 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 33,252B, BPFP=0.0631 +⌛️ [2/4] FRONTEND: Frontend time: 0.557s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.041s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.29135144 16.07489067 + layer.39.0 428.26293732 22864.94071914 + ------------------------------------------------------------------------------------- + TOTAL 214.27714438 11440.50780491 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 58004 +BPFP 0.0550 bits/point +EBPFP 0.0550 equivalent bits/point +MSE 11440.507805 +---------------------- -------------------------------------------------------- +Time: 1.648s Load: 0.050s, Pack+Encode: 0.557s, Decode+Unpack: 1.041s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 11440.5078 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000247-ILSVRC2012_val_00002280.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03000247-ILSVRC2012_val_00002280.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000684-ILSVRC2012_val_00000537.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000684-ILSVRC2012_val_00000537.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,388B, BPFP=0.0387 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,400B, BPFP=0.0615 +⌛️ [2/4] FRONTEND: Frontend time: 0.582s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.049s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13150742 15.36613122 + layer.39.0 55.24585459 22168.42759961 + ------------------------------------------------------------------------------------- + TOTAL 27.68868101 11091.89686541 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52788 +BPFP 0.0501 bits/point +EBPFP 0.0501 equivalent bits/point +MSE 11091.896865 +---------------------- -------------------------------------------------------- +Time: 1.700s Load: 0.070s, Pack+Encode: 0.582s, Decode+Unpack: 1.049s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 11091.8969 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000684-ILSVRC2012_val_00000537.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03000684-ILSVRC2012_val_00000537.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03014705-ILSVRC2012_val_00001168.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03014705-ILSVRC2012_val_00001168.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,984B, BPFP=0.0398 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,704B, BPFP=0.0621 +⌛️ [2/4] FRONTEND: Frontend time: 0.532s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.978s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09787338 14.62841370 + layer.39.0 322.89622813 20616.15937804 + ------------------------------------------------------------------------------------- + TOTAL 161.49705076 10315.39389587 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 53688 +BPFP 0.0510 bits/point +EBPFP 0.0510 equivalent bits/point +MSE 10315.393896 +---------------------- -------------------------------------------------------- +Time: 1.559s Load: 0.050s, Pack+Encode: 0.532s, Decode+Unpack: 0.978s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10315.3939 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03014705-ILSVRC2012_val_00001168.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03014705-ILSVRC2012_val_00001168.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03017168-ILSVRC2012_val_00001601.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03017168-ILSVRC2012_val_00001601.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,028B, BPFP=0.0399 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 33,476B, BPFP=0.0635 +⌛️ [2/4] FRONTEND: Frontend time: 0.550s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.973s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10213913 14.64764885 + layer.39.0 475.40952988 23049.48882410 + ------------------------------------------------------------------------------------- + TOTAL 237.75583451 11532.06823647 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 54504 +BPFP 0.0517 bits/point +EBPFP 0.0517 equivalent bits/point +MSE 11532.068236 +---------------------- -------------------------------------------------------- +Time: 1.574s Load: 0.051s, Pack+Encode: 0.550s, Decode+Unpack: 0.973s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 11532.0682 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03017168-ILSVRC2012_val_00001601.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03017168-ILSVRC2012_val_00001601.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03018349-ILSVRC2012_val_00000346.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03018349-ILSVRC2012_val_00000346.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,060B, BPFP=0.0362 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,880B, BPFP=0.0586 +⌛️ [2/4] FRONTEND: Frontend time: 0.523s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.969s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09959339 14.64656599 + layer.39.0 56.59841169 22112.10106900 + ------------------------------------------------------------------------------------- + TOTAL 28.34900254 11063.37381750 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 49940 +BPFP 0.0474 bits/point +EBPFP 0.0474 equivalent bits/point +MSE 11063.373817 +---------------------- -------------------------------------------------------- +Time: 1.553s Load: 0.061s, Pack+Encode: 0.523s, Decode+Unpack: 0.969s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 11063.3738 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03018349-ILSVRC2012_val_00000346.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03018349-ILSVRC2012_val_00000346.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03026506-ILSVRC2012_val_00001908.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03026506-ILSVRC2012_val_00001908.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,256B, BPFP=0.0365 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,536B, BPFP=0.0599 +⌛️ [2/4] FRONTEND: Frontend time: 0.502s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.993s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10977067 14.77180420 + layer.39.0 668.54063411 24202.80855199 + ------------------------------------------------------------------------------------- + TOTAL 334.32520239 12108.79017810 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50792 +BPFP 0.0482 bits/point +EBPFP 0.0482 equivalent bits/point +MSE 12108.790178 +---------------------- -------------------------------------------------------- +Time: 1.548s Load: 0.052s, Pack+Encode: 0.502s, Decode+Unpack: 0.993s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 12108.7902 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03026506-ILSVRC2012_val_00001908.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03026506-ILSVRC2012_val_00001908.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03028079-ILSVRC2012_val_00003351.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03028079-ILSVRC2012_val_00003351.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,924B, BPFP=0.0378 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 28,892B, BPFP=0.0548 +⌛️ [2/4] FRONTEND: Frontend time: 0.537s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.982s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10934904 14.90443259 + layer.39.0 15.31112010 17558.88824101 + ------------------------------------------------------------------------------------- + TOTAL 7.71023457 8786.89633680 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 48816 +BPFP 0.0463 bits/point +EBPFP 0.0463 equivalent bits/point +MSE 8786.896337 +---------------------- -------------------------------------------------------- +Time: 1.571s Load: 0.052s, Pack+Encode: 0.537s, Decode+Unpack: 0.982s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8786.8963 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03028079-ILSVRC2012_val_00003351.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03028079-ILSVRC2012_val_00003351.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03032252-ILSVRC2012_val_00000086.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03032252-ILSVRC2012_val_00000086.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,888B, BPFP=0.0396 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,364B, BPFP=0.0614 +⌛️ [2/4] FRONTEND: Frontend time: 0.530s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.003s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13507480 15.36676613 + layer.39.0 103.55165816 21277.53935860 + ------------------------------------------------------------------------------------- + TOTAL 51.84336648 10646.45306236 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 53252 +BPFP 0.0505 bits/point +EBPFP 0.0505 equivalent bits/point +MSE 10646.453062 +---------------------- -------------------------------------------------------- +Time: 1.594s Load: 0.061s, Pack+Encode: 0.530s, Decode+Unpack: 1.003s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10646.4531 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03032252-ILSVRC2012_val_00000086.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03032252-ILSVRC2012_val_00000086.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03041632-ILSVRC2012_val_00000564.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03041632-ILSVRC2012_val_00000564.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 23,056B, BPFP=0.0438 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 33,264B, BPFP=0.0631 +⌛️ [2/4] FRONTEND: Frontend time: 0.580s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.051s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10123130 14.79194758 + layer.39.0 371.34277818 20310.58114674 + ------------------------------------------------------------------------------------- + TOTAL 185.72200474 10162.68654716 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 56320 +BPFP 0.0534 bits/point +EBPFP 0.0534 equivalent bits/point +MSE 10162.686547 +---------------------- -------------------------------------------------------- +Time: 1.701s Load: 0.070s, Pack+Encode: 0.580s, Decode+Unpack: 1.051s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10162.6865 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03041632-ILSVRC2012_val_00000564.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03041632-ILSVRC2012_val_00000564.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03042490-ILSVRC2012_val_00001426.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03042490-ILSVRC2012_val_00001426.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.082s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,564B, BPFP=0.0390 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 34,128B, BPFP=0.0648 +⌛️ [2/4] FRONTEND: Frontend time: 0.585s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.041s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10706725 14.87893947 + layer.39.0 141.71039845 23368.39650146 + ------------------------------------------------------------------------------------- + TOTAL 70.90873285 11691.63772046 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 54692 +BPFP 0.0519 bits/point +EBPFP 0.0519 equivalent bits/point +MSE 11691.637720 +---------------------- -------------------------------------------------------- +Time: 1.707s Load: 0.082s, Pack+Encode: 0.585s, Decode+Unpack: 1.041s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 11691.6377 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03042490-ILSVRC2012_val_00001426.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03042490-ILSVRC2012_val_00001426.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03047690-ILSVRC2012_val_00001500.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03047690-ILSVRC2012_val_00001500.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 18,952B, BPFP=0.0360 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,504B, BPFP=0.0579 +⌛️ [2/4] FRONTEND: Frontend time: 0.572s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.049s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09570478 14.54222945 + layer.39.0 226.76483540 22983.69484937 + ------------------------------------------------------------------------------------- + TOTAL 113.43027009 11499.11853941 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 49456 +BPFP 0.0469 bits/point +EBPFP 0.0469 equivalent bits/point +MSE 11499.118539 +---------------------- -------------------------------------------------------- +Time: 1.691s Load: 0.070s, Pack+Encode: 0.572s, Decode+Unpack: 1.049s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 11499.1185 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03047690-ILSVRC2012_val_00001500.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03047690-ILSVRC2012_val_00001500.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03062245-ILSVRC2012_val_00000344.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03062245-ILSVRC2012_val_00000344.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,012B, BPFP=0.0361 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,088B, BPFP=0.0571 +⌛️ [2/4] FRONTEND: Frontend time: 0.523s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.975s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09619164 14.59397682 + layer.39.0 46.71096787 20205.82312925 + ------------------------------------------------------------------------------------- + TOTAL 23.40357976 10110.20855304 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 49100 +BPFP 0.0466 bits/point +EBPFP 0.0466 equivalent bits/point +MSE 10110.208553 +---------------------- -------------------------------------------------------- +Time: 1.570s Load: 0.072s, Pack+Encode: 0.523s, Decode+Unpack: 0.975s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10110.2086 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03062245-ILSVRC2012_val_00000344.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03062245-ILSVRC2012_val_00000344.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063599-ILSVRC2012_val_00000164.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063599-ILSVRC2012_val_00000164.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,456B, BPFP=0.0369 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,260B, BPFP=0.0574 +⌛️ [2/4] FRONTEND: Frontend time: 0.518s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.997s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10111790 14.66140614 + layer.39.0 9.80528160 19588.89018465 + ------------------------------------------------------------------------------------- + TOTAL 4.95319975 9801.77579539 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 49716 +BPFP 0.0472 bits/point +EBPFP 0.0472 equivalent bits/point +MSE 9801.775795 +---------------------- -------------------------------------------------------- +Time: 1.566s Load: 0.052s, Pack+Encode: 0.518s, Decode+Unpack: 0.997s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9801.7758 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063599-ILSVRC2012_val_00000164.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03063599-ILSVRC2012_val_00000164.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063689-ILSVRC2012_val_00001940.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063689-ILSVRC2012_val_00001940.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,700B, BPFP=0.0374 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 33,088B, BPFP=0.0628 +⌛️ [2/4] FRONTEND: Frontend time: 0.512s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.969s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09645106 14.66282116 + layer.39.0 18.48014797 24377.43245870 + ------------------------------------------------------------------------------------- + TOTAL 9.28829952 12196.04763993 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52788 +BPFP 0.0501 bits/point +EBPFP 0.0501 equivalent bits/point +MSE 12196.047640 +---------------------- -------------------------------------------------------- +Time: 1.531s Load: 0.051s, Pack+Encode: 0.512s, Decode+Unpack: 0.969s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 12196.0476 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063689-ILSVRC2012_val_00001940.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03063689-ILSVRC2012_val_00001940.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03065424-ILSVRC2012_val_00000915.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03065424-ILSVRC2012_val_00000915.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,904B, BPFP=0.0397 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 34,520B, BPFP=0.0655 +⌛️ [2/4] FRONTEND: Frontend time: 0.542s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.990s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12384982 15.36383454 + layer.39.0 2154.15986395 28653.77259475 + ------------------------------------------------------------------------------------- + TOTAL 1077.14185688 14334.56821465 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 55424 +BPFP 0.0526 bits/point +EBPFP 0.0526 equivalent bits/point +MSE 14334.568215 +---------------------- -------------------------------------------------------- +Time: 1.582s Load: 0.050s, Pack+Encode: 0.542s, Decode+Unpack: 0.990s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 14334.5682 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03065424-ILSVRC2012_val_00000915.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03065424-ILSVRC2012_val_00000915.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03075370-ILSVRC2012_val_00004971.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03075370-ILSVRC2012_val_00004971.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,628B, BPFP=0.0373 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,716B, BPFP=0.0583 +⌛️ [2/4] FRONTEND: Frontend time: 0.593s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.038s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10672879 14.93386328 + layer.39.0 301.29020894 21447.59378037 + ------------------------------------------------------------------------------------- + TOTAL 150.69846886 10731.26382182 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50344 +BPFP 0.0478 bits/point +EBPFP 0.0478 equivalent bits/point +MSE 10731.263822 +---------------------- -------------------------------------------------------- +Time: 1.711s Load: 0.080s, Pack+Encode: 0.593s, Decode+Unpack: 1.038s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10731.2638 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03075370-ILSVRC2012_val_00004971.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03075370-ILSVRC2012_val_00004971.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03089624-ILSVRC2012_val_00001190.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03089624-ILSVRC2012_val_00001190.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,096B, BPFP=0.0381 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,728B, BPFP=0.0602 +⌛️ [2/4] FRONTEND: Frontend time: 0.557s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.996s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10385029 14.78123861 + layer.39.0 606.38896987 21501.47133139 + ------------------------------------------------------------------------------------- + TOTAL 303.24641008 10758.12628500 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51824 +BPFP 0.0492 bits/point +EBPFP 0.0492 equivalent bits/point +MSE 10758.126285 +---------------------- -------------------------------------------------------- +Time: 1.602s Load: 0.050s, Pack+Encode: 0.557s, Decode+Unpack: 0.996s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10758.1263 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03089624-ILSVRC2012_val_00001190.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03089624-ILSVRC2012_val_00001190.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03095699-ILSVRC2012_val_00000403.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03095699-ILSVRC2012_val_00000403.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,308B, BPFP=0.0404 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 35,056B, BPFP=0.0665 +⌛️ [2/4] FRONTEND: Frontend time: 0.600s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.011s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12139760 15.25279208 + layer.39.0 62.59250486 22516.95626822 + ------------------------------------------------------------------------------------- + TOTAL 31.35695123 11266.10453015 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 56364 +BPFP 0.0535 bits/point +EBPFP 0.0535 equivalent bits/point +MSE 11266.104530 +---------------------- -------------------------------------------------------- +Time: 1.661s Load: 0.050s, Pack+Encode: 0.600s, Decode+Unpack: 1.011s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 11266.1045 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03095699-ILSVRC2012_val_00000403.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03095699-ILSVRC2012_val_00000403.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03100240-ILSVRC2012_val_00001201.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03100240-ILSVRC2012_val_00001201.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,132B, BPFP=0.0401 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,372B, BPFP=0.0595 +⌛️ [2/4] FRONTEND: Frontend time: 0.547s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.976s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10258218 14.89327187 + layer.39.0 42.98202138 16974.51506317 + ------------------------------------------------------------------------------------- + TOTAL 21.54230178 8494.70416752 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52504 +BPFP 0.0498 bits/point +EBPFP 0.0498 equivalent bits/point +MSE 8494.704168 +---------------------- -------------------------------------------------------- +Time: 1.573s Load: 0.050s, Pack+Encode: 0.547s, Decode+Unpack: 0.976s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8494.7042 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03100240-ILSVRC2012_val_00001201.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03100240-ILSVRC2012_val_00001201.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03109150-ILSVRC2012_val_00000678.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03109150-ILSVRC2012_val_00000678.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,324B, BPFP=0.0386 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 33,400B, BPFP=0.0634 +⌛️ [2/4] FRONTEND: Frontend time: 0.554s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.971s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09720685 14.80890693 + layer.39.0 496.21158285 21709.51214772 + ------------------------------------------------------------------------------------- + TOTAL 248.15439485 10862.16052732 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 53724 +BPFP 0.0510 bits/point +EBPFP 0.0510 equivalent bits/point +MSE 10862.160527 +---------------------- -------------------------------------------------------- +Time: 1.585s Load: 0.061s, Pack+Encode: 0.554s, Decode+Unpack: 0.971s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10862.1605 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03109150-ILSVRC2012_val_00000678.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03109150-ILSVRC2012_val_00000678.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03110669-ILSVRC2012_val_00002171.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03110669-ILSVRC2012_val_00002171.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,748B, BPFP=0.0394 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,248B, BPFP=0.0574 +⌛️ [2/4] FRONTEND: Frontend time: 0.557s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.985s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.15128201 15.32137732 + layer.39.0 15.00769387 20596.13605442 + ------------------------------------------------------------------------------------- + TOTAL 7.57948794 10305.72871587 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50996 +BPFP 0.0484 bits/point +EBPFP 0.0484 equivalent bits/point +MSE 10305.728716 +---------------------- -------------------------------------------------------- +Time: 1.592s Load: 0.050s, Pack+Encode: 0.557s, Decode+Unpack: 0.985s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10305.7287 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03110669-ILSVRC2012_val_00002171.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03110669-ILSVRC2012_val_00002171.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124043-ILSVRC2012_val_00000766.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124043-ILSVRC2012_val_00000766.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,688B, BPFP=0.0412 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,040B, BPFP=0.0608 +⌛️ [2/4] FRONTEND: Frontend time: 0.574s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.027s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11473456 14.62500095 + layer.39.0 54.83309418 21810.53449951 + ------------------------------------------------------------------------------------- + TOTAL 27.47391437 10912.57975023 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 53728 +BPFP 0.0510 bits/point +EBPFP 0.0510 equivalent bits/point +MSE 10912.579750 +---------------------- -------------------------------------------------------- +Time: 1.651s Load: 0.050s, Pack+Encode: 0.574s, Decode+Unpack: 1.027s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10912.5798 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124043-ILSVRC2012_val_00000766.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03124043-ILSVRC2012_val_00000766.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124170-ILSVRC2012_val_00001875.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124170-ILSVRC2012_val_00001875.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,396B, BPFP=0.0387 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,912B, BPFP=0.0587 +⌛️ [2/4] FRONTEND: Frontend time: 0.578s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.053s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11393612 14.85146190 + layer.39.0 9.06747107 17349.17784257 + ------------------------------------------------------------------------------------- + TOTAL 4.59070360 8682.01465223 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51308 +BPFP 0.0487 bits/point +EBPFP 0.0487 equivalent bits/point +MSE 8682.014652 +---------------------- -------------------------------------------------------- +Time: 1.701s Load: 0.070s, Pack+Encode: 0.578s, Decode+Unpack: 1.053s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8682.0147 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124170-ILSVRC2012_val_00001875.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03124170-ILSVRC2012_val_00001875.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03126707-ILSVRC2012_val_00000020.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03126707-ILSVRC2012_val_00000020.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,420B, BPFP=0.0407 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,740B, BPFP=0.0583 +⌛️ [2/4] FRONTEND: Frontend time: 0.531s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.984s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.15273996 15.07103472 + layer.39.0 1033.15269679 19717.05150632 + ------------------------------------------------------------------------------------- + TOTAL 516.65271838 9866.06127052 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52160 +BPFP 0.0495 bits/point +EBPFP 0.0495 equivalent bits/point +MSE 9866.061271 +---------------------- -------------------------------------------------------- +Time: 1.564s Load: 0.050s, Pack+Encode: 0.531s, Decode+Unpack: 0.984s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9866.0613 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03126707-ILSVRC2012_val_00000020.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03126707-ILSVRC2012_val_00000020.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03127747-ILSVRC2012_val_00001689.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03127747-ILSVRC2012_val_00001689.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,564B, BPFP=0.0371 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,680B, BPFP=0.0601 +⌛️ [2/4] FRONTEND: Frontend time: 0.544s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.992s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10152024 14.44002445 + layer.39.0 322.92343902 21106.63556851 + ------------------------------------------------------------------------------------- + TOTAL 161.51247963 10560.53779648 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51244 +BPFP 0.0486 bits/point +EBPFP 0.0486 equivalent bits/point +MSE 10560.537796 +---------------------- -------------------------------------------------------- +Time: 1.607s Load: 0.070s, Pack+Encode: 0.544s, Decode+Unpack: 0.992s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10560.5378 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03127747-ILSVRC2012_val_00001689.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03127747-ILSVRC2012_val_00001689.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03131574-ILSVRC2012_val_00003036.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03131574-ILSVRC2012_val_00003036.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,408B, BPFP=0.0387 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 34,056B, BPFP=0.0646 +⌛️ [2/4] FRONTEND: Frontend time: 0.527s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.980s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09568423 14.64354615 + layer.39.0 163.24681122 23587.15840622 + ------------------------------------------------------------------------------------- + TOTAL 81.67124773 11800.90097618 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 54464 +BPFP 0.0517 bits/point +EBPFP 0.0517 equivalent bits/point +MSE 11800.900976 +---------------------- -------------------------------------------------------- +Time: 1.578s Load: 0.071s, Pack+Encode: 0.527s, Decode+Unpack: 0.980s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 11800.9010 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03131574-ILSVRC2012_val_00003036.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03131574-ILSVRC2012_val_00003036.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03133878-ILSVRC2012_val_00000534.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03133878-ILSVRC2012_val_00000534.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,632B, BPFP=0.0392 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 33,132B, BPFP=0.0629 +⌛️ [2/4] FRONTEND: Frontend time: 0.546s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.983s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11186348 15.02116740 + layer.39.0 28.46096218 21821.82701652 + ------------------------------------------------------------------------------------- + TOTAL 14.28641283 10918.42409196 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 53764 +BPFP 0.0510 bits/point +EBPFP 0.0510 equivalent bits/point +MSE 10918.424092 +---------------------- -------------------------------------------------------- +Time: 1.579s Load: 0.051s, Pack+Encode: 0.546s, Decode+Unpack: 0.983s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10918.4241 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03133878-ILSVRC2012_val_00000534.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03133878-ILSVRC2012_val_00000534.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03134739-ILSVRC2012_val_00000249.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03134739-ILSVRC2012_val_00000249.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,228B, BPFP=0.0365 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,776B, BPFP=0.0622 +⌛️ [2/4] FRONTEND: Frontend time: 0.524s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.970s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09967384 14.66646072 + layer.39.0 372.24465500 23658.23712342 + ------------------------------------------------------------------------------------- + TOTAL 186.17216442 11836.45179207 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52004 +BPFP 0.0494 bits/point +EBPFP 0.0494 equivalent bits/point +MSE 11836.451792 +---------------------- -------------------------------------------------------- +Time: 1.556s Load: 0.061s, Pack+Encode: 0.524s, Decode+Unpack: 0.970s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 11836.4518 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03134739-ILSVRC2012_val_00000249.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03134739-ILSVRC2012_val_00000249.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03141823-ILSVRC2012_val_00001337.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03141823-ILSVRC2012_val_00001337.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 18,996B, BPFP=0.0361 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,100B, BPFP=0.0571 +⌛️ [2/4] FRONTEND: Frontend time: 0.524s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.984s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10422104 14.92761480 + layer.39.0 29.45558301 22710.11467444 + ------------------------------------------------------------------------------------- + TOTAL 14.77990203 11362.52114462 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 49096 +BPFP 0.0466 bits/point +EBPFP 0.0466 equivalent bits/point +MSE 11362.521145 +---------------------- -------------------------------------------------------- +Time: 1.569s Load: 0.061s, Pack+Encode: 0.524s, Decode+Unpack: 0.984s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 11362.5211 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03141823-ILSVRC2012_val_00001337.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03141823-ILSVRC2012_val_00001337.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03160309-ILSVRC2012_val_00000330.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03160309-ILSVRC2012_val_00000330.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,744B, BPFP=0.0413 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,284B, BPFP=0.0613 +⌛️ [2/4] FRONTEND: Frontend time: 0.537s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.975s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09980877 14.90233521 + layer.39.0 30.04123011 18583.04178814 + ------------------------------------------------------------------------------------- + TOTAL 15.07051944 9298.97206168 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 54028 +BPFP 0.0513 bits/point +EBPFP 0.0513 equivalent bits/point +MSE 9298.972062 +---------------------- -------------------------------------------------------- +Time: 1.562s Load: 0.050s, Pack+Encode: 0.537s, Decode+Unpack: 0.975s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9298.9721 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03160309-ILSVRC2012_val_00000330.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03160309-ILSVRC2012_val_00000330.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03187595-ILSVRC2012_val_00000137.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03187595-ILSVRC2012_val_00000137.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,268B, BPFP=0.0366 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,208B, BPFP=0.0573 +⌛️ [2/4] FRONTEND: Frontend time: 0.539s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.995s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10716813 14.77068528 + layer.39.0 12.39187394 20267.37414966 + ------------------------------------------------------------------------------------- + TOTAL 6.24952103 10141.07241747 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 49476 +BPFP 0.0470 bits/point +EBPFP 0.0470 equivalent bits/point +MSE 10141.072417 +---------------------- -------------------------------------------------------- +Time: 1.596s Load: 0.062s, Pack+Encode: 0.539s, Decode+Unpack: 0.995s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10141.0724 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03187595-ILSVRC2012_val_00000137.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03187595-ILSVRC2012_val_00000137.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03188531-ILSVRC2012_val_00000493.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03188531-ILSVRC2012_val_00000493.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 18,576B, BPFP=0.0353 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,524B, BPFP=0.0598 +⌛️ [2/4] FRONTEND: Frontend time: 0.591s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.030s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09509044 14.56770833 + layer.39.0 10.77256154 21508.58697765 + ------------------------------------------------------------------------------------- + TOTAL 5.43382599 10761.57734299 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50100 +BPFP 0.0475 bits/point +EBPFP 0.0475 equivalent bits/point +MSE 10761.577343 +---------------------- -------------------------------------------------------- +Time: 1.701s Load: 0.080s, Pack+Encode: 0.591s, Decode+Unpack: 1.030s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10761.5773 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03188531-ILSVRC2012_val_00000493.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03188531-ILSVRC2012_val_00000493.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03196217-ILSVRC2012_val_00003643.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03196217-ILSVRC2012_val_00003643.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,188B, BPFP=0.0364 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,964B, BPFP=0.0607 +⌛️ [2/4] FRONTEND: Frontend time: 0.556s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.971s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09478207 14.49947233 + layer.39.0 65.57403274 19915.10981535 + ------------------------------------------------------------------------------------- + TOTAL 32.83440740 9964.80464384 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51152 +BPFP 0.0485 bits/point +EBPFP 0.0485 equivalent bits/point +MSE 9964.804644 +---------------------- -------------------------------------------------------- +Time: 1.577s Load: 0.050s, Pack+Encode: 0.556s, Decode+Unpack: 0.971s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9964.8046 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03196217-ILSVRC2012_val_00003643.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03196217-ILSVRC2012_val_00003643.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03201208-ILSVRC2012_val_00000241.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03201208-ILSVRC2012_val_00000241.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,216B, BPFP=0.0384 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 29,700B, BPFP=0.0564 +⌛️ [2/4] FRONTEND: Frontend time: 0.565s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.966s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10331685 14.86226862 + layer.39.0 136.59314261 20237.44412051 + ------------------------------------------------------------------------------------- + TOTAL 68.34822973 10126.15319456 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 49916 +BPFP 0.0474 bits/point +EBPFP 0.0474 equivalent bits/point +MSE 10126.153195 +---------------------- -------------------------------------------------------- +Time: 1.601s Load: 0.070s, Pack+Encode: 0.565s, Decode+Unpack: 0.966s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10126.1532 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03201208-ILSVRC2012_val_00000241.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03201208-ILSVRC2012_val_00000241.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03207743-ILSVRC2012_val_00000256.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03207743-ILSVRC2012_val_00000256.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 23,200B, BPFP=0.0440 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,264B, BPFP=0.0612 +⌛️ [2/4] FRONTEND: Frontend time: 0.529s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.993s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09674843 15.24385686 + layer.39.0 189.63590258 21977.13896987 + ------------------------------------------------------------------------------------- + TOTAL 94.86632550 10996.19141337 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 55464 +BPFP 0.0526 bits/point +EBPFP 0.0526 equivalent bits/point +MSE 10996.191413 +---------------------- -------------------------------------------------------- +Time: 1.574s Load: 0.052s, Pack+Encode: 0.529s, Decode+Unpack: 0.993s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10996.1914 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03207743-ILSVRC2012_val_00000256.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03207743-ILSVRC2012_val_00000256.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03216828-ILSVRC2012_val_00001729.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03216828-ILSVRC2012_val_00001729.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,340B, BPFP=0.0405 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,580B, BPFP=0.0599 +⌛️ [2/4] FRONTEND: Frontend time: 0.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.009s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10800209 15.01828136 + layer.39.0 31.30713223 19049.77453839 + ------------------------------------------------------------------------------------- + TOTAL 15.70756716 9532.39640988 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52920 +BPFP 0.0502 bits/point +EBPFP 0.0502 equivalent bits/point +MSE 9532.396410 +---------------------- -------------------------------------------------------- +Time: 1.661s Load: 0.070s, Pack+Encode: 0.581s, Decode+Unpack: 1.009s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9532.3964 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03216828-ILSVRC2012_val_00001729.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03216828-ILSVRC2012_val_00001729.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03218198-ILSVRC2012_val_00002266.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03218198-ILSVRC2012_val_00002266.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.079s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,720B, BPFP=0.0374 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,656B, BPFP=0.0582 +⌛️ [2/4] FRONTEND: Frontend time: 0.586s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.047s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11617067 15.09830539 + layer.39.0 195.83184524 19898.02915452 + ------------------------------------------------------------------------------------- + TOTAL 97.97400795 9956.56372996 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50376 +BPFP 0.0478 bits/point +EBPFP 0.0478 equivalent bits/point +MSE 9956.563730 +---------------------- -------------------------------------------------------- +Time: 1.713s Load: 0.079s, Pack+Encode: 0.586s, Decode+Unpack: 1.047s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9956.5637 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03218198-ILSVRC2012_val_00002266.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03218198-ILSVRC2012_val_00002266.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03220513-ILSVRC2012_val_00001868.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03220513-ILSVRC2012_val_00001868.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 23,472B, BPFP=0.0446 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,488B, BPFP=0.0617 +⌛️ [2/4] FRONTEND: Frontend time: 0.508s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.965s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.20032125 15.93724376 + layer.39.0 377.00176142 22801.76093294 + ------------------------------------------------------------------------------------- + TOTAL 188.60104134 11408.84908835 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 55960 +BPFP 0.0531 bits/point +EBPFP 0.0531 equivalent bits/point +MSE 11408.849088 +---------------------- -------------------------------------------------------- +Time: 1.523s Load: 0.050s, Pack+Encode: 0.508s, Decode+Unpack: 0.965s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 11408.8491 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03220513-ILSVRC2012_val_00001868.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03220513-ILSVRC2012_val_00001868.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03223299-ILSVRC2012_val_00001893.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03223299-ILSVRC2012_val_00001893.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 22,116B, BPFP=0.0420 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,880B, BPFP=0.0624 +⌛️ [2/4] FRONTEND: Frontend time: 0.551s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.012s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10735053 14.59352792 + layer.39.0 354.51621720 21732.46647230 + ------------------------------------------------------------------------------------- + TOTAL 177.31178386 10873.53000011 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 54996 +BPFP 0.0522 bits/point +EBPFP 0.0522 equivalent bits/point +MSE 10873.530000 +---------------------- -------------------------------------------------------- +Time: 1.615s Load: 0.052s, Pack+Encode: 0.551s, Decode+Unpack: 1.012s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10873.5300 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03223299-ILSVRC2012_val_00001893.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03223299-ILSVRC2012_val_00001893.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03240683-ILSVRC2012_val_00000504.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03240683-ILSVRC2012_val_00000504.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,092B, BPFP=0.0400 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,912B, BPFP=0.0606 +⌛️ [2/4] FRONTEND: Frontend time: 0.582s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.040s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10065408 14.69753895 + layer.39.0 443.53838678 20583.12342080 + ------------------------------------------------------------------------------------- + TOTAL 221.81952043 10298.91047987 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 53004 +BPFP 0.0503 bits/point +EBPFP 0.0503 equivalent bits/point +MSE 10298.910480 +---------------------- -------------------------------------------------------- +Time: 1.692s Load: 0.070s, Pack+Encode: 0.582s, Decode+Unpack: 1.040s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10298.9105 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03240683-ILSVRC2012_val_00000504.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03240683-ILSVRC2012_val_00000504.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03250847-ILSVRC2012_val_00000542.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03250847-ILSVRC2012_val_00000542.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,492B, BPFP=0.0370 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,268B, BPFP=0.0612 +⌛️ [2/4] FRONTEND: Frontend time: 0.507s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.982s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10136319 14.76469969 + layer.39.0 140.24735787 22367.72011662 + ------------------------------------------------------------------------------------- + TOTAL 70.17436053 11191.24240815 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51760 +BPFP 0.0491 bits/point +EBPFP 0.0491 equivalent bits/point +MSE 11191.242408 +---------------------- -------------------------------------------------------- +Time: 1.538s Load: 0.050s, Pack+Encode: 0.507s, Decode+Unpack: 0.982s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 11191.2424 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03250847-ILSVRC2012_val_00000542.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03250847-ILSVRC2012_val_00000542.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03255030-ILSVRC2012_val_00001045.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03255030-ILSVRC2012_val_00001045.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,672B, BPFP=0.0392 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,620B, BPFP=0.0600 +⌛️ [2/4] FRONTEND: Frontend time: 0.504s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.965s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10050351 14.75031413 + layer.39.0 12.06722622 19442.18464529 + ------------------------------------------------------------------------------------- + TOTAL 6.08386487 9728.46747971 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52292 +BPFP 0.0496 bits/point +EBPFP 0.0496 equivalent bits/point +MSE 9728.467480 +---------------------- -------------------------------------------------------- +Time: 1.521s Load: 0.052s, Pack+Encode: 0.504s, Decode+Unpack: 0.965s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9728.4675 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03255030-ILSVRC2012_val_00001045.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03255030-ILSVRC2012_val_00001045.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03271574-ILSVRC2012_val_00000942.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03271574-ILSVRC2012_val_00000942.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,992B, BPFP=0.0379 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 34,040B, BPFP=0.0646 +⌛️ [2/4] FRONTEND: Frontend time: 0.544s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.989s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10164264 14.67099239 + layer.39.0 660.63544704 22122.75996113 + ------------------------------------------------------------------------------------- + TOTAL 330.36854484 11068.71547676 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 54032 +BPFP 0.0513 bits/point +EBPFP 0.0513 equivalent bits/point +MSE 11068.715477 +---------------------- -------------------------------------------------------- +Time: 1.585s Load: 0.052s, Pack+Encode: 0.544s, Decode+Unpack: 0.989s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 11068.7155 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03271574-ILSVRC2012_val_00000942.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03271574-ILSVRC2012_val_00000942.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272010-ILSVRC2012_val_00000374.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272010-ILSVRC2012_val_00000374.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 18,348B, BPFP=0.0348 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 29,604B, BPFP=0.0562 +⌛️ [2/4] FRONTEND: Frontend time: 0.537s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.985s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10420663 14.77502619 + layer.39.0 9.63653369 18818.47230321 + ------------------------------------------------------------------------------------- + TOTAL 4.87037016 9416.62366470 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 47952 +BPFP 0.0455 bits/point +EBPFP 0.0455 equivalent bits/point +MSE 9416.623665 +---------------------- -------------------------------------------------------- +Time: 1.572s Load: 0.050s, Pack+Encode: 0.537s, Decode+Unpack: 0.985s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9416.6237 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272010-ILSVRC2012_val_00000374.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03272010-ILSVRC2012_val_00000374.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272562-ILSVRC2012_val_00001699.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272562-ILSVRC2012_val_00001699.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,524B, BPFP=0.0409 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,920B, BPFP=0.0625 +⌛️ [2/4] FRONTEND: Frontend time: 0.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.024s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11399285 15.02539062 + layer.39.0 12.79457642 18905.20699708 + ------------------------------------------------------------------------------------- + TOTAL 6.45428464 9460.11619385 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 54444 +BPFP 0.0517 bits/point +EBPFP 0.0517 equivalent bits/point +MSE 9460.116194 +---------------------- -------------------------------------------------------- +Time: 1.675s Load: 0.070s, Pack+Encode: 0.581s, Decode+Unpack: 1.024s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9460.1162 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272562-ILSVRC2012_val_00001699.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03272562-ILSVRC2012_val_00001699.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03290653-ILSVRC2012_val_00000199.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03290653-ILSVRC2012_val_00000199.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,656B, BPFP=0.0392 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,216B, BPFP=0.0611 +⌛️ [2/4] FRONTEND: Frontend time: 0.541s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.967s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09581849 14.57312640 + layer.39.0 9.30266794 20325.95724004 + ------------------------------------------------------------------------------------- + TOTAL 4.69924322 10170.26518322 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52872 +BPFP 0.0502 bits/point +EBPFP 0.0502 equivalent bits/point +MSE 10170.265183 +---------------------- -------------------------------------------------------- +Time: 1.568s Load: 0.059s, Pack+Encode: 0.541s, Decode+Unpack: 0.967s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10170.2652 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03290653-ILSVRC2012_val_00000199.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03290653-ILSVRC2012_val_00000199.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03291819-ILSVRC2012_val_00000419.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03291819-ILSVRC2012_val_00000419.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,892B, BPFP=0.0378 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,700B, BPFP=0.0602 +⌛️ [2/4] FRONTEND: Frontend time: 0.561s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.985s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10621172 14.61835007 + layer.39.0 31.36357166 20269.89698737 + ------------------------------------------------------------------------------------- + TOTAL 15.73489169 10142.25766872 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51592 +BPFP 0.0490 bits/point +EBPFP 0.0490 equivalent bits/point +MSE 10142.257669 +---------------------- -------------------------------------------------------- +Time: 1.598s Load: 0.052s, Pack+Encode: 0.561s, Decode+Unpack: 0.985s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10142.2577 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03291819-ILSVRC2012_val_00000419.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03291819-ILSVRC2012_val_00000419.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03314780-ILSVRC2012_val_00000624.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03314780-ILSVRC2012_val_00000624.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 18,872B, BPFP=0.0358 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,832B, BPFP=0.0604 +⌛️ [2/4] FRONTEND: Frontend time: 0.509s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.984s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10172509 14.70061289 + layer.39.0 35.60390853 22148.71914480 + ------------------------------------------------------------------------------------- + TOTAL 17.85281681 11081.70987885 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50704 +BPFP 0.0481 bits/point +EBPFP 0.0481 equivalent bits/point +MSE 11081.709879 +---------------------- -------------------------------------------------------- +Time: 1.545s Load: 0.052s, Pack+Encode: 0.509s, Decode+Unpack: 0.984s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 11081.7099 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03314780-ILSVRC2012_val_00000624.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03314780-ILSVRC2012_val_00000624.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03325584-ILSVRC2012_val_00001256.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03325584-ILSVRC2012_val_00001256.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,156B, BPFP=0.0364 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,456B, BPFP=0.0578 +⌛️ [2/4] FRONTEND: Frontend time: 0.540s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.963s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11348933 15.18568069 + layer.39.0 26.85401292 20815.70068027 + ------------------------------------------------------------------------------------- + TOTAL 13.48375113 10415.44318048 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 49612 +BPFP 0.0471 bits/point +EBPFP 0.0471 equivalent bits/point +MSE 10415.443180 +---------------------- -------------------------------------------------------- +Time: 1.553s Load: 0.050s, Pack+Encode: 0.540s, Decode+Unpack: 0.963s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10415.4432 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03325584-ILSVRC2012_val_00001256.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03325584-ILSVRC2012_val_00001256.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03337140-ILSVRC2012_val_00000132.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03337140-ILSVRC2012_val_00000132.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,656B, BPFP=0.0373 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,348B, BPFP=0.0595 +⌛️ [2/4] FRONTEND: Frontend time: 0.559s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.009s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09852950 14.62149045 + layer.39.0 10.39905343 20454.05442177 + ------------------------------------------------------------------------------------- + TOTAL 5.24879146 10234.33795611 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51004 +BPFP 0.0484 bits/point +EBPFP 0.0484 equivalent bits/point +MSE 10234.337956 +---------------------- -------------------------------------------------------- +Time: 1.618s Load: 0.050s, Pack+Encode: 0.559s, Decode+Unpack: 1.009s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10234.3380 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03337140-ILSVRC2012_val_00000132.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03337140-ILSVRC2012_val_00000132.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03344393-ILSVRC2012_val_00000288.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03344393-ILSVRC2012_val_00000288.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,784B, BPFP=0.0394 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,896B, BPFP=0.0624 +⌛️ [2/4] FRONTEND: Frontend time: 0.526s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.975s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09830858 14.76002946 + layer.39.0 109.00505649 19712.90379009 + ------------------------------------------------------------------------------------- + TOTAL 54.55168253 9863.83190977 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 53680 +BPFP 0.0509 bits/point +EBPFP 0.0509 equivalent bits/point +MSE 9863.831910 +---------------------- -------------------------------------------------------- +Time: 1.552s Load: 0.051s, Pack+Encode: 0.526s, Decode+Unpack: 0.975s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9863.8319 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03344393-ILSVRC2012_val_00000288.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03344393-ILSVRC2012_val_00000288.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03345487-ILSVRC2012_val_00000764.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03345487-ILSVRC2012_val_00000764.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,204B, BPFP=0.0365 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,308B, BPFP=0.0613 +⌛️ [2/4] FRONTEND: Frontend time: 0.515s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.985s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10639974 14.96818058 + layer.39.0 14.55993569 17709.07482993 + ------------------------------------------------------------------------------------- + TOTAL 7.33316771 8862.02150525 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51512 +BPFP 0.0489 bits/point +EBPFP 0.0489 equivalent bits/point +MSE 8862.021505 +---------------------- -------------------------------------------------------- +Time: 1.562s Load: 0.061s, Pack+Encode: 0.515s, Decode+Unpack: 0.985s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8862.0215 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03345487-ILSVRC2012_val_00000764.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03345487-ILSVRC2012_val_00000764.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03347037-ILSVRC2012_val_00000743.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03347037-ILSVRC2012_val_00000743.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 22,036B, BPFP=0.0418 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,284B, BPFP=0.0613 +⌛️ [2/4] FRONTEND: Frontend time: 0.563s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.060s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14351733 15.17057292 + layer.39.0 355.98426871 21898.06997085 + ------------------------------------------------------------------------------------- + TOTAL 178.06389302 10956.62027188 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 54320 +BPFP 0.0516 bits/point +EBPFP 0.0516 equivalent bits/point +MSE 10956.620272 +---------------------- -------------------------------------------------------- +Time: 1.674s Load: 0.051s, Pack+Encode: 0.563s, Decode+Unpack: 1.060s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10956.6203 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03347037-ILSVRC2012_val_00000743.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03347037-ILSVRC2012_val_00000743.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03355925-ILSVRC2012_val_00000445.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03355925-ILSVRC2012_val_00000445.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,260B, BPFP=0.0385 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 27,972B, BPFP=0.0531 +⌛️ [2/4] FRONTEND: Frontend time: 0.644s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.039s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09979894 14.66823258 + layer.39.0 9.06502540 16655.51603499 + ------------------------------------------------------------------------------------- + TOTAL 4.58241217 8335.09213378 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 48232 +BPFP 0.0458 bits/point +EBPFP 0.0458 equivalent bits/point +MSE 8335.092134 +---------------------- -------------------------------------------------------- +Time: 1.753s Load: 0.069s, Pack+Encode: 0.644s, Decode+Unpack: 1.039s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 8335.0921 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03355925-ILSVRC2012_val_00000445.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03355925-ILSVRC2012_val_00000445.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03376595-ILSVRC2012_val_00001616.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03376595-ILSVRC2012_val_00001616.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,320B, BPFP=0.0386 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 33,820B, BPFP=0.0642 +⌛️ [2/4] FRONTEND: Frontend time: 0.550s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.002s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09988844 14.94473928 + layer.39.0 1408.20760447 27196.63751215 + ------------------------------------------------------------------------------------- + TOTAL 704.15374646 13605.79112571 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 54140 +BPFP 0.0514 bits/point +EBPFP 0.0514 equivalent bits/point +MSE 13605.791126 +---------------------- -------------------------------------------------------- +Time: 1.602s Load: 0.050s, Pack+Encode: 0.550s, Decode+Unpack: 1.002s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 13605.7911 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03376595-ILSVRC2012_val_00001616.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03376595-ILSVRC2012_val_00001616.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03379051-ILSVRC2012_val_00002562.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03379051-ILSVRC2012_val_00002562.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 18,836B, BPFP=0.0358 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 34,644B, BPFP=0.0658 +⌛️ [2/4] FRONTEND: Frontend time: 0.527s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.003s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10889592 14.90144216 + layer.39.0 102.95462828 24146.46258503 + ------------------------------------------------------------------------------------- + TOTAL 51.53176210 12080.68201360 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 53480 +BPFP 0.0508 bits/point +EBPFP 0.0508 equivalent bits/point +MSE 12080.682014 +---------------------- -------------------------------------------------------- +Time: 1.600s Load: 0.071s, Pack+Encode: 0.527s, Decode+Unpack: 1.003s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 12080.6820 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03379051-ILSVRC2012_val_00002562.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03379051-ILSVRC2012_val_00002562.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388043-ILSVRC2012_val_00001018.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388043-ILSVRC2012_val_00001018.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 21,324B, BPFP=0.0405 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 32,452B, BPFP=0.0616 +⌛️ [2/4] FRONTEND: Frontend time: 0.587s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.033s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09747427 14.83106703 + layer.39.0 21.12933142 20684.47619048 + ------------------------------------------------------------------------------------- + TOTAL 10.61340285 10349.65362875 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 53776 +BPFP 0.0510 bits/point +EBPFP 0.0510 equivalent bits/point +MSE 10349.653629 +---------------------- -------------------------------------------------------- +Time: 1.689s Load: 0.069s, Pack+Encode: 0.587s, Decode+Unpack: 1.033s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10349.6536 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388043-ILSVRC2012_val_00001018.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03388043-ILSVRC2012_val_00001018.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388183-ILSVRC2012_val_00002799.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388183-ILSVRC2012_val_00002799.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,660B, BPFP=0.0373 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,608B, BPFP=0.0600 +⌛️ [2/4] FRONTEND: Frontend time: 0.582s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.050s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10066175 14.91024071 + layer.39.0 786.68810739 21941.45966958 + ------------------------------------------------------------------------------------- + TOTAL 393.39438457 10978.18495515 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 51268 +BPFP 0.0487 bits/point +EBPFP 0.0487 equivalent bits/point +MSE 10978.184955 +---------------------- -------------------------------------------------------- +Time: 1.701s Load: 0.069s, Pack+Encode: 0.582s, Decode+Unpack: 1.050s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10978.1850 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388183-ILSVRC2012_val_00002799.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03388183-ILSVRC2012_val_00002799.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388549-ILSVRC2012_val_00002945.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388549-ILSVRC2012_val_00002945.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,676B, BPFP=0.0373 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 33,292B, BPFP=0.0632 +⌛️ [2/4] FRONTEND: Frontend time: 0.525s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.991s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09849939 14.75809436 + layer.39.0 10.79426799 21057.88921283 + ------------------------------------------------------------------------------------- + TOTAL 5.44638369 10536.32365360 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 52968 +BPFP 0.0503 bits/point +EBPFP 0.0503 equivalent bits/point +MSE 10536.323654 +---------------------- -------------------------------------------------------- +Time: 1.568s Load: 0.052s, Pack+Encode: 0.525s, Decode+Unpack: 0.991s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10536.3237 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388549-ILSVRC2012_val_00002945.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03388549-ILSVRC2012_val_00002945.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03393912-ILSVRC2012_val_00000047.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03393912-ILSVRC2012_val_00000047.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,576B, BPFP=0.0391 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 36,884B, BPFP=0.0700 +⌛️ [2/4] FRONTEND: Frontend time: 0.521s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.977s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09729456 14.72767288 + layer.39.0 38.26720800 23134.38095238 + ------------------------------------------------------------------------------------- + TOTAL 19.18225128 11574.55431263 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 57460 +BPFP 0.0545 bits/point +EBPFP 0.0545 equivalent bits/point +MSE 11574.554313 +---------------------- -------------------------------------------------------- +Time: 1.548s Load: 0.050s, Pack+Encode: 0.521s, Decode+Unpack: 0.977s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 11574.5543 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03393912-ILSVRC2012_val_00000047.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03393912-ILSVRC2012_val_00000047.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03394916-ILSVRC2012_val_00000957.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03394916-ILSVRC2012_val_00000957.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,724B, BPFP=0.0374 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 30,832B, BPFP=0.0585 +⌛️ [2/4] FRONTEND: Frontend time: 0.520s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.983s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10421823 14.82949921 + layer.39.0 9.72561820 19417.25558795 + ------------------------------------------------------------------------------------- + TOTAL 4.91491822 9716.04254358 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50556 +BPFP 0.0480 bits/point +EBPFP 0.0480 equivalent bits/point +MSE 9716.042544 +---------------------- -------------------------------------------------------- +Time: 1.555s Load: 0.052s, Pack+Encode: 0.520s, Decode+Unpack: 0.983s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 9716.0425 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03394916-ILSVRC2012_val_00000957.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03394916-ILSVRC2012_val_00000957.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03404251-ILSVRC2012_val_00000641.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03404251-ILSVRC2012_val_00000641.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 19,444B, BPFP=0.0369 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 31,200B, BPFP=0.0592 +⌛️ [2/4] FRONTEND: Frontend time: 0.526s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.993s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10764784 14.72623888 + layer.39.0 585.45553936 22779.62293489 + ------------------------------------------------------------------------------------- + TOTAL 292.78159360 11397.17458688 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 50644 +BPFP 0.0481 bits/point +EBPFP 0.0481 equivalent bits/point +MSE 11397.174587 +---------------------- -------------------------------------------------------- +Time: 1.571s Load: 0.051s, Pack+Encode: 0.526s, Decode+Unpack: 0.993s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 11397.1746 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03404251-ILSVRC2012_val_00000641.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03404251-ILSVRC2012_val_00000641.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03417042-ILSVRC2012_val_00001144.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03417042-ILSVRC2012_val_00001144.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 20,156B, BPFP=0.0383 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 34,384B, BPFP=0.0653 +⌛️ [2/4] FRONTEND: Frontend time: 0.553s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.961s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10091509 14.74812375 + layer.39.0 202.93364310 20170.63168124 + ------------------------------------------------------------------------------------- + TOTAL 101.51727910 10092.68990250 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 54540 +BPFP 0.0518 bits/point +EBPFP 0.0518 equivalent bits/point +MSE 10092.689902 +---------------------- -------------------------------------------------------- +Time: 1.565s Load: 0.051s, Pack+Encode: 0.553s, Decode+Unpack: 0.961s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 10092.6899 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03417042-ILSVRC2012_val_00001144.zst + to output-fixed/dinov3-total/lambda0.001/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03417042-ILSVRC2012_val_00001144.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 0.0498 bits/point +Avg EBPFP 0.0498 equivalent bits/point +Avg MSE 10474.617980 +Avg Time 1.611s +------------------------ ---------------------------- diff --git a/lambda0.004/elic-featurecoding-8bit-individual/cls_in1ka/dtufc_elic-featurecoding_dinov3-total_individual.log b/lambda0.004/elic-featurecoding-8bit-individual/cls_in1ka/dtufc_elic-featurecoding_dinov3-total_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..949bb4cf8ceb84fc67f32778c5ab3429efb91ace --- /dev/null +++ b/lambda0.004/elic-featurecoding-8bit-individual/cls_in1ka/dtufc_elic-featurecoding_dinov3-total_individual.log @@ -0,0 +1,9344 @@ +Experiment: dtufc_elic-featurecoding_dinov3-total_individual +Log file: output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/dtufc_elic-featurecoding_dinov3-total_individual.log +DTUFCCodecConfig: + arch: elic-featurecoding + handler: dinov3-total + checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.004_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.004_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 255 +Loaded elic-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.9' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.19' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.29' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.39' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json +Loaded per-key mappings: model=dinov3-total + Keys: ['layer.9', 'layer.19', 'layer.29', 'layer.39'] +---------------- -------------------------------------------------------------------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +Checkpoint codec_weights/elic_hybrid/elic2022-official_lambda0.004_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-DINOv3/imagenet1k-a +Output output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka +---------------- -------------------------------------------------------------------------------------------------------------------- +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01498041-0.006639_koala _ American bullfrog_0.6658246.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01498041-0.006639_koala _ American bullfrog_0.6658246.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 58,308B, BPFP=0.1107 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 177,212B, BPFP=0.3364 +⌛️ [2/4] FRONTEND: Frontend time: 2.979s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.526s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09594801 12.62020640 + layer.39.0 58.94484178 2827.13896987 + ------------------------------------------------------------------------------------- + TOTAL 29.52039490 1419.87958814 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 235520 +BPFP 0.2235 bits/point +EBPFP 0.2235 equivalent bits/point +MSE 1419.879588 +---------------------- -------------------------------------------------------- +Time: 5.577s Load: 0.072s, Pack+Encode: 2.979s, Decode+Unpack: 2.526s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1419.8796 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01498041-0.006639_koala _ American bullfrog_0.6658246.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n01498041-0.006639_koala _ American bullfrog_0.6658246.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01531178-0.000765_fox squirrel _ ant_0.9486805.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01531178-0.000765_fox squirrel _ ant_0.9486805.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.093s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 54,240B, BPFP=0.1030 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 168,000B, BPFP=0.3189 +⌛️ [2/4] FRONTEND: Frontend time: 2.602s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.492s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09773727 12.41185313 + layer.39.0 17.17825445 2861.24465500 + ------------------------------------------------------------------------------------- + TOTAL 8.63799586 1436.82825407 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 222240 +BPFP 0.2109 bits/point +EBPFP 0.2109 equivalent bits/point +MSE 1436.828254 +---------------------- -------------------------------------------------------- +Time: 5.186s Load: 0.093s, Pack+Encode: 2.602s, Decode+Unpack: 2.492s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1436.8283 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01531178-0.000765_fox squirrel _ ant_0.9486805.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n01531178-0.000765_fox squirrel _ ant_0.9486805.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01534433-0.004573_stingray _ stingray_0.97124094.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01534433-0.004573_stingray _ stingray_0.97124094.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 44,360B, BPFP=0.0842 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 132,048B, BPFP=0.2506 +⌛️ [2/4] FRONTEND: Frontend time: 2.588s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.473s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09515371 12.74049156 + layer.39.0 6.87362484 1851.96197765 + ------------------------------------------------------------------------------------- + TOTAL 3.48438928 932.35123461 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 176408 +BPFP 0.1674 bits/point +EBPFP 0.1674 equivalent bits/point +MSE 932.351235 +---------------------- -------------------------------------------------------- +Time: 5.113s Load: 0.052s, Pack+Encode: 2.588s, Decode+Unpack: 2.473s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 932.3512 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01534433-0.004573_stingray _ stingray_0.97124094.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n01534433-0.004573_stingray _ stingray_0.97124094.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01558993-0.000522_bow _ bow_0.9033333.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01558993-0.000522_bow _ bow_0.9033333.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 75,948B, BPFP=0.1442 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 139,376B, BPFP=0.2645 +⌛️ [2/4] FRONTEND: Frontend time: 2.596s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.494s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09874929 12.66192811 + layer.39.0 7.31778236 1775.52733236 + ------------------------------------------------------------------------------------- + TOTAL 3.70826583 894.09463023 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 215324 +BPFP 0.2044 bits/point +EBPFP 0.2044 equivalent bits/point +MSE 894.094630 +---------------------- -------------------------------------------------------- +Time: 5.142s Load: 0.051s, Pack+Encode: 2.596s, Decode+Unpack: 2.494s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 894.0946 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01558993-0.000522_bow _ bow_0.9033333.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n01558993-0.000522_bow _ bow_0.9033333.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01580077-0.004583_African bush elephant _ African bush elephant_0.59939015.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01580077-0.004583_African bush elephant _ African bush elephant_0.59939015.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 70,972B, BPFP=0.1347 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 161,268B, BPFP=0.3061 +⌛️ [2/4] FRONTEND: Frontend time: 2.612s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.492s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10720986 12.26157260 + layer.39.0 24.46209533 2069.78960155 + ------------------------------------------------------------------------------------- + TOTAL 12.28465260 1041.02558708 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 232240 +BPFP 0.2204 bits/point +EBPFP 0.2204 equivalent bits/point +MSE 1041.025587 +---------------------- -------------------------------------------------------- +Time: 5.155s Load: 0.051s, Pack+Encode: 2.612s, Decode+Unpack: 2.492s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1041.0256 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01580077-0.004583_African bush elephant _ African bush elephant_0.59939015.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n01580077-0.004583_African bush elephant _ African bush elephant_0.59939015.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01614925-0.049724_white-headed capuchin _ white-headed capuchin_0.9309422.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01614925-0.049724_white-headed capuchin _ white-headed capuchin_0.9309422.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 70,592B, BPFP=0.1340 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 134,032B, BPFP=0.2544 +⌛️ [2/4] FRONTEND: Frontend time: 2.606s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.501s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09739119 24.40817465 + layer.39.0 8.81423010 1924.95784742 + ------------------------------------------------------------------------------------- + TOTAL 4.45581065 974.68301104 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 204624 +BPFP 0.1942 bits/point +EBPFP 0.1942 equivalent bits/point +MSE 974.683011 +---------------------- -------------------------------------------------------- +Time: 5.157s Load: 0.050s, Pack+Encode: 2.606s, Decode+Unpack: 2.501s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 974.6830 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01614925-0.049724_white-headed capuchin _ white-headed capuchin_0.9309422.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n01614925-0.049724_white-headed capuchin _ white-headed capuchin_0.9309422.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01631663-0.004606_American bullfrog _ American bullfrog_0.8789855.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01631663-0.004606_American bullfrog _ American bullfrog_0.8789855.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 49,628B, BPFP=0.0942 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 158,576B, BPFP=0.3010 +⌛️ [2/4] FRONTEND: Frontend time: 2.580s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.482s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09716670 12.71628819 + layer.39.0 20.45897868 2045.40719145 + ------------------------------------------------------------------------------------- + TOTAL 10.27807269 1029.06173982 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 208204 +BPFP 0.1976 bits/point +EBPFP 0.1976 equivalent bits/point +MSE 1029.061740 +---------------------- -------------------------------------------------------- +Time: 5.113s Load: 0.051s, Pack+Encode: 2.580s, Decode+Unpack: 2.482s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1029.0617 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01631663-0.004606_American bullfrog _ American bullfrog_0.8789855.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n01631663-0.004606_American bullfrog _ American bullfrog_0.8789855.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01669191-0.029754_sandal _ sandal_0.38198605.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01669191-0.029754_sandal _ sandal_0.38198605.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 90,208B, BPFP=0.1712 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 136,980B, BPFP=0.2600 +⌛️ [2/4] FRONTEND: Frontend time: 2.603s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.486s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10877632 12.39272333 + layer.39.0 13.16500205 1659.10556365 + ------------------------------------------------------------------------------------- + TOTAL 6.63688918 835.74914349 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 227188 +BPFP 0.2156 bits/point +EBPFP 0.2156 equivalent bits/point +MSE 835.749143 +---------------------- -------------------------------------------------------- +Time: 5.141s Load: 0.052s, Pack+Encode: 2.603s, Decode+Unpack: 2.486s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 835.7491 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01669191-0.029754_sandal _ sandal_0.38198605.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n01669191-0.029754_sandal _ sandal_0.38198605.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770081-0.000571_syringe _ syringe_0.7369336.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770081-0.000571_syringe _ syringe_0.7369336.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 50,620B, BPFP=0.0961 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 205,876B, BPFP=0.3908 +⌛️ [2/4] FRONTEND: Frontend time: 2.600s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.513s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09508557 12.57020526 + layer.39.0 60.03878538 2492.63046647 + ------------------------------------------------------------------------------------- + TOTAL 30.06693547 1252.60033587 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 256496 +BPFP 0.2434 bits/point +EBPFP 0.2434 equivalent bits/point +MSE 1252.600336 +---------------------- -------------------------------------------------------- +Time: 5.165s Load: 0.052s, Pack+Encode: 2.600s, Decode+Unpack: 2.513s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1252.6003 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770081-0.000571_syringe _ syringe_0.7369336.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n01770081-0.000571_syringe _ syringe_0.7369336.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770393-0.000908_harvestman _ harvestman_0.8782497.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770393-0.000908_harvestman _ harvestman_0.8782497.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 64,820B, BPFP=0.1230 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 166,228B, BPFP=0.3155 +⌛️ [2/4] FRONTEND: Frontend time: 2.599s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.504s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11350316 12.51293067 + layer.39.0 19.73148992 2584.80393586 + ------------------------------------------------------------------------------------- + TOTAL 9.92249654 1298.65843327 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 231048 +BPFP 0.2193 bits/point +EBPFP 0.2193 equivalent bits/point +MSE 1298.658433 +---------------------- -------------------------------------------------------- +Time: 5.155s Load: 0.052s, Pack+Encode: 2.599s, Decode+Unpack: 2.504s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1298.6584 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770393-0.000908_harvestman _ harvestman_0.8782497.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n01770393-0.000908_harvestman _ harvestman_0.8782497.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01784675-0.027853_syringe _ syringe_0.9584382.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01784675-0.027853_syringe _ syringe_0.9584382.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 80,120B, BPFP=0.1521 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 176,532B, BPFP=0.3351 +⌛️ [2/4] FRONTEND: Frontend time: 2.612s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.501s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11002613 12.55537650 + layer.39.0 26.08665877 4432.78522838 + ------------------------------------------------------------------------------------- + TOTAL 13.09834245 2222.67030244 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 256652 +BPFP 0.2436 bits/point +EBPFP 0.2436 equivalent bits/point +MSE 2222.670302 +---------------------- -------------------------------------------------------- +Time: 5.165s Load: 0.052s, Pack+Encode: 2.612s, Decode+Unpack: 2.501s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2222.6703 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01784675-0.027853_syringe _ syringe_0.9584382.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n01784675-0.027853_syringe _ syringe_0.9584382.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01819313-0.053742_koala _ koala_0.98647016.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01819313-0.053742_koala _ koala_0.98647016.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 83,292B, BPFP=0.1581 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 192,588B, BPFP=0.3655 +⌛️ [2/4] FRONTEND: Frontend time: 2.607s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.509s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14565475 12.62125509 + layer.39.0 25.01023445 2670.89091351 + ------------------------------------------------------------------------------------- + TOTAL 12.57794460 1341.75608430 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 275880 +BPFP 0.2618 bits/point +EBPFP 0.2618 equivalent bits/point +MSE 1341.756084 +---------------------- -------------------------------------------------------- +Time: 5.168s Load: 0.052s, Pack+Encode: 2.607s, Decode+Unpack: 2.509s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1341.7561 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01819313-0.053742_koala _ koala_0.98647016.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n01819313-0.053742_koala _ koala_0.98647016.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01820546-0.012522_toucan _ toucan_0.63882655.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01820546-0.012522_toucan _ toucan_0.63882655.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 55,072B, BPFP=0.1045 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 154,924B, BPFP=0.2941 +⌛️ [2/4] FRONTEND: Frontend time: 2.615s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.501s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09696376 12.48955486 + layer.39.0 16.65489097 2345.78352770 + ------------------------------------------------------------------------------------- + TOTAL 8.37592737 1179.13654128 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 209996 +BPFP 0.1993 bits/point +EBPFP 0.1993 equivalent bits/point +MSE 1179.136541 +---------------------- -------------------------------------------------------- +Time: 5.177s Load: 0.061s, Pack+Encode: 2.615s, Decode+Unpack: 2.501s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1179.1365 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01820546-0.012522_toucan _ toucan_0.63882655.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n01820546-0.012522_toucan _ toucan_0.63882655.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01833805-0.013248_toucan _ lorikeet_0.77773976.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01833805-0.013248_toucan _ lorikeet_0.77773976.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 52,652B, BPFP=0.0999 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 155,632B, BPFP=0.2954 +⌛️ [2/4] FRONTEND: Frontend time: 2.602s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.501s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09866240 12.45953767 + layer.39.0 7.67772963 2142.20262391 + ------------------------------------------------------------------------------------- + TOTAL 3.88819601 1077.33108079 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 208284 +BPFP 0.1977 bits/point +EBPFP 0.1977 equivalent bits/point +MSE 1077.331081 +---------------------- -------------------------------------------------------- +Time: 5.153s Load: 0.050s, Pack+Encode: 2.602s, Decode+Unpack: 2.501s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1077.3311 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01833805-0.013248_toucan _ lorikeet_0.77773976.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n01833805-0.013248_toucan _ lorikeet_0.77773976.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01843383-0.013605_white-headed capuchin _ white-headed capuchin_0.9984768.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01843383-0.013605_white-headed capuchin _ white-headed capuchin_0.9984768.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 71,796B, BPFP=0.1363 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 163,536B, BPFP=0.3104 +⌛️ [2/4] FRONTEND: Frontend time: 2.599s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.496s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11910487 1.04319213 + layer.39.0 9.20068692 2500.18124393 + ------------------------------------------------------------------------------------- + TOTAL 4.65989589 1250.61221803 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 235332 +BPFP 0.2233 bits/point +EBPFP 0.2233 equivalent bits/point +MSE 1250.612218 +---------------------- -------------------------------------------------------- +Time: 5.145s Load: 0.051s, Pack+Encode: 2.599s, Decode+Unpack: 2.496s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1250.6122 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01843383-0.013605_white-headed capuchin _ white-headed capuchin_0.9984768.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n01843383-0.013605_white-headed capuchin _ white-headed capuchin_0.9984768.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01924916-0.000644_jay _ jay_0.82223135.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01924916-0.000644_jay _ jay_0.82223135.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 79,588B, BPFP=0.1511 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 137,440B, BPFP=0.2609 +⌛️ [2/4] FRONTEND: Frontend time: 2.601s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.500s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11488669 12.46390420 + layer.39.0 141.08750911 1975.48396501 + ------------------------------------------------------------------------------------- + TOTAL 70.60119790 993.97393461 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 217028 +BPFP 0.2060 bits/point +EBPFP 0.2060 equivalent bits/point +MSE 993.973935 +---------------------- -------------------------------------------------------- +Time: 5.152s Load: 0.052s, Pack+Encode: 2.601s, Decode+Unpack: 2.500s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 993.9739 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01924916-0.000644_jay _ jay_0.82223135.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n01924916-0.000644_jay _ jay_0.82223135.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01944390-0.002567_American robin _ American robin_0.5629079.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01944390-0.002567_American robin _ American robin_0.5629079.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 65,284B, BPFP=0.1239 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 151,672B, BPFP=0.2879 +⌛️ [2/4] FRONTEND: Frontend time: 2.607s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.492s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10732387 12.54866679 + layer.39.0 16.74672581 2223.86297376 + ------------------------------------------------------------------------------------- + TOTAL 8.42702484 1118.20582027 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 216956 +BPFP 0.2059 bits/point +EBPFP 0.2059 equivalent bits/point +MSE 1118.205820 +---------------------- -------------------------------------------------------- +Time: 5.151s Load: 0.052s, Pack+Encode: 2.607s, Decode+Unpack: 2.492s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1118.2058 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01944390-0.002567_American robin _ American robin_0.5629079.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n01944390-0.002567_American robin _ American robin_0.5629079.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01985128-0.001579_centipede _ centipede_0.85936093.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01985128-0.001579_centipede _ centipede_0.85936093.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 57,420B, BPFP=0.1090 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 150,112B, BPFP=0.2849 +⌛️ [2/4] FRONTEND: Frontend time: 2.611s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.488s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09645609 12.72259172 + layer.39.0 23.47999613 2083.45821186 + ------------------------------------------------------------------------------------- + TOTAL 11.78822611 1048.09040179 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 207532 +BPFP 0.1970 bits/point +EBPFP 0.1970 equivalent bits/point +MSE 1048.090402 +---------------------- -------------------------------------------------------- +Time: 5.150s Load: 0.051s, Pack+Encode: 2.611s, Decode+Unpack: 2.488s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1048.0904 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01985128-0.001579_centipede _ centipede_0.85936093.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n01985128-0.001579_centipede _ centipede_0.85936093.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02037110-0.003503_snowmobile _ snowmobile_0.5200631.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02037110-0.003503_snowmobile _ snowmobile_0.5200631.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 51,308B, BPFP=0.0974 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 142,164B, BPFP=0.2698 +⌛️ [2/4] FRONTEND: Frontend time: 2.606s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.486s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09471867 12.57904272 + layer.39.0 17.04498261 2092.36491740 + ------------------------------------------------------------------------------------- + TOTAL 8.56985064 1052.47198006 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 193472 +BPFP 0.1836 bits/point +EBPFP 0.1836 equivalent bits/point +MSE 1052.471980 +---------------------- -------------------------------------------------------- +Time: 5.143s Load: 0.051s, Pack+Encode: 2.606s, Decode+Unpack: 2.486s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1052.4720 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02037110-0.003503_snowmobile _ snowmobile_0.5200631.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n02037110-0.003503_snowmobile _ snowmobile_0.5200631.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02123394-0.015363_marmot _ marmot_0.82052565.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02123394-0.015363_marmot _ marmot_0.82052565.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 57,540B, BPFP=0.1092 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 183,888B, BPFP=0.3490 +⌛️ [2/4] FRONTEND: Frontend time: 2.593s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.493s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10209646 12.58762110 + layer.39.0 11.38238543 2759.91448008 + ------------------------------------------------------------------------------------- + TOTAL 5.74224095 1386.25105059 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 241428 +BPFP 0.2291 bits/point +EBPFP 0.2291 equivalent bits/point +MSE 1386.251051 +---------------------- -------------------------------------------------------- +Time: 5.138s Load: 0.052s, Pack+Encode: 2.593s, Decode+Unpack: 2.493s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1386.2511 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02123394-0.015363_marmot _ marmot_0.82052565.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n02123394-0.015363_marmot _ marmot_0.82052565.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02165456-0.000157_corn _ corn_0.9868978.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02165456-0.000157_corn _ corn_0.9868978.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 68,916B, BPFP=0.1308 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 174,480B, BPFP=0.3312 +⌛️ [2/4] FRONTEND: Frontend time: 2.599s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.507s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10346756 12.50691566 + layer.39.0 776.17699223 2747.18027211 + ------------------------------------------------------------------------------------- + TOTAL 388.14022989 1379.84359388 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 243396 +BPFP 0.2310 bits/point +EBPFP 0.2310 equivalent bits/point +MSE 1379.843594 +---------------------- -------------------------------------------------------- +Time: 5.167s Load: 0.061s, Pack+Encode: 2.599s, Decode+Unpack: 2.507s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1379.8436 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02165456-0.000157_corn _ corn_0.9868978.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n02165456-0.000157_corn _ corn_0.9868978.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02219486-0.000060_cliff _ cliff_0.99684334.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02219486-0.000060_cliff _ cliff_0.99684334.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 52,232B, BPFP=0.0991 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 130,820B, BPFP=0.2483 +⌛️ [2/4] FRONTEND: Frontend time: 2.591s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.495s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09584527 12.39558943 + layer.39.0 31.94620460 1715.74817784 + ------------------------------------------------------------------------------------- + TOTAL 16.02102494 864.07188364 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 183052 +BPFP 0.1737 bits/point +EBPFP 0.1737 equivalent bits/point +MSE 864.071884 +---------------------- -------------------------------------------------------- +Time: 5.147s Load: 0.061s, Pack+Encode: 2.591s, Decode+Unpack: 2.495s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 864.0719 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02219486-0.000060_cliff _ cliff_0.99684334.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n02219486-0.000060_cliff _ cliff_0.99684334.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02226429-0.000085_stick insect _ stick insect_0.99900275.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02226429-0.000085_stick insect _ stick insect_0.99900275.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 55,196B, BPFP=0.1048 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 220,976B, BPFP=0.4194 +⌛️ [2/4] FRONTEND: Frontend time: 2.598s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.483s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09547379 12.63185777 + layer.39.0 19.16722850 3073.61200194 + ------------------------------------------------------------------------------------- + TOTAL 9.63135114 1543.12192985 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 276172 +BPFP 0.2621 bits/point +EBPFP 0.2621 equivalent bits/point +MSE 1543.121930 +---------------------- -------------------------------------------------------- +Time: 5.131s Load: 0.050s, Pack+Encode: 2.598s, Decode+Unpack: 2.483s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1543.1219 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02226429-0.000085_stick insect _ stick insect_0.99900275.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n02226429-0.000085_stick insect _ stick insect_0.99900275.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02231487-0.000080_manhole cover _ manhole cover_0.7554716.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02231487-0.000080_manhole cover _ manhole cover_0.7554716.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 50,548B, BPFP=0.0959 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 177,372B, BPFP=0.3367 +⌛️ [2/4] FRONTEND: Frontend time: 2.594s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.481s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09512618 12.71610787 + layer.39.0 210.79875790 2533.70651118 + ------------------------------------------------------------------------------------- + TOTAL 105.44694204 1273.21130952 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 227920 +BPFP 0.2163 bits/point +EBPFP 0.2163 equivalent bits/point +MSE 1273.211310 +---------------------- -------------------------------------------------------- +Time: 5.125s Load: 0.051s, Pack+Encode: 2.594s, Decode+Unpack: 2.481s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1273.2113 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02231487-0.000080_manhole cover _ manhole cover_0.7554716.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n02231487-0.000080_manhole cover _ manhole cover_0.7554716.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02233338-0.002901_manhole cover _ manhole cover_0.69458175.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02233338-0.002901_manhole cover _ manhole cover_0.69458175.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 52,788B, BPFP=0.1002 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 168,872B, BPFP=0.3205 +⌛️ [2/4] FRONTEND: Frontend time: 2.613s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.482s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09539769 12.58416374 + layer.39.0 58.97704841 2347.57774538 + ------------------------------------------------------------------------------------- + TOTAL 29.53622305 1180.08095456 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 221660 +BPFP 0.2104 bits/point +EBPFP 0.2104 equivalent bits/point +MSE 1180.080955 +---------------------- -------------------------------------------------------- +Time: 5.148s Load: 0.052s, Pack+Encode: 2.613s, Decode+Unpack: 2.482s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1180.0810 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02233338-0.002901_manhole cover _ manhole cover_0.69458175.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n02233338-0.002901_manhole cover _ manhole cover_0.69458175.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02236044-0.000522_sundial _ sundial_0.96381366.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02236044-0.000522_sundial _ sundial_0.96381366.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 66,728B, BPFP=0.1267 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 189,860B, BPFP=0.3604 +⌛️ [2/4] FRONTEND: Frontend time: 2.612s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.491s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09795647 12.52655130 + layer.39.0 53.12385356 2829.22303207 + ------------------------------------------------------------------------------------- + TOTAL 26.61090502 1420.87479169 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 256588 +BPFP 0.2435 bits/point +EBPFP 0.2435 equivalent bits/point +MSE 1420.874792 +---------------------- -------------------------------------------------------- +Time: 5.163s Load: 0.060s, Pack+Encode: 2.612s, Decode+Unpack: 2.491s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1420.8748 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02236044-0.000522_sundial _ sundial_0.96381366.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n02236044-0.000522_sundial _ sundial_0.96381366.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02259212-0.000032_chain _ chain_0.6590295.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02259212-0.000032_chain _ chain_0.6590295.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 54,436B, BPFP=0.1033 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 207,160B, BPFP=0.3932 +⌛️ [2/4] FRONTEND: Frontend time: 2.591s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.506s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09523673 12.58619944 + layer.39.0 80.66082058 2981.93051506 + ------------------------------------------------------------------------------------- + TOTAL 40.37802865 1497.25835725 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 261596 +BPFP 0.2483 bits/point +EBPFP 0.2483 equivalent bits/point +MSE 1497.258357 +---------------------- -------------------------------------------------------- +Time: 5.158s Load: 0.061s, Pack+Encode: 2.591s, Decode+Unpack: 2.506s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1497.2584 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02259212-0.000032_chain _ chain_0.6590295.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n02259212-0.000032_chain _ chain_0.6590295.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02279972-0.000576_apron _ apron_0.7661352.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02279972-0.000576_apron _ apron_0.7661352.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 93,632B, BPFP=0.1777 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 201,384B, BPFP=0.3822 +⌛️ [2/4] FRONTEND: Frontend time: 2.605s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.504s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12772729 12.73755808 + layer.39.0 1038.59135083 3160.15136054 + ------------------------------------------------------------------------------------- + TOTAL 519.35953906 1586.44445931 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 295016 +BPFP 0.2800 bits/point +EBPFP 0.2800 equivalent bits/point +MSE 1586.444459 +---------------------- -------------------------------------------------------- +Time: 5.169s Load: 0.061s, Pack+Encode: 2.605s, Decode+Unpack: 2.504s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1586.4445 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02279972-0.000576_apron _ apron_0.7661352.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n02279972-0.000576_apron _ apron_0.7661352.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02280649-0.001599_custard apple _ leafhopper_0.9128452.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02280649-0.001599_custard apple _ leafhopper_0.9128452.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.068s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 51,284B, BPFP=0.0973 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 209,296B, BPFP=0.3973 +⌛️ [2/4] FRONTEND: Frontend time: 2.609s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.482s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09488542 12.70281466 + layer.39.0 1031.59973275 3358.34596696 + ------------------------------------------------------------------------------------- + TOTAL 515.84730909 1685.52439081 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 260580 +BPFP 0.2473 bits/point +EBPFP 0.2473 equivalent bits/point +MSE 1685.524391 +---------------------- -------------------------------------------------------- +Time: 5.159s Load: 0.068s, Pack+Encode: 2.609s, Decode+Unpack: 2.482s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1685.5244 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02280649-0.001599_custard apple _ leafhopper_0.9128452.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n02280649-0.001599_custard apple _ leafhopper_0.9128452.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02317335-0.033681_flatworm _ flatworm_0.6070924.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02317335-0.033681_flatworm _ flatworm_0.6070924.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 50,312B, BPFP=0.0955 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 167,740B, BPFP=0.3184 +⌛️ [2/4] FRONTEND: Frontend time: 2.613s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.479s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09575805 12.71990498 + layer.39.0 62.35741238 2738.30515063 + ------------------------------------------------------------------------------------- + TOTAL 31.22658522 1375.51252781 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 218052 +BPFP 0.2069 bits/point +EBPFP 0.2069 equivalent bits/point +MSE 1375.512528 +---------------------- -------------------------------------------------------- +Time: 5.142s Load: 0.050s, Pack+Encode: 2.613s, Decode+Unpack: 2.479s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1375.5125 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02317335-0.033681_flatworm _ flatworm_0.6070924.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n02317335-0.033681_flatworm _ flatworm_0.6070924.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02325366-0.000350_flatworm _ flatworm_0.87634724.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02325366-0.000350_flatworm _ flatworm_0.87634724.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 47,072B, BPFP=0.0893 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 148,540B, BPFP=0.2819 +⌛️ [2/4] FRONTEND: Frontend time: 2.604s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.490s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09712043 12.89114887 + layer.39.0 30.59439155 2129.75315841 + ------------------------------------------------------------------------------------- + TOTAL 15.34575599 1071.32215364 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 195612 +BPFP 0.1856 bits/point +EBPFP 0.1856 equivalent bits/point +MSE 1071.322154 +---------------------- -------------------------------------------------------- +Time: 5.146s Load: 0.052s, Pack+Encode: 2.604s, Decode+Unpack: 2.490s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1071.3222 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02325366-0.000350_flatworm _ flatworm_0.87634724.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n02325366-0.000350_flatworm _ flatworm_0.87634724.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02346627-0.011107_fountain _ skunk_0.28641737.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02346627-0.011107_fountain _ skunk_0.28641737.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.068s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 53,460B, BPFP=0.1015 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 130,484B, BPFP=0.2477 +⌛️ [2/4] FRONTEND: Frontend time: 2.599s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.485s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09705289 12.61476460 + layer.39.0 9.52721088 1711.04664723 + ------------------------------------------------------------------------------------- + TOTAL 4.81213189 861.83070592 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 183944 +BPFP 0.1746 bits/point +EBPFP 0.1746 equivalent bits/point +MSE 861.830706 +---------------------- -------------------------------------------------------- +Time: 5.152s Load: 0.068s, Pack+Encode: 2.599s, Decode+Unpack: 2.485s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 861.8307 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02346627-0.011107_fountain _ skunk_0.28641737.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n02346627-0.011107_fountain _ skunk_0.28641737.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02445715-0.036432_shovel _ tarantula_0.26785344.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02445715-0.036432_shovel _ tarantula_0.26785344.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 66,048B, BPFP=0.1254 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 129,744B, BPFP=0.2463 +⌛️ [2/4] FRONTEND: Frontend time: 2.594s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.489s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09708806 12.49157727 + layer.39.0 8.00606437 1486.62815841 + ------------------------------------------------------------------------------------- + TOTAL 4.05157622 749.55986784 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 195792 +BPFP 0.1858 bits/point +EBPFP 0.1858 equivalent bits/point +MSE 749.559868 +---------------------- -------------------------------------------------------- +Time: 5.134s Load: 0.051s, Pack+Encode: 2.594s, Decode+Unpack: 2.489s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 749.5599 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02445715-0.036432_shovel _ tarantula_0.26785344.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n02445715-0.036432_shovel _ tarantula_0.26785344.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02454379-0.082010_koala _ koala_0.7052893.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02454379-0.082010_koala _ koala_0.7052893.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 103,804B, BPFP=0.1970 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 141,508B, BPFP=0.2686 +⌛️ [2/4] FRONTEND: Frontend time: 2.587s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.499s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14585212 1.58476959 + layer.39.0 44.19989826 1932.12026239 + ------------------------------------------------------------------------------------- + TOTAL 22.17287519 966.85251599 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 245312 +BPFP 0.2328 bits/point +EBPFP 0.2328 equivalent bits/point +MSE 966.852516 +---------------------- -------------------------------------------------------- +Time: 5.138s Load: 0.052s, Pack+Encode: 2.587s, Decode+Unpack: 2.499s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 966.8525 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02454379-0.082010_koala _ koala_0.7052893.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n02454379-0.082010_koala _ koala_0.7052893.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02782093-0.007542_American alligator _ American alligator_0.49872985.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02782093-0.007542_American alligator _ American alligator_0.49872985.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 50,596B, BPFP=0.0960 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 161,296B, BPFP=0.3062 +⌛️ [2/4] FRONTEND: Frontend time: 2.587s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.480s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09848133 12.77262987 + layer.39.0 9.18780844 2117.85641399 + ------------------------------------------------------------------------------------- + TOTAL 4.64314488 1065.31452193 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 211892 +BPFP 0.2011 bits/point +EBPFP 0.2011 equivalent bits/point +MSE 1065.314522 +---------------------- -------------------------------------------------------- +Time: 5.119s Load: 0.051s, Pack+Encode: 2.587s, Decode+Unpack: 2.480s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1065.3145 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02782093-0.007542_American alligator _ American alligator_0.49872985.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n02782093-0.007542_American alligator _ American alligator_0.49872985.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02787622-0.004599_marimba _ accordion_0.25991488.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02787622-0.004599_marimba _ accordion_0.25991488.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 77,300B, BPFP=0.1467 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 250,604B, BPFP=0.4757 +⌛️ [2/4] FRONTEND: Frontend time: 2.583s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.486s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12856446 12.45485415 + layer.39.0 1004.59450923 5357.04178814 + ------------------------------------------------------------------------------------- + TOTAL 502.36153685 2684.74832115 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 327904 +BPFP 0.3112 bits/point +EBPFP 0.3112 equivalent bits/point +MSE 2684.748321 +---------------------- -------------------------------------------------------- +Time: 5.120s Load: 0.051s, Pack+Encode: 2.583s, Decode+Unpack: 2.486s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2684.7483 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02787622-0.004599_marimba _ accordion_0.25991488.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n02787622-0.004599_marimba _ accordion_0.25991488.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02793495-0.000130_flatworm _ stingray_0.5528234.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02793495-0.000130_flatworm _ stingray_0.5528234.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 52,088B, BPFP=0.0989 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 133,508B, BPFP=0.2534 +⌛️ [2/4] FRONTEND: Frontend time: 2.604s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.487s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09706621 12.77138662 + layer.39.0 8.05872662 1379.02247328 + ------------------------------------------------------------------------------------- + TOTAL 4.07789641 695.89692995 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 185596 +BPFP 0.1761 bits/point +EBPFP 0.1761 equivalent bits/point +MSE 695.896930 +---------------------- -------------------------------------------------------- +Time: 5.143s Load: 0.052s, Pack+Encode: 2.604s, Decode+Unpack: 2.487s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 695.8969 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02793495-0.000130_flatworm _ stingray_0.5528234.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n02793495-0.000130_flatworm _ stingray_0.5528234.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02797295-0.002775_hair dryer _ box turtle_0.2407761.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02797295-0.002775_hair dryer _ box turtle_0.2407761.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 71,304B, BPFP=0.1353 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 267,552B, BPFP=0.5078 +⌛️ [2/4] FRONTEND: Frontend time: 2.638s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.478s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11161610 12.49011669 + layer.39.0 373.09438776 5234.82750243 + ------------------------------------------------------------------------------------- + TOTAL 186.60300193 2623.65880956 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 338856 +BPFP 0.3216 bits/point +EBPFP 0.3216 equivalent bits/point +MSE 2623.658810 +---------------------- -------------------------------------------------------- +Time: 5.186s Load: 0.070s, Pack+Encode: 2.638s, Decode+Unpack: 2.478s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2623.6588 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02797295-0.002775_hair dryer _ box turtle_0.2407761.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n02797295-0.002775_hair dryer _ box turtle_0.2407761.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02814860-0.006340_fountain _ fountain_0.7891514.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02814860-0.006340_fountain _ fountain_0.7891514.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 54,680B, BPFP=0.1038 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 144,904B, BPFP=0.2750 +⌛️ [2/4] FRONTEND: Frontend time: 2.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.477s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.04615183 12.87181502 + layer.39.0 7.48662090 1977.30442177 + ------------------------------------------------------------------------------------- + TOTAL 7.76638637 995.08811839 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 199584 +BPFP 0.1894 bits/point +EBPFP 0.1894 equivalent bits/point +MSE 995.088118 +---------------------- -------------------------------------------------------- +Time: 5.109s Load: 0.051s, Pack+Encode: 2.581s, Decode+Unpack: 2.477s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 995.0881 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02814860-0.006340_fountain _ fountain_0.7891514.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n02814860-0.006340_fountain _ fountain_0.7891514.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02879718-0.003578_maraca _ maraca_0.6809677.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02879718-0.003578_maraca _ maraca_0.6809677.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 63,128B, BPFP=0.1198 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 288,004B, BPFP=0.5467 +⌛️ [2/4] FRONTEND: Frontend time: 2.587s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.486s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10989876 12.64719616 + layer.39.0 33.03751367 5244.58746356 + ------------------------------------------------------------------------------------- + TOTAL 16.57370621 2628.61732986 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 351132 +BPFP 0.3332 bits/point +EBPFP 0.3332 equivalent bits/point +MSE 2628.617330 +---------------------- -------------------------------------------------------- +Time: 5.124s Load: 0.051s, Pack+Encode: 2.587s, Decode+Unpack: 2.486s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2628.6173 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02879718-0.003578_maraca _ maraca_0.6809677.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n02879718-0.003578_maraca _ maraca_0.6809677.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02883205-0.000262_syringe _ syringe_0.7098205.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02883205-0.000262_syringe _ syringe_0.7098205.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 60,360B, BPFP=0.1146 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 185,756B, BPFP=0.3526 +⌛️ [2/4] FRONTEND: Frontend time: 2.574s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.482s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09610580 12.56315484 + layer.39.0 8.14318931 2181.50413022 + ------------------------------------------------------------------------------------- + TOTAL 4.11964755 1097.03364253 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 246116 +BPFP 0.2336 bits/point +EBPFP 0.2336 equivalent bits/point +MSE 1097.033643 +---------------------- -------------------------------------------------------- +Time: 5.117s Load: 0.061s, Pack+Encode: 2.574s, Decode+Unpack: 2.482s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1097.0336 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02883205-0.000262_syringe _ syringe_0.7098205.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n02883205-0.000262_syringe _ syringe_0.7098205.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02906734-0.000163_stethoscope _ stethoscope_0.9290994.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02906734-0.000163_stethoscope _ stethoscope_0.9290994.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 80,800B, BPFP=0.1534 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 227,572B, BPFP=0.4320 +⌛️ [2/4] FRONTEND: Frontend time: 2.582s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.483s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12024398 12.41563031 + layer.39.0 47.23105336 3060.81292517 + ------------------------------------------------------------------------------------- + TOTAL 23.67564867 1536.61427774 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 308372 +BPFP 0.2927 bits/point +EBPFP 0.2927 equivalent bits/point +MSE 1536.614278 +---------------------- -------------------------------------------------------- +Time: 5.116s Load: 0.051s, Pack+Encode: 2.582s, Decode+Unpack: 2.483s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1536.6143 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02906734-0.000163_stethoscope _ stethoscope_0.9290994.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n02906734-0.000163_stethoscope _ stethoscope_0.9290994.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02948072-0.002303_digital clock _ digital clock_0.928374.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02948072-0.002303_digital clock _ digital clock_0.928374.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 59,348B, BPFP=0.1126 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 223,312B, BPFP=0.4239 +⌛️ [2/4] FRONTEND: Frontend time: 2.604s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.495s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09670976 12.89123808 + layer.39.0 81.62974520 2851.36686103 + ------------------------------------------------------------------------------------- + TOTAL 40.86322748 1432.12904956 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 282660 +BPFP 0.2683 bits/point +EBPFP 0.2683 equivalent bits/point +MSE 1432.129050 +---------------------- -------------------------------------------------------- +Time: 5.150s Load: 0.052s, Pack+Encode: 2.604s, Decode+Unpack: 2.495s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1432.1290 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02948072-0.002303_digital clock _ digital clock_0.928374.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n02948072-0.002303_digital clock _ digital clock_0.928374.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02999410-0.000148_chest _ chest_0.9948565.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02999410-0.000148_chest _ chest_0.9948565.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 55,604B, BPFP=0.1055 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 155,208B, BPFP=0.2946 +⌛️ [2/4] FRONTEND: Frontend time: 2.591s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.487s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10256943 12.68742313 + layer.39.0 13.72598738 1847.34268707 + ------------------------------------------------------------------------------------- + TOTAL 6.91427841 930.01505510 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 210812 +BPFP 0.2001 bits/point +EBPFP 0.2001 equivalent bits/point +MSE 930.015055 +---------------------- -------------------------------------------------------- +Time: 5.130s Load: 0.052s, Pack+Encode: 2.591s, Decode+Unpack: 2.487s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 930.0151 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02999410-0.000148_chest _ chest_0.9948565.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n02999410-0.000148_chest _ chest_0.9948565.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03026506-0.001828_basketball _ basketball_0.6904969.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03026506-0.001828_basketball _ basketball_0.6904969.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 56,908B, BPFP=0.1080 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 203,672B, BPFP=0.3866 +⌛️ [2/4] FRONTEND: Frontend time: 2.591s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.501s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09484169 12.60067610 + layer.39.0 87.31533194 2546.57240039 + ------------------------------------------------------------------------------------- + TOTAL 43.70508681 1279.58653824 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 260580 +BPFP 0.2473 bits/point +EBPFP 0.2473 equivalent bits/point +MSE 1279.586538 +---------------------- -------------------------------------------------------- +Time: 5.144s Load: 0.052s, Pack+Encode: 2.591s, Decode+Unpack: 2.501s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1279.5865 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03026506-0.001828_basketball _ basketball_0.6904969.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n03026506-0.001828_basketball _ basketball_0.6904969.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03124043-0.001154_bow tie _ bow tie_0.9622156.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03124043-0.001154_bow tie _ bow tie_0.9622156.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 59,068B, BPFP=0.1121 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 187,400B, BPFP=0.3557 +⌛️ [2/4] FRONTEND: Frontend time: 2.596s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.488s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09893820 12.59622605 + layer.39.0 13.24554141 2862.99975705 + ------------------------------------------------------------------------------------- + TOTAL 6.67223981 1437.79799155 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 246468 +BPFP 0.2339 bits/point +EBPFP 0.2339 equivalent bits/point +MSE 1437.797992 +---------------------- -------------------------------------------------------- +Time: 5.135s Load: 0.052s, Pack+Encode: 2.596s, Decode+Unpack: 2.488s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1437.7980 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03124043-0.001154_bow tie _ bow tie_0.9622156.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n03124043-0.001154_bow tie _ bow tie_0.9622156.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03196217-0.015958_soap dispenser _ envelope_0.80274254.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03196217-0.015958_soap dispenser _ envelope_0.80274254.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 64,068B, BPFP=0.1216 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 175,836B, BPFP=0.3338 +⌛️ [2/4] FRONTEND: Frontend time: 2.593s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.485s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10340443 12.57791716 + layer.39.0 8.70910111 2678.05029155 + ------------------------------------------------------------------------------------- + TOTAL 4.40625277 1345.31410435 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 239904 +BPFP 0.2277 bits/point +EBPFP 0.2277 equivalent bits/point +MSE 1345.314104 +---------------------- -------------------------------------------------------- +Time: 5.129s Load: 0.051s, Pack+Encode: 2.593s, Decode+Unpack: 2.485s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1345.3141 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03196217-0.015958_soap dispenser _ envelope_0.80274254.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n03196217-0.015958_soap dispenser _ envelope_0.80274254.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03223299-0.001528_hot dog _ hot dog_0.9147216.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03223299-0.001528_hot dog _ hot dog_0.9147216.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 76,936B, BPFP=0.1460 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 165,184B, BPFP=0.3135 +⌛️ [2/4] FRONTEND: Frontend time: 2.602s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.485s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10130972 12.26926931 + layer.39.0 352.09596696 2548.77526725 + ------------------------------------------------------------------------------------- + TOTAL 176.09863834 1280.52226828 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 242120 +BPFP 0.2298 bits/point +EBPFP 0.2298 equivalent bits/point +MSE 1280.522268 +---------------------- -------------------------------------------------------- +Time: 5.139s Load: 0.051s, Pack+Encode: 2.602s, Decode+Unpack: 2.485s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1280.5223 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03223299-0.001528_hot dog _ hot dog_0.9147216.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n03223299-0.001528_hot dog _ hot dog_0.9147216.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03255030-0.005469_bubble _ bubble_0.9381716.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03255030-0.005469_bubble _ bubble_0.9381716.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 57,852B, BPFP=0.1098 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 262,508B, BPFP=0.4983 +⌛️ [2/4] FRONTEND: Frontend time: 2.591s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.490s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09675161 12.66391825 + layer.39.0 42.23478499 3866.14820214 + ------------------------------------------------------------------------------------- + TOTAL 21.16576830 1939.40606019 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 320360 +BPFP 0.3040 bits/point +EBPFP 0.3040 equivalent bits/point +MSE 1939.406060 +---------------------- -------------------------------------------------------- +Time: 5.141s Load: 0.061s, Pack+Encode: 2.591s, Decode+Unpack: 2.490s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1939.4061 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03255030-0.005469_bubble _ bubble_0.9381716.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n03255030-0.005469_bubble _ bubble_0.9381716.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03325584-0.000773_candle _ candle_0.810919.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03325584-0.000773_candle _ candle_0.810919.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 63,696B, BPFP=0.1209 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 271,256B, BPFP=0.5149 +⌛️ [2/4] FRONTEND: Frontend time: 2.593s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.487s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10394677 12.69588382 + layer.39.0 140.58187561 4667.44169096 + ------------------------------------------------------------------------------------- + TOTAL 70.34291119 2340.06878739 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 334952 +BPFP 0.3179 bits/point +EBPFP 0.3179 equivalent bits/point +MSE 2340.068787 +---------------------- -------------------------------------------------------- +Time: 5.132s Load: 0.052s, Pack+Encode: 2.593s, Decode+Unpack: 2.487s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2340.0688 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03325584-0.000773_candle _ candle_0.810919.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n03325584-0.000773_candle _ candle_0.810919.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03355925-0.004997_spider web _ spider web_0.9142101.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03355925-0.004997_spider web _ spider web_0.9142101.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 46,896B, BPFP=0.0890 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 124,008B, BPFP=0.2354 +⌛️ [2/4] FRONTEND: Frontend time: 2.585s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.477s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09873271 12.92563225 + layer.39.0 6.60211199 1478.78304179 + ------------------------------------------------------------------------------------- + TOTAL 3.35042235 745.85433702 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 170904 +BPFP 0.1622 bits/point +EBPFP 0.1622 equivalent bits/point +MSE 745.854337 +---------------------- -------------------------------------------------------- +Time: 5.124s Load: 0.062s, Pack+Encode: 2.585s, Decode+Unpack: 2.477s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 745.8543 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03355925-0.004997_spider web _ spider web_0.9142101.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n03355925-0.004997_spider web _ spider web_0.9142101.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03384352-0.002273_viaduct _ viaduct_0.6161024.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03384352-0.002273_viaduct _ viaduct_0.6161024.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 55,316B, BPFP=0.1050 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 175,252B, BPFP=0.3326 +⌛️ [2/4] FRONTEND: Frontend time: 2.602s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.476s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09647940 12.64633443 + layer.39.0 175.50411504 2741.54081633 + ------------------------------------------------------------------------------------- + TOTAL 87.80029722 1377.09357538 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 230568 +BPFP 0.2188 bits/point +EBPFP 0.2188 equivalent bits/point +MSE 1377.093575 +---------------------- -------------------------------------------------------- +Time: 5.149s Load: 0.071s, Pack+Encode: 2.602s, Decode+Unpack: 2.476s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1377.0936 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03384352-0.002273_viaduct _ viaduct_0.6161024.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n03384352-0.002273_viaduct _ viaduct_0.6161024.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03388043-0.005154_candle _ candle_0.9636924.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03388043-0.005154_candle _ candle_0.9636924.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 54,176B, BPFP=0.1028 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 166,872B, BPFP=0.3167 +⌛️ [2/4] FRONTEND: Frontend time: 2.608s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.480s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09640297 12.63143354 + layer.39.0 7.87377147 2017.97619048 + ------------------------------------------------------------------------------------- + TOTAL 3.98508722 1015.30381201 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 221048 +BPFP 0.2098 bits/point +EBPFP 0.2098 equivalent bits/point +MSE 1015.303812 +---------------------- -------------------------------------------------------- +Time: 5.158s Load: 0.070s, Pack+Encode: 2.608s, Decode+Unpack: 2.480s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1015.3038 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03388043-0.005154_candle _ candle_0.9636924.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n03388043-0.005154_candle _ candle_0.9636924.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03417042-0.001187_tank _ tank_0.70379025.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03417042-0.001187_tank _ tank_0.70379025.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.056s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 53,280B, BPFP=0.1011 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 200,820B, BPFP=0.3812 +⌛️ [2/4] FRONTEND: Frontend time: 2.614s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.492s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09848782 12.71298268 + layer.39.0 16.63742104 2692.50777454 + ------------------------------------------------------------------------------------- + TOTAL 8.36795443 1352.61037861 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 254100 +BPFP 0.2412 bits/point +EBPFP 0.2412 equivalent bits/point +MSE 1352.610379 +---------------------- -------------------------------------------------------- +Time: 5.162s Load: 0.056s, Pack+Encode: 2.614s, Decode+Unpack: 2.492s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1352.6104 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03417042-0.001187_tank _ tank_0.70379025.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n03417042-0.001187_tank _ tank_0.70379025.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03444034-0.002100_maraca _ maraca_0.502369.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03444034-0.002100_maraca _ maraca_0.502369.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.068s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 69,120B, BPFP=0.1312 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 314,788B, BPFP=0.5975 +⌛️ [2/4] FRONTEND: Frontend time: 2.605s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.501s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11197850 12.56266229 + layer.39.0 347.54634354 5830.42857143 + ------------------------------------------------------------------------------------- + TOTAL 173.82916102 2921.49561686 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 383908 +BPFP 0.3643 bits/point +EBPFP 0.3643 equivalent bits/point +MSE 2921.495617 +---------------------- -------------------------------------------------------- +Time: 5.173s Load: 0.068s, Pack+Encode: 2.605s, Decode+Unpack: 2.501s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2921.4956 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03444034-0.002100_maraca _ maraca_0.502369.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n03444034-0.002100_maraca _ maraca_0.502369.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03445924-0.003505_baseball player _ baseball player_0.6723684.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03445924-0.003505_baseball player _ baseball player_0.6723684.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 54,656B, BPFP=0.1037 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 228,020B, BPFP=0.4328 +⌛️ [2/4] FRONTEND: Frontend time: 2.613s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.493s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09665277 12.51841233 + layer.39.0 26.28463618 4201.14188533 + ------------------------------------------------------------------------------------- + TOTAL 13.19064447 2106.83014883 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 282676 +BPFP 0.2683 bits/point +EBPFP 0.2683 equivalent bits/point +MSE 2106.830149 +---------------------- -------------------------------------------------------- +Time: 5.177s Load: 0.071s, Pack+Encode: 2.613s, Decode+Unpack: 2.493s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2106.8301 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03445924-0.003505_baseball player _ baseball player_0.6723684.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n03445924-0.003505_baseball player _ baseball player_0.6723684.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03452741-0.002771_chain _ chain_0.9575044.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03452741-0.002771_chain _ chain_0.9575044.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 65,144B, BPFP=0.1236 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 263,220B, BPFP=0.4996 +⌛️ [2/4] FRONTEND: Frontend time: 2.604s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.514s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12351380 12.53147018 + layer.39.0 42.82565370 4527.32993197 + ------------------------------------------------------------------------------------- + TOTAL 21.47458375 2269.93070108 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 328364 +BPFP 0.3116 bits/point +EBPFP 0.3116 equivalent bits/point +MSE 2269.930701 +---------------------- -------------------------------------------------------- +Time: 5.190s Load: 0.072s, Pack+Encode: 2.604s, Decode+Unpack: 2.514s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2269.9307 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03452741-0.002771_chain _ chain_0.9575044.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n03452741-0.002771_chain _ chain_0.9575044.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03483316-0.004974_lighter _ lighter_0.27796906.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03483316-0.004974_lighter _ lighter_0.27796906.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 93,636B, BPFP=0.1777 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 210,416B, BPFP=0.3994 +⌛️ [2/4] FRONTEND: Frontend time: 2.601s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.511s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12993333 12.66277275 + layer.39.0 87.07173986 2722.90427600 + ------------------------------------------------------------------------------------- + TOTAL 43.60083660 1367.78352438 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 304052 +BPFP 0.2886 bits/point +EBPFP 0.2886 equivalent bits/point +MSE 1367.783524 +---------------------- -------------------------------------------------------- +Time: 5.182s Load: 0.070s, Pack+Encode: 2.601s, Decode+Unpack: 2.511s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1367.7835 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03483316-0.004974_lighter _ lighter_0.27796906.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n03483316-0.004974_lighter _ lighter_0.27796906.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03584829-0.104805_golf cart _ golf cart_0.73568964.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03584829-0.104805_golf cart _ golf cart_0.73568964.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 65,212B, BPFP=0.1238 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 204,944B, BPFP=0.3890 +⌛️ [2/4] FRONTEND: Frontend time: 2.599s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.497s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09917131 12.55483365 + layer.39.0 24.34873246 2891.65257532 + ------------------------------------------------------------------------------------- + TOTAL 12.22395189 1452.10370448 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 270156 +BPFP 0.2564 bits/point +EBPFP 0.2564 equivalent bits/point +MSE 1452.103704 +---------------------- -------------------------------------------------------- +Time: 5.166s Load: 0.070s, Pack+Encode: 2.599s, Decode+Unpack: 2.497s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1452.1037 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03584829-0.104805_golf cart _ golf cart_0.73568964.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n03584829-0.104805_golf cart _ golf cart_0.73568964.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03590841-0.001115_rocking chair _ rocking chair_0.2192492.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03590841-0.001115_rocking chair _ rocking chair_0.2192492.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 75,620B, BPFP=0.1435 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 153,744B, BPFP=0.2918 +⌛️ [2/4] FRONTEND: Frontend time: 2.593s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.491s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11329899 12.77945631 + layer.39.0 19.97532495 2211.01020408 + ------------------------------------------------------------------------------------- + TOTAL 10.04431197 1111.89483020 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 229364 +BPFP 0.2177 bits/point +EBPFP 0.2177 equivalent bits/point +MSE 1111.894830 +---------------------- -------------------------------------------------------- +Time: 5.153s Load: 0.069s, Pack+Encode: 2.593s, Decode+Unpack: 2.491s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1111.8948 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03590841-0.001115_rocking chair _ rocking chair_0.2192492.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n03590841-0.001115_rocking chair _ rocking chair_0.2192492.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03617480-0.003238_basketball _ basketball_0.67568874.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03617480-0.003238_basketball _ basketball_0.67568874.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 81,728B, BPFP=0.1551 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 291,064B, BPFP=0.5525 +⌛️ [2/4] FRONTEND: Frontend time: 2.622s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.504s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12967051 12.49726581 + layer.39.0 57.10576865 4903.43926142 + ------------------------------------------------------------------------------------- + TOTAL 28.61771958 2457.96826362 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 372792 +BPFP 0.3538 bits/point +EBPFP 0.3538 equivalent bits/point +MSE 2457.968264 +---------------------- -------------------------------------------------------- +Time: 5.177s Load: 0.050s, Pack+Encode: 2.622s, Decode+Unpack: 2.504s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2457.9683 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03617480-0.003238_basketball _ basketball_0.67568874.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n03617480-0.003238_basketball _ basketball_0.67568874.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03666591-0.004622_torch _ torch_0.99906796.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03666591-0.004622_torch _ torch_0.99906796.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 53,536B, BPFP=0.1016 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 155,892B, BPFP=0.2959 +⌛️ [2/4] FRONTEND: Frontend time: 2.601s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.489s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.05477861 12.86803594 + layer.39.0 7.78975672 1945.79774052 + ------------------------------------------------------------------------------------- + TOTAL 7.92226767 979.33288823 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 209428 +BPFP 0.1988 bits/point +EBPFP 0.1988 equivalent bits/point +MSE 979.332888 +---------------------- -------------------------------------------------------- +Time: 5.150s Load: 0.059s, Pack+Encode: 2.601s, Decode+Unpack: 2.489s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 979.3329 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03666591-0.004622_torch _ torch_0.99906796.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n03666591-0.004622_torch _ torch_0.99906796.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03670208-0.003899_hair dryer _ grand piano_0.7359066.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03670208-0.003899_hair dryer _ grand piano_0.7359066.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 69,372B, BPFP=0.1317 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 264,456B, BPFP=0.5020 +⌛️ [2/4] FRONTEND: Frontend time: 2.589s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.505s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11232473 12.68414135 + layer.39.0 36.60432231 4085.60932945 + ------------------------------------------------------------------------------------- + TOTAL 18.35832352 2049.14673540 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 333828 +BPFP 0.3168 bits/point +EBPFP 0.3168 equivalent bits/point +MSE 2049.146735 +---------------------- -------------------------------------------------------- +Time: 5.164s Load: 0.070s, Pack+Encode: 2.589s, Decode+Unpack: 2.505s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2049.1467 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03670208-0.003899_hair dryer _ grand piano_0.7359066.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n03670208-0.003899_hair dryer _ grand piano_0.7359066.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03717622-0.001175_sundial _ sundial_0.9998197.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03717622-0.001175_sundial _ sundial_0.9998197.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.068s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 74,084B, BPFP=0.1406 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 281,672B, BPFP=0.5346 +⌛️ [2/4] FRONTEND: Frontend time: 2.621s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.499s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13381931 12.70387189 + layer.39.0 773.52204810 4703.46938776 + ------------------------------------------------------------------------------------- + TOTAL 386.82793371 2358.08662982 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 355756 +BPFP 0.3376 bits/point +EBPFP 0.3376 equivalent bits/point +MSE 2358.086630 +---------------------- -------------------------------------------------------- +Time: 5.188s Load: 0.068s, Pack+Encode: 2.621s, Decode+Unpack: 2.499s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2358.0866 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03717622-0.001175_sundial _ sundial_0.9998197.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n03717622-0.001175_sundial _ sundial_0.9998197.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03720891-0.001854_African bush elephant _ African bush elephant_0.722115.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03720891-0.001854_African bush elephant _ African bush elephant_0.722115.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 52,632B, BPFP=0.0999 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 308,528B, BPFP=0.5856 +⌛️ [2/4] FRONTEND: Frontend time: 2.604s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.494s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09642763 12.50180318 + layer.39.0 155.23232507 5279.39650146 + ------------------------------------------------------------------------------------- + TOTAL 77.66437635 2645.94915232 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 361160 +BPFP 0.3428 bits/point +EBPFP 0.3428 equivalent bits/point +MSE 2645.949152 +---------------------- -------------------------------------------------------- +Time: 5.168s Load: 0.069s, Pack+Encode: 2.604s, Decode+Unpack: 2.494s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2645.9492 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03720891-0.001854_African bush elephant _ African bush elephant_0.722115.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n03720891-0.001854_African bush elephant _ African bush elephant_0.722115.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03721384-0.003327_chain _ chain_0.5599652.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03721384-0.003327_chain _ chain_0.5599652.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 57,932B, BPFP=0.1100 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 175,156B, BPFP=0.3325 +⌛️ [2/4] FRONTEND: Frontend time: 2.592s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.500s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09561452 12.58775491 + layer.39.0 742.66502672 3005.16520894 + ------------------------------------------------------------------------------------- + TOTAL 371.38032062 1508.87648193 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 233088 +BPFP 0.2212 bits/point +EBPFP 0.2212 equivalent bits/point +MSE 1508.876482 +---------------------- -------------------------------------------------------- +Time: 5.161s Load: 0.070s, Pack+Encode: 2.592s, Decode+Unpack: 2.500s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1508.8765 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03721384-0.003327_chain _ chain_0.5599652.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n03721384-0.003327_chain _ chain_0.5599652.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03724870-0.001366_Christmas stocking _ Christmas stocking_0.5709616.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03724870-0.001366_Christmas stocking _ Christmas stocking_0.5709616.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 70,540B, BPFP=0.1339 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 212,876B, BPFP=0.4041 +⌛️ [2/4] FRONTEND: Frontend time: 2.586s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.506s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10329660 12.54127756 + layer.39.0 513.92243683 2924.06802721 + ------------------------------------------------------------------------------------- + TOTAL 257.01286671 1468.30465239 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 283416 +BPFP 0.2690 bits/point +EBPFP 0.2690 equivalent bits/point +MSE 1468.304652 +---------------------- -------------------------------------------------------- +Time: 5.143s Load: 0.050s, Pack+Encode: 2.586s, Decode+Unpack: 2.506s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1468.3047 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03724870-0.001366_Christmas stocking _ Christmas stocking_0.5709616.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n03724870-0.001366_Christmas stocking _ Christmas stocking_0.5709616.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03775071-0.000145_Christmas stocking _ Christmas stocking_0.9986047.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03775071-0.000145_Christmas stocking _ Christmas stocking_0.9986047.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 58,600B, BPFP=0.1112 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 235,424B, BPFP=0.4469 +⌛️ [2/4] FRONTEND: Frontend time: 2.603s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.492s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09700392 12.65961435 + layer.39.0 284.92189018 3589.58114674 + ------------------------------------------------------------------------------------- + TOTAL 142.50944705 1801.12038055 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 294024 +BPFP 0.2790 bits/point +EBPFP 0.2790 equivalent bits/point +MSE 1801.120381 +---------------------- -------------------------------------------------------- +Time: 5.164s Load: 0.069s, Pack+Encode: 2.603s, Decode+Unpack: 2.492s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1801.1204 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03775071-0.000145_Christmas stocking _ Christmas stocking_0.9986047.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n03775071-0.000145_Christmas stocking _ Christmas stocking_0.9986047.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03788195-0.005975_steam locomotive _ steam locomotive_0.42419377.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03788195-0.005975_steam locomotive _ steam locomotive_0.42419377.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 67,768B, BPFP=0.1286 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 172,512B, BPFP=0.3274 +⌛️ [2/4] FRONTEND: Frontend time: 2.587s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.475s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10790903 12.68015633 + layer.39.0 10.34781284 2510.95748299 + ------------------------------------------------------------------------------------- + TOTAL 5.22786094 1261.81881966 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 240280 +BPFP 0.2280 bits/point +EBPFP 0.2280 equivalent bits/point +MSE 1261.818820 +---------------------- -------------------------------------------------------- +Time: 5.132s Load: 0.070s, Pack+Encode: 2.587s, Decode+Unpack: 2.475s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1261.8188 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03788195-0.005975_steam locomotive _ steam locomotive_0.42419377.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n03788195-0.005975_steam locomotive _ steam locomotive_0.42419377.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03837869-0.002023_lighthouse _ lighthouse_0.5898798.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03837869-0.002023_lighthouse _ lighthouse_0.5898798.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 69,624B, BPFP=0.1322 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 153,552B, BPFP=0.2915 +⌛️ [2/4] FRONTEND: Frontend time: 2.597s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.498s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12703056 12.68252608 + layer.39.0 141.21340500 2371.83965015 + ------------------------------------------------------------------------------------- + TOTAL 70.67021778 1192.26108811 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 223176 +BPFP 0.2118 bits/point +EBPFP 0.2118 equivalent bits/point +MSE 1192.261088 +---------------------- -------------------------------------------------------- +Time: 5.167s Load: 0.072s, Pack+Encode: 2.597s, Decode+Unpack: 2.498s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1192.2611 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03837869-0.002023_lighthouse _ lighthouse_0.5898798.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n03837869-0.002023_lighthouse _ lighthouse_0.5898798.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03840681-0.002188_ladybug _ ladybug_0.9972365.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03840681-0.002188_ladybug _ ladybug_0.9972365.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 48,476B, BPFP=0.0920 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 145,236B, BPFP=0.2757 +⌛️ [2/4] FRONTEND: Frontend time: 2.592s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.491s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09487485 12.65793834 + layer.39.0 29.40353574 1906.54737609 + ------------------------------------------------------------------------------------- + TOTAL 14.74920530 959.60265722 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 193712 +BPFP 0.1838 bits/point +EBPFP 0.1838 equivalent bits/point +MSE 959.602657 +---------------------- -------------------------------------------------------- +Time: 5.152s Load: 0.069s, Pack+Encode: 2.592s, Decode+Unpack: 2.491s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 959.6027 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03840681-0.002188_ladybug _ ladybug_0.9972365.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n03840681-0.002188_ladybug _ ladybug_0.9972365.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03888257-0.004083_rugby ball _ snail_0.41588444.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03888257-0.004083_rugby ball _ snail_0.41588444.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 54,924B, BPFP=0.1043 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 148,716B, BPFP=0.2823 +⌛️ [2/4] FRONTEND: Frontend time: 2.604s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.489s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10005040 12.73530886 + layer.39.0 7.47115060 1661.62767250 + ------------------------------------------------------------------------------------- + TOTAL 3.78560050 837.18149068 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 203640 +BPFP 0.1933 bits/point +EBPFP 0.1933 equivalent bits/point +MSE 837.181491 +---------------------- -------------------------------------------------------- +Time: 5.163s Load: 0.070s, Pack+Encode: 2.604s, Decode+Unpack: 2.489s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 837.1815 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03888257-0.004083_rugby ball _ snail_0.41588444.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n03888257-0.004083_rugby ball _ snail_0.41588444.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03891332-0.003727_syringe _ syringe_0.93799996.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03891332-0.003727_syringe _ syringe_0.93799996.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.068s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 54,644B, BPFP=0.1037 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 236,628B, BPFP=0.4491 +⌛️ [2/4] FRONTEND: Frontend time: 2.599s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.506s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09617506 12.64324625 + layer.39.0 18.45312310 3762.33163265 + ------------------------------------------------------------------------------------- + TOTAL 9.27464908 1887.48743945 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 291272 +BPFP 0.2764 bits/point +EBPFP 0.2764 equivalent bits/point +MSE 1887.487439 +---------------------- -------------------------------------------------------- +Time: 5.174s Load: 0.068s, Pack+Encode: 2.599s, Decode+Unpack: 2.506s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1887.4874 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03891332-0.003727_syringe _ syringe_0.93799996.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n03891332-0.003727_syringe _ syringe_0.93799996.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03982430-0.005102_couch _ couch_0.9976859.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03982430-0.005102_couch _ couch_0.9976859.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 51,420B, BPFP=0.0976 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 170,796B, BPFP=0.3242 +⌛️ [2/4] FRONTEND: Frontend time: 2.567s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.493s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09691652 12.63362298 + layer.39.0 169.89398081 2510.61175899 + ------------------------------------------------------------------------------------- + TOTAL 84.99544866 1261.62269098 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 222216 +BPFP 0.2109 bits/point +EBPFP 0.2109 equivalent bits/point +MSE 1261.622691 +---------------------- -------------------------------------------------------- +Time: 5.131s Load: 0.071s, Pack+Encode: 2.567s, Decode+Unpack: 2.493s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1261.6227 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03982430-0.005102_couch _ couch_0.9976859.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n03982430-0.005102_couch _ couch_0.9976859.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04033901-0.007476_envelope _ envelope_0.9990971.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04033901-0.007476_envelope _ envelope_0.9990971.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 52,260B, BPFP=0.0992 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 157,512B, BPFP=0.2990 +⌛️ [2/4] FRONTEND: Frontend time: 2.598s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.487s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10364226 12.78622961 + layer.39.0 7.34252906 2034.23044218 + ------------------------------------------------------------------------------------- + TOTAL 3.72308566 1023.50833590 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 209772 +BPFP 0.1991 bits/point +EBPFP 0.1991 equivalent bits/point +MSE 1023.508336 +---------------------- -------------------------------------------------------- +Time: 5.156s Load: 0.071s, Pack+Encode: 2.598s, Decode+Unpack: 2.487s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1023.5083 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04033901-0.007476_envelope _ envelope_0.9990971.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n04033901-0.007476_envelope _ envelope_0.9990971.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04039381-0.000054_hair dryer _ hair dryer_0.5667548.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04039381-0.000054_hair dryer _ hair dryer_0.5667548.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 52,228B, BPFP=0.0991 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 223,856B, BPFP=0.4249 +⌛️ [2/4] FRONTEND: Frontend time: 2.587s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.480s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09588603 12.74556419 + layer.39.0 26.21653304 3110.62755102 + ------------------------------------------------------------------------------------- + TOTAL 13.15620954 1561.68655760 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 276084 +BPFP 0.2620 bits/point +EBPFP 0.2620 equivalent bits/point +MSE 1561.686558 +---------------------- -------------------------------------------------------- +Time: 5.137s Load: 0.070s, Pack+Encode: 2.587s, Decode+Unpack: 2.480s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1561.6866 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04039381-0.000054_hair dryer _ hair dryer_0.5667548.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n04039381-0.000054_hair dryer _ hair dryer_0.5667548.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04118538-0.005804_cowboy boot _ cowboy boot_0.21208441.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04118538-0.005804_cowboy boot _ cowboy boot_0.21208441.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 64,280B, BPFP=0.1220 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 174,132B, BPFP=0.3305 +⌛️ [2/4] FRONTEND: Frontend time: 2.584s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.496s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09664223 12.39545846 + layer.39.0 8.64007266 2199.55150632 + ------------------------------------------------------------------------------------- + TOTAL 4.36835744 1105.97348239 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 238412 +BPFP 0.2263 bits/point +EBPFP 0.2263 equivalent bits/point +MSE 1105.973482 +---------------------- -------------------------------------------------------- +Time: 5.151s Load: 0.071s, Pack+Encode: 2.584s, Decode+Unpack: 2.496s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1105.9735 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04118538-0.005804_cowboy boot _ cowboy boot_0.21208441.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n04118538-0.005804_cowboy boot _ cowboy boot_0.21208441.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04146614-0.008793_marimba _ marimba_0.54555196.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04146614-0.008793_marimba _ marimba_0.54555196.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 70,532B, BPFP=0.1339 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 217,396B, BPFP=0.4126 +⌛️ [2/4] FRONTEND: Frontend time: 2.602s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.501s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09774729 12.59136696 + layer.39.0 155.07908163 4042.69800777 + ------------------------------------------------------------------------------------- + TOTAL 77.58841446 2027.64468737 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 287928 +BPFP 0.2733 bits/point +EBPFP 0.2733 equivalent bits/point +MSE 2027.644687 +---------------------- -------------------------------------------------------- +Time: 5.172s Load: 0.070s, Pack+Encode: 2.602s, Decode+Unpack: 2.501s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2027.6447 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04146614-0.008793_marimba _ marimba_0.54555196.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n04146614-0.008793_marimba _ marimba_0.54555196.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04179913-0.006024_guacamole _ custard apple_0.5550306.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04179913-0.006024_guacamole _ custard apple_0.5550306.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 72,408B, BPFP=0.1374 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 240,764B, BPFP=0.4570 +⌛️ [2/4] FRONTEND: Frontend time: 2.597s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.494s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11409367 12.60182918 + layer.39.0 68.43204871 3259.58989310 + ------------------------------------------------------------------------------------- + TOTAL 34.27307119 1636.09586114 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 313172 +BPFP 0.2972 bits/point +EBPFP 0.2972 equivalent bits/point +MSE 1636.095861 +---------------------- -------------------------------------------------------- +Time: 5.161s Load: 0.070s, Pack+Encode: 2.597s, Decode+Unpack: 2.494s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1636.0959 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04179913-0.006024_guacamole _ custard apple_0.5550306.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n04179913-0.006024_guacamole _ custard apple_0.5550306.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04208210-0.007213_doormat _ doormat_0.81736016.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04208210-0.007213_doormat _ doormat_0.81736016.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 76,008B, BPFP=0.1443 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 189,536B, BPFP=0.3598 +⌛️ [2/4] FRONTEND: Frontend time: 2.626s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.499s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10601767 12.45671996 + layer.39.0 349.44518343 2988.01239067 + ------------------------------------------------------------------------------------- + TOTAL 174.77560055 1500.23455532 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 265544 +BPFP 0.2520 bits/point +EBPFP 0.2520 equivalent bits/point +MSE 1500.234555 +---------------------- -------------------------------------------------------- +Time: 5.195s Load: 0.070s, Pack+Encode: 2.626s, Decode+Unpack: 2.499s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1500.2346 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04208210-0.007213_doormat _ doormat_0.81736016.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n04208210-0.007213_doormat _ doormat_0.81736016.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04270147-0.000435_rotary dial telephone _ rotary dial telephone_0.9268941.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04270147-0.000435_rotary dial telephone _ rotary dial telephone_0.9268941.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 55,896B, BPFP=0.1061 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 230,324B, BPFP=0.4372 +⌛️ [2/4] FRONTEND: Frontend time: 2.612s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.487s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09464848 12.60134042 + layer.39.0 229.78908528 3072.12487852 + ------------------------------------------------------------------------------------- + TOTAL 114.94186688 1542.36310947 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 286220 +BPFP 0.2716 bits/point +EBPFP 0.2716 equivalent bits/point +MSE 1542.363109 +---------------------- -------------------------------------------------------- +Time: 5.171s Load: 0.072s, Pack+Encode: 2.612s, Decode+Unpack: 2.487s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1542.3631 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04270147-0.000435_rotary dial telephone _ rotary dial telephone_0.9268941.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n04270147-0.000435_rotary dial telephone _ rotary dial telephone_0.9268941.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04317175-0.006894_bow tie _ bow tie_0.95877784.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04317175-0.006894_bow tie _ bow tie_0.95877784.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 59,296B, BPFP=0.1125 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 271,220B, BPFP=0.5148 +⌛️ [2/4] FRONTEND: Frontend time: 2.613s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.508s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09706025 12.77876541 + layer.39.0 10.87108806 4024.95578231 + ------------------------------------------------------------------------------------- + TOTAL 5.48407415 2018.86727386 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 330516 +BPFP 0.3137 bits/point +EBPFP 0.3137 equivalent bits/point +MSE 2018.867274 +---------------------- -------------------------------------------------------- +Time: 5.189s Load: 0.069s, Pack+Encode: 2.613s, Decode+Unpack: 2.508s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2018.8673 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04317175-0.006894_bow tie _ bow tie_0.95877784.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n04317175-0.006894_bow tie _ bow tie_0.95877784.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04347754-0.001405_viaduct _ viaduct_0.8445672.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04347754-0.001405_viaduct _ viaduct_0.8445672.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 52,416B, BPFP=0.0995 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 193,696B, BPFP=0.3677 +⌛️ [2/4] FRONTEND: Frontend time: 2.599s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.504s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09586499 12.77674206 + layer.39.0 267.55718537 2755.94290573 + ------------------------------------------------------------------------------------- + TOTAL 133.82652518 1384.35982390 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 246112 +BPFP 0.2336 bits/point +EBPFP 0.2336 equivalent bits/point +MSE 1384.359824 +---------------------- -------------------------------------------------------- +Time: 5.173s Load: 0.070s, Pack+Encode: 2.599s, Decode+Unpack: 2.504s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1384.3598 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04347754-0.001405_viaduct _ viaduct_0.8445672.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n04347754-0.001405_viaduct _ viaduct_0.8445672.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04355338-0.000791_envelope _ envelope_0.9969598.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04355338-0.000791_envelope _ envelope_0.9969598.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 60,696B, BPFP=0.1152 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 199,624B, BPFP=0.3789 +⌛️ [2/4] FRONTEND: Frontend time: 2.609s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.511s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10273007 12.65999586 + layer.39.0 331.89978134 2813.00680272 + ------------------------------------------------------------------------------------- + TOTAL 166.00125571 1412.83339929 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 260320 +BPFP 0.2471 bits/point +EBPFP 0.2471 equivalent bits/point +MSE 1412.833399 +---------------------- -------------------------------------------------------- +Time: 5.190s Load: 0.070s, Pack+Encode: 2.609s, Decode+Unpack: 2.511s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1412.8334 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04355338-0.000791_envelope _ envelope_0.9969598.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n04355338-0.000791_envelope _ envelope_0.9969598.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04366367-0.002021_parachute _ parachute_0.9226023.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04366367-0.002021_parachute _ parachute_0.9226023.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 62,544B, BPFP=0.1187 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 132,920B, BPFP=0.2523 +⌛️ [2/4] FRONTEND: Frontend time: 2.602s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.490s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09577132 12.66372085 + layer.39.0 47.60657343 1845.53194849 + ------------------------------------------------------------------------------------- + TOTAL 23.85117238 929.09783467 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 195464 +BPFP 0.1855 bits/point +EBPFP 0.1855 equivalent bits/point +MSE 929.097835 +---------------------- -------------------------------------------------------- +Time: 5.162s Load: 0.070s, Pack+Encode: 2.602s, Decode+Unpack: 2.490s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 929.0978 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04366367-0.002021_parachute _ parachute_0.9226023.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n04366367-0.002021_parachute _ parachute_0.9226023.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04376876-0.004615_lighter _ lighter_0.69176143.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04376876-0.004615_lighter _ lighter_0.69176143.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 59,568B, BPFP=0.1131 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 220,700B, BPFP=0.4189 +⌛️ [2/4] FRONTEND: Frontend time: 2.598s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.483s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09912059 12.77267542 + layer.39.0 173.01079628 2890.58819242 + ------------------------------------------------------------------------------------- + TOTAL 86.55495844 1451.68043392 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 280268 +BPFP 0.2660 bits/point +EBPFP 0.2660 equivalent bits/point +MSE 1451.680434 +---------------------- -------------------------------------------------------- +Time: 5.150s Load: 0.069s, Pack+Encode: 2.598s, Decode+Unpack: 2.483s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1451.6804 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04376876-0.004615_lighter _ lighter_0.69176143.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n04376876-0.004615_lighter _ lighter_0.69176143.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04442312-0.001690_washing machine _ washing machine_0.8494714.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04442312-0.001690_washing machine _ washing machine_0.8494714.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 53,736B, BPFP=0.1020 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 170,728B, BPFP=0.3241 +⌛️ [2/4] FRONTEND: Frontend time: 2.595s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.497s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.08302300 12.87926878 + layer.39.0 28.24609944 2512.72278912 + ------------------------------------------------------------------------------------- + TOTAL 18.16456122 1262.80102895 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 224464 +BPFP 0.2130 bits/point +EBPFP 0.2130 equivalent bits/point +MSE 1262.801029 +---------------------- -------------------------------------------------------- +Time: 5.161s Load: 0.069s, Pack+Encode: 2.595s, Decode+Unpack: 2.497s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1262.8010 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04442312-0.001690_washing machine _ washing machine_0.8494714.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n04442312-0.001690_washing machine _ washing machine_0.8494714.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04456115-0.002573_hair dryer _ envelope_0.34823084.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04456115-0.002573_hair dryer _ envelope_0.34823084.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 52,896B, BPFP=0.1004 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 206,080B, BPFP=0.3912 +⌛️ [2/4] FRONTEND: Frontend time: 2.596s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.483s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09444211 12.57532723 + layer.39.0 8.80792942 2908.69800777 + ------------------------------------------------------------------------------------- + TOTAL 4.45118577 1460.63666750 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 258976 +BPFP 0.2458 bits/point +EBPFP 0.2458 equivalent bits/point +MSE 1460.636668 +---------------------- -------------------------------------------------------- +Time: 5.149s Load: 0.069s, Pack+Encode: 2.596s, Decode+Unpack: 2.483s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1460.6367 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04456115-0.002573_hair dryer _ envelope_0.34823084.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n04456115-0.002573_hair dryer _ envelope_0.34823084.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04509417-0.000350_sundial _ sundial_0.99936897.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04509417-0.000350_sundial _ sundial_0.99936897.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 61,380B, BPFP=0.1165 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 161,492B, BPFP=0.3065 +⌛️ [2/4] FRONTEND: Frontend time: 2.598s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.507s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10319057 12.59509004 + layer.39.0 8.14296913 2068.88143829 + ------------------------------------------------------------------------------------- + TOTAL 4.12307985 1040.73826417 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 222872 +BPFP 0.2115 bits/point +EBPFP 0.2115 equivalent bits/point +MSE 1040.738264 +---------------------- -------------------------------------------------------- +Time: 5.175s Load: 0.070s, Pack+Encode: 2.598s, Decode+Unpack: 2.507s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1040.7383 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04509417-0.000350_sundial _ sundial_0.99936897.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n04509417-0.000350_sundial _ sundial_0.99936897.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04532670-0.000670_spider web _ spider web_0.70711935.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04532670-0.000670_spider web _ spider web_0.70711935.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.068s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 55,868B, BPFP=0.1060 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 183,932B, BPFP=0.3491 +⌛️ [2/4] FRONTEND: Frontend time: 2.587s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.495s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09618602 12.39158353 + layer.39.0 175.41615039 2493.65427600 + ------------------------------------------------------------------------------------- + TOTAL 87.75616821 1253.02292976 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 239800 +BPFP 0.2276 bits/point +EBPFP 0.2276 equivalent bits/point +MSE 1253.022930 +---------------------- -------------------------------------------------------- +Time: 5.150s Load: 0.068s, Pack+Encode: 2.587s, Decode+Unpack: 2.495s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1253.0229 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04532670-0.000670_spider web _ spider web_0.70711935.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n04532670-0.000670_spider web _ spider web_0.70711935.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04540053-0.000734_balance beam _ balance beam_0.99765223.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04540053-0.000734_balance beam _ balance beam_0.99765223.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 48,056B, BPFP=0.0912 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 161,052B, BPFP=0.3057 +⌛️ [2/4] FRONTEND: Frontend time: 2.594s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.505s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09941827 12.83010849 + layer.39.0 8.11341412 2317.14480078 + ------------------------------------------------------------------------------------- + TOTAL 4.10641619 1164.98745464 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 209108 +BPFP 0.1985 bits/point +EBPFP 0.1985 equivalent bits/point +MSE 1164.987455 +---------------------- -------------------------------------------------------- +Time: 5.169s Load: 0.070s, Pack+Encode: 2.594s, Decode+Unpack: 2.505s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1164.9875 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04540053-0.000734_balance beam _ balance beam_0.99765223.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n04540053-0.000734_balance beam _ balance beam_0.99765223.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04606251-0.003483_cliff _ cliff_0.9972174.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04606251-0.003483_cliff _ cliff_0.9972174.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.073s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 65,360B, BPFP=0.1241 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 158,432B, BPFP=0.3007 +⌛️ [2/4] FRONTEND: Frontend time: 2.588s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.478s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09940710 12.52432485 + layer.39.0 906.86880466 2485.65864917 + ------------------------------------------------------------------------------------- + TOTAL 453.48410588 1249.09148701 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 223792 +BPFP 0.2124 bits/point +EBPFP 0.2124 equivalent bits/point +MSE 1249.091487 +---------------------- -------------------------------------------------------- +Time: 5.139s Load: 0.073s, Pack+Encode: 2.588s, Decode+Unpack: 2.478s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1249.0915 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04606251-0.003483_cliff _ cliff_0.9972174.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n04606251-0.003483_cliff _ cliff_0.9972174.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07583066-0.003935_maraca _ maraca_0.5370013.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07583066-0.003935_maraca _ maraca_0.5370013.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 67,084B, BPFP=0.1273 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 169,484B, BPFP=0.3217 +⌛️ [2/4] FRONTEND: Frontend time: 2.592s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.498s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12045678 12.62073596 + layer.39.0 38.29438092 3008.20383868 + ------------------------------------------------------------------------------------- + TOTAL 19.20741885 1510.41228732 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 236568 +BPFP 0.2245 bits/point +EBPFP 0.2245 equivalent bits/point +MSE 1510.412287 +---------------------- -------------------------------------------------------- +Time: 5.161s Load: 0.070s, Pack+Encode: 2.592s, Decode+Unpack: 2.498s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1510.4123 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07583066-0.003935_maraca _ maraca_0.5370013.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n07583066-0.003935_maraca _ maraca_0.5370013.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07695742-0.003226_ant _ ant_0.64413536.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07695742-0.003226_ant _ ant_0.64413536.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 93,192B, BPFP=0.1769 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 206,632B, BPFP=0.3922 +⌛️ [2/4] FRONTEND: Frontend time: 2.610s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.501s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.16263347 12.64107295 + layer.39.0 172.10254191 3090.72643343 + ------------------------------------------------------------------------------------- + TOTAL 86.13258769 1551.68375319 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 299824 +BPFP 0.2845 bits/point +EBPFP 0.2845 equivalent bits/point +MSE 1551.683753 +---------------------- -------------------------------------------------------- +Time: 5.183s Load: 0.071s, Pack+Encode: 2.610s, Decode+Unpack: 2.501s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1551.6838 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07695742-0.003226_ant _ ant_0.64413536.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n07695742-0.003226_ant _ ant_0.64413536.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07718472-0.001330_spatula _ spatula_0.5388231.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07718472-0.001330_spatula _ spatula_0.5388231.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 69,528B, BPFP=0.1320 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 234,504B, BPFP=0.4451 +⌛️ [2/4] FRONTEND: Frontend time: 2.623s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.512s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09672572 12.61952783 + layer.39.0 34.52145211 3854.76773567 + ------------------------------------------------------------------------------------- + TOTAL 17.30908891 1933.69363175 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 304032 +BPFP 0.2885 bits/point +EBPFP 0.2885 equivalent bits/point +MSE 1933.693632 +---------------------- -------------------------------------------------------- +Time: 5.205s Load: 0.070s, Pack+Encode: 2.623s, Decode+Unpack: 2.512s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1933.6936 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07718472-0.001330_spatula _ spatula_0.5388231.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n07718472-0.001330_spatula _ spatula_0.5388231.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07831146-0.002710_guacamole _ guacamole_0.99510396.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07831146-0.002710_guacamole _ guacamole_0.99510396.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 64,568B, BPFP=0.1226 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 259,100B, BPFP=0.4918 +⌛️ [2/4] FRONTEND: Frontend time: 2.611s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.492s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09717902 12.57704120 + layer.39.0 26.55584533 4055.23615160 + ------------------------------------------------------------------------------------- + TOTAL 13.32651218 2033.90659640 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 323668 +BPFP 0.3072 bits/point +EBPFP 0.3072 equivalent bits/point +MSE 2033.906596 +---------------------- -------------------------------------------------------- +Time: 5.173s Load: 0.070s, Pack+Encode: 2.611s, Decode+Unpack: 2.492s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2033.9066 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07831146-0.002710_guacamole _ guacamole_0.99510396.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n07831146-0.002710_guacamole _ guacamole_0.99510396.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n09229709-0.002628_stethoscope _ stethoscope_0.9726458.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n09229709-0.002628_stethoscope _ stethoscope_0.9726458.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 59,652B, BPFP=0.1132 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 165,888B, BPFP=0.3149 +⌛️ [2/4] FRONTEND: Frontend time: 2.585s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.495s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10247729 12.57950111 + layer.39.0 58.71458181 2138.43464529 + ------------------------------------------------------------------------------------- + TOTAL 29.40852955 1075.50707320 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 225540 +BPFP 0.2140 bits/point +EBPFP 0.2140 equivalent bits/point +MSE 1075.507073 +---------------------- -------------------------------------------------------- +Time: 5.152s Load: 0.072s, Pack+Encode: 2.585s, Decode+Unpack: 2.495s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1075.5071 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n09229709-0.002628_stethoscope _ stethoscope_0.9726458.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n09229709-0.002628_stethoscope _ stethoscope_0.9726458.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12057211-0.000404_nail _ newt_0.31321314.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12057211-0.000404_nail _ newt_0.31321314.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 90,300B, BPFP=0.1714 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 147,812B, BPFP=0.2806 +⌛️ [2/4] FRONTEND: Frontend time: 2.605s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.507s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11577855 12.51137140 + layer.39.0 8.72387956 1987.41484451 + ------------------------------------------------------------------------------------- + TOTAL 4.41982905 999.96310796 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 238112 +BPFP 0.2260 bits/point +EBPFP 0.2260 equivalent bits/point +MSE 999.963108 +---------------------- -------------------------------------------------------- +Time: 5.182s Load: 0.070s, Pack+Encode: 2.605s, Decode+Unpack: 2.507s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 999.9631 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12057211-0.000404_nail _ newt_0.31321314.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n12057211-0.000404_nail _ newt_0.31321314.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12144580-0.002806_banana _ banana_0.999156.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12144580-0.002806_banana _ banana_0.999156.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 65,992B, BPFP=0.1253 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 238,780B, BPFP=0.4532 +⌛️ [2/4] FRONTEND: Frontend time: 2.600s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.494s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09629347 12.57499601 + layer.39.0 105.38953930 5098.42954325 + ------------------------------------------------------------------------------------- + TOTAL 52.74291638 2555.50226963 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 304772 +BPFP 0.2892 bits/point +EBPFP 0.2892 equivalent bits/point +MSE 2555.502270 +---------------------- -------------------------------------------------------- +Time: 5.166s Load: 0.071s, Pack+Encode: 2.600s, Decode+Unpack: 2.494s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2555.5023 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12144580-0.002806_banana _ banana_0.999156.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n12144580-0.002806_banana _ banana_0.999156.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12267677-0.004617_pomegranate _ pomegranate_0.9159373.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12267677-0.004617_pomegranate _ pomegranate_0.9159373.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 64,252B, BPFP=0.1220 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 197,780B, BPFP=0.3754 +⌛️ [2/4] FRONTEND: Frontend time: 2.613s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.489s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10323383 12.62106433 + layer.39.0 78.12042942 2517.19290573 + ------------------------------------------------------------------------------------- + TOTAL 39.11183162 1264.90698503 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 262032 +BPFP 0.2487 bits/point +EBPFP 0.2487 equivalent bits/point +MSE 1264.906985 +---------------------- -------------------------------------------------------- +Time: 5.175s Load: 0.072s, Pack+Encode: 2.613s, Decode+Unpack: 2.489s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1264.9070 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12267677-0.004617_pomegranate _ pomegranate_0.9159373.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1ka/n12267677-0.004617_pomegranate _ pomegranate_0.9159373.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 0.2401 bits/point +Avg EBPFP 0.2401 equivalent bits/point +Avg MSE 1428.936115 +Avg Time 5.159s +------------------------ ---------------------------- diff --git a/lambda0.004/elic-featurecoding-8bit-individual/cls_in1kr/dtufc_elic-featurecoding_dinov3-total_individual.log b/lambda0.004/elic-featurecoding-8bit-individual/cls_in1kr/dtufc_elic-featurecoding_dinov3-total_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..b0bc64ec47b722801d30e1c7cbbf7126660c7bc1 --- /dev/null +++ b/lambda0.004/elic-featurecoding-8bit-individual/cls_in1kr/dtufc_elic-featurecoding_dinov3-total_individual.log @@ -0,0 +1,9344 @@ +Experiment: dtufc_elic-featurecoding_dinov3-total_individual +Log file: output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/dtufc_elic-featurecoding_dinov3-total_individual.log +DTUFCCodecConfig: + arch: elic-featurecoding + handler: dinov3-total + checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.004_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.004_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 255 +Loaded elic-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.9' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.19' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.29' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.39' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json +Loaded per-key mappings: model=dinov3-total + Keys: ['layer.9', 'layer.19', 'layer.29', 'layer.39'] +---------------- -------------------------------------------------------------------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +Checkpoint codec_weights/elic_hybrid/elic2022-official_lambda0.004_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-DINOv3/imagenet1k-r +Output output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr +---------------- -------------------------------------------------------------------------------------------------------------------- +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01443537-graffiti_3.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01443537-graffiti_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 65,076B, BPFP=0.1235 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 176,640B, BPFP=0.3353 +⌛️ [2/4] FRONTEND: Frontend time: 2.883s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.564s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09690064 12.57972318 + layer.39.0 23.14008974 2528.04373178 + ------------------------------------------------------------------------------------- + TOTAL 11.61849519 1270.31172748 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 241716 +BPFP 0.2294 bits/point +EBPFP 0.2294 equivalent bits/point +MSE 1270.311727 +---------------------- -------------------------------------------------------- +Time: 5.519s Load: 0.072s, Pack+Encode: 2.883s, Decode+Unpack: 2.564s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1270.3117 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01443537-graffiti_3.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n01443537-graffiti_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01494475-misc_31.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01494475-misc_31.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 52,744B, BPFP=0.1001 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 245,556B, BPFP=0.4661 +⌛️ [2/4] FRONTEND: Frontend time: 2.592s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.501s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09558801 12.75669833 + layer.39.0 281.54433916 5817.31486880 + ------------------------------------------------------------------------------------- + TOTAL 140.81996359 2915.03578357 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 298300 +BPFP 0.2831 bits/point +EBPFP 0.2831 equivalent bits/point +MSE 2915.035784 +---------------------- -------------------------------------------------------- +Time: 5.164s Load: 0.071s, Pack+Encode: 2.592s, Decode+Unpack: 2.501s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2915.0358 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01494475-misc_31.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n01494475-misc_31.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01531178-cartoon_22.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01531178-cartoon_22.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.057s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 56,264B, BPFP=0.1068 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 169,700B, BPFP=0.3221 +⌛️ [2/4] FRONTEND: Frontend time: 2.586s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.485s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10319715 12.79356475 + layer.39.0 12.97479918 2276.30952381 + ------------------------------------------------------------------------------------- + TOTAL 6.53899817 1144.55154428 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 225964 +BPFP 0.2144 bits/point +EBPFP 0.2144 equivalent bits/point +MSE 1144.551544 +---------------------- -------------------------------------------------------- +Time: 5.128s Load: 0.057s, Pack+Encode: 2.586s, Decode+Unpack: 2.485s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1144.5515 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01531178-cartoon_22.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n01531178-cartoon_22.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01534433-painting_9.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01534433-painting_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 72,768B, BPFP=0.1381 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 153,772B, BPFP=0.2919 +⌛️ [2/4] FRONTEND: Frontend time: 2.599s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.503s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10660143 12.60622703 + layer.39.0 8.42910859 2002.35216229 + ------------------------------------------------------------------------------------- + TOTAL 4.26785501 1007.47919466 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 226540 +BPFP 0.2150 bits/point +EBPFP 0.2150 equivalent bits/point +MSE 1007.479195 +---------------------- -------------------------------------------------------- +Time: 5.173s Load: 0.071s, Pack+Encode: 2.599s, Decode+Unpack: 2.503s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1007.4792 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01534433-painting_9.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n01534433-painting_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01632777-toy_21.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01632777-toy_21.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 61,808B, BPFP=0.1173 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 257,804B, BPFP=0.4893 +⌛️ [2/4] FRONTEND: Frontend time: 2.586s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.515s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09516629 12.51582145 + layer.39.0 31.73491595 4829.40476190 + ------------------------------------------------------------------------------------- + TOTAL 15.91504112 2420.96029168 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 319612 +BPFP 0.3033 bits/point +EBPFP 0.3033 equivalent bits/point +MSE 2420.960292 +---------------------- -------------------------------------------------------- +Time: 5.170s Load: 0.069s, Pack+Encode: 2.586s, Decode+Unpack: 2.515s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2420.9603 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01632777-toy_21.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n01632777-toy_21.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01748264-misc_18.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01748264-misc_18.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 101,224B, BPFP=0.1921 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 202,616B, BPFP=0.3846 +⌛️ [2/4] FRONTEND: Frontend time: 2.594s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.503s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.16139180 12.55065693 + layer.39.0 362.83485180 3689.26530612 + ------------------------------------------------------------------------------------- + TOTAL 181.49812180 1850.90798152 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 303840 +BPFP 0.2884 bits/point +EBPFP 0.2884 equivalent bits/point +MSE 1850.907982 +---------------------- -------------------------------------------------------- +Time: 5.167s Load: 0.070s, Pack+Encode: 2.594s, Decode+Unpack: 2.503s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1850.9080 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01748264-misc_18.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n01748264-misc_18.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01784675-painting_2.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01784675-painting_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 76,008B, BPFP=0.1443 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 219,400B, BPFP=0.4164 +⌛️ [2/4] FRONTEND: Frontend time: 2.606s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.508s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13866578 12.74322195 + layer.39.0 232.10166120 4381.07871720 + ------------------------------------------------------------------------------------- + TOTAL 116.12016349 2196.91096958 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 295408 +BPFP 0.2804 bits/point +EBPFP 0.2804 equivalent bits/point +MSE 2196.910970 +---------------------- -------------------------------------------------------- +Time: 5.173s Load: 0.059s, Pack+Encode: 2.606s, Decode+Unpack: 2.508s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2196.9110 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01784675-painting_2.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n01784675-painting_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01820546-painting_29.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01820546-painting_29.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 78,616B, BPFP=0.1492 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 197,584B, BPFP=0.3750 +⌛️ [2/4] FRONTEND: Frontend time: 2.588s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.522s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10398871 12.77912605 + layer.39.0 202.99580904 3448.28620019 + ------------------------------------------------------------------------------------- + TOTAL 101.54989888 1730.53266312 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 276200 +BPFP 0.2621 bits/point +EBPFP 0.2621 equivalent bits/point +MSE 1730.532663 +---------------------- -------------------------------------------------------- +Time: 5.182s Load: 0.072s, Pack+Encode: 2.588s, Decode+Unpack: 2.522s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1730.5327 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01820546-painting_29.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n01820546-painting_29.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01833805-painting_23.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01833805-painting_23.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 60,260B, BPFP=0.1144 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 179,544B, BPFP=0.3408 +⌛️ [2/4] FRONTEND: Frontend time: 2.605s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.485s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09675035 12.52549882 + layer.39.0 56.43029868 2346.27356657 + ------------------------------------------------------------------------------------- + TOTAL 28.26352451 1179.39953269 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 239804 +BPFP 0.2276 bits/point +EBPFP 0.2276 equivalent bits/point +MSE 1179.399533 +---------------------- -------------------------------------------------------- +Time: 5.159s Load: 0.069s, Pack+Encode: 2.605s, Decode+Unpack: 2.485s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1179.3995 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01833805-painting_23.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n01833805-painting_23.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01860187-painting_2.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01860187-painting_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 54,032B, BPFP=0.1026 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 171,080B, BPFP=0.3247 +⌛️ [2/4] FRONTEND: Frontend time: 2.579s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.511s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09532418 12.61252202 + layer.39.0 11.39113179 2278.86418853 + ------------------------------------------------------------------------------------- + TOTAL 5.74322799 1145.73835528 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 225112 +BPFP 0.2136 bits/point +EBPFP 0.2136 equivalent bits/point +MSE 1145.738355 +---------------------- -------------------------------------------------------- +Time: 5.158s Load: 0.069s, Pack+Encode: 2.579s, Decode+Unpack: 2.511s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1145.7384 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01860187-painting_2.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n01860187-painting_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01944390-deviantart_6.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01944390-deviantart_6.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 69,488B, BPFP=0.1319 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 189,696B, BPFP=0.3601 +⌛️ [2/4] FRONTEND: Frontend time: 2.592s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.521s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10713051 12.60498379 + layer.39.0 82.30322218 3099.48931001 + ------------------------------------------------------------------------------------- + TOTAL 41.20517635 1556.04714690 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 259184 +BPFP 0.2460 bits/point +EBPFP 0.2460 equivalent bits/point +MSE 1556.047147 +---------------------- -------------------------------------------------------- +Time: 5.183s Load: 0.070s, Pack+Encode: 2.592s, Decode+Unpack: 2.521s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1556.0471 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01944390-deviantart_6.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n01944390-deviantart_6.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01983481-misc_6.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01983481-misc_6.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 79,332B, BPFP=0.1506 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 201,092B, BPFP=0.3817 +⌛️ [2/4] FRONTEND: Frontend time: 2.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.510s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10315659 12.51058654 + layer.39.0 236.29731535 3261.92371234 + ------------------------------------------------------------------------------------- + TOTAL 118.20023597 1637.21714944 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 280424 +BPFP 0.2661 bits/point +EBPFP 0.2661 equivalent bits/point +MSE 1637.217149 +---------------------- -------------------------------------------------------- +Time: 5.161s Load: 0.070s, Pack+Encode: 2.581s, Decode+Unpack: 2.510s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1637.2171 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01983481-misc_6.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n01983481-misc_6.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02051845-cartoon_2.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02051845-cartoon_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.049s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 67,316B, BPFP=0.1278 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 185,036B, BPFP=0.3512 +⌛️ [2/4] FRONTEND: Frontend time: 2.595s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.491s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11657756 12.58467338 + layer.39.0 123.57765428 2580.25218659 + ------------------------------------------------------------------------------------- + TOTAL 61.84711592 1296.41842998 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 252352 +BPFP 0.2395 bits/point +EBPFP 0.2395 equivalent bits/point +MSE 1296.418430 +---------------------- -------------------------------------------------------- +Time: 5.135s Load: 0.049s, Pack+Encode: 2.595s, Decode+Unpack: 2.491s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1296.4184 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02051845-cartoon_2.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n02051845-cartoon_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02056570-art_2.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02056570-art_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 60,540B, BPFP=0.1149 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 182,772B, BPFP=0.3469 +⌛️ [2/4] FRONTEND: Frontend time: 2.582s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.500s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09569211 12.64117734 + layer.39.0 33.39981930 3029.75704568 + ------------------------------------------------------------------------------------- + TOTAL 16.74775571 1521.19911151 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 243312 +BPFP 0.2309 bits/point +EBPFP 0.2309 equivalent bits/point +MSE 1521.199112 +---------------------- -------------------------------------------------------- +Time: 5.133s Load: 0.051s, Pack+Encode: 2.582s, Decode+Unpack: 2.500s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1521.1991 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02056570-art_2.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n02056570-art_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02085620-misc_90.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02085620-misc_90.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 56,340B, BPFP=0.1069 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 226,128B, BPFP=0.4292 +⌛️ [2/4] FRONTEND: Frontend time: 2.597s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.514s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09843166 12.62544035 + layer.39.0 72.76188958 4551.64480078 + ------------------------------------------------------------------------------------- + TOTAL 36.43016062 2282.13512057 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 282468 +BPFP 0.2681 bits/point +EBPFP 0.2681 equivalent bits/point +MSE 2282.135121 +---------------------- -------------------------------------------------------- +Time: 5.163s Load: 0.052s, Pack+Encode: 2.597s, Decode+Unpack: 2.514s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2282.1351 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02085620-misc_90.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n02085620-misc_90.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088094-misc_39.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088094-misc_39.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 68,036B, BPFP=0.1291 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 180,996B, BPFP=0.3435 +⌛️ [2/4] FRONTEND: Frontend time: 2.583s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.510s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09820385 12.57245638 + layer.39.0 12.32374423 2741.26895044 + ------------------------------------------------------------------------------------- + TOTAL 6.21097404 1376.92070341 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 249032 +BPFP 0.2363 bits/point +EBPFP 0.2363 equivalent bits/point +MSE 1376.920703 +---------------------- -------------------------------------------------------- +Time: 5.164s Load: 0.071s, Pack+Encode: 2.583s, Decode+Unpack: 2.510s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1376.9207 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088094-misc_39.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n02088094-misc_39.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088466-sketch_11.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088466-sketch_11.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 46,748B, BPFP=0.0887 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 208,860B, BPFP=0.3964 +⌛️ [2/4] FRONTEND: Frontend time: 2.591s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.522s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09459993 12.62804547 + layer.39.0 16.33682960 3144.89820214 + ------------------------------------------------------------------------------------- + TOTAL 8.21571477 1578.76312380 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 255608 +BPFP 0.2426 bits/point +EBPFP 0.2426 equivalent bits/point +MSE 1578.763124 +---------------------- -------------------------------------------------------- +Time: 5.182s Load: 0.070s, Pack+Encode: 2.591s, Decode+Unpack: 2.522s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1578.7631 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088466-sketch_11.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n02088466-sketch_11.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02094433-misc_20.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02094433-misc_20.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.068s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 57,860B, BPFP=0.1098 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 198,172B, BPFP=0.3761 +⌛️ [2/4] FRONTEND: Frontend time: 2.593s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.503s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09538842 12.56041021 + layer.39.0 94.83275632 3510.10398445 + ------------------------------------------------------------------------------------- + TOTAL 47.46407237 1761.33219733 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 256032 +BPFP 0.2430 bits/point +EBPFP 0.2430 equivalent bits/point +MSE 1761.332197 +---------------------- -------------------------------------------------------- +Time: 5.164s Load: 0.068s, Pack+Encode: 2.593s, Decode+Unpack: 2.503s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1761.3322 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02094433-misc_20.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n02094433-misc_20.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02097298-misc_9.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02097298-misc_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 91,836B, BPFP=0.1743 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 208,460B, BPFP=0.3957 +⌛️ [2/4] FRONTEND: Frontend time: 2.612s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.535s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11199322 12.52556715 + layer.39.0 26.16675018 3430.16180758 + ------------------------------------------------------------------------------------- + TOTAL 13.13937170 1721.34368736 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 300296 +BPFP 0.2850 bits/point +EBPFP 0.2850 equivalent bits/point +MSE 1721.343687 +---------------------- -------------------------------------------------------- +Time: 5.197s Load: 0.050s, Pack+Encode: 2.612s, Decode+Unpack: 2.535s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1721.3437 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02097298-misc_9.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n02097298-misc_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02106662-misc_55.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02106662-misc_55.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 64,024B, BPFP=0.1215 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 199,476B, BPFP=0.3786 +⌛️ [2/4] FRONTEND: Frontend time: 2.604s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.504s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09642073 12.30773107 + layer.39.0 14.86428154 3170.30223518 + ------------------------------------------------------------------------------------- + TOTAL 7.48035113 1591.30498313 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 263500 +BPFP 0.2501 bits/point +EBPFP 0.2501 equivalent bits/point +MSE 1591.304983 +---------------------- -------------------------------------------------------- +Time: 5.160s Load: 0.052s, Pack+Encode: 2.604s, Decode+Unpack: 2.504s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1591.3050 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02106662-misc_55.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n02106662-misc_55.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02109525-sketch_23.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02109525-sketch_23.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.068s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 51,800B, BPFP=0.0983 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 215,856B, BPFP=0.4097 +⌛️ [2/4] FRONTEND: Frontend time: 2.586s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.513s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09568003 12.52997449 + layer.39.0 14.01675815 3405.19606414 + ------------------------------------------------------------------------------------- + TOTAL 7.05621909 1708.86301931 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 267656 +BPFP 0.2540 bits/point +EBPFP 0.2540 equivalent bits/point +MSE 1708.863019 +---------------------- -------------------------------------------------------- +Time: 5.167s Load: 0.068s, Pack+Encode: 2.586s, Decode+Unpack: 2.513s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1708.8630 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02109525-sketch_23.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n02109525-sketch_23.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110185-painting_33.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110185-painting_33.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 56,164B, BPFP=0.1066 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 239,612B, BPFP=0.4548 +⌛️ [2/4] FRONTEND: Frontend time: 2.594s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.490s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09599521 12.47436262 + layer.39.0 22.05506522 3454.10738581 + ------------------------------------------------------------------------------------- + TOTAL 11.07553021 1733.29087422 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 295776 +BPFP 0.2807 bits/point +EBPFP 0.2807 equivalent bits/point +MSE 1733.290874 +---------------------- -------------------------------------------------------- +Time: 5.155s Load: 0.070s, Pack+Encode: 2.594s, Decode+Unpack: 2.490s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1733.2909 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110185-painting_33.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n02110185-painting_33.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110341-misc_162.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110341-misc_162.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 60,968B, BPFP=0.1157 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 160,272B, BPFP=0.3042 +⌛️ [2/4] FRONTEND: Frontend time: 2.593s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.496s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11124049 12.72441197 + layer.39.0 14.33747210 2301.65063168 + ------------------------------------------------------------------------------------- + TOTAL 7.22435629 1157.18752183 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 221240 +BPFP 0.2100 bits/point +EBPFP 0.2100 equivalent bits/point +MSE 1157.187522 +---------------------- -------------------------------------------------------- +Time: 5.159s Load: 0.070s, Pack+Encode: 2.593s, Decode+Unpack: 2.496s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1157.1875 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110341-misc_162.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n02110341-misc_162.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02165456-tattoo_37.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02165456-tattoo_37.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 62,712B, BPFP=0.1190 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 196,460B, BPFP=0.3729 +⌛️ [2/4] FRONTEND: Frontend time: 2.601s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.521s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09780899 12.27596005 + layer.39.0 88.96013271 2612.26044704 + ------------------------------------------------------------------------------------- + TOTAL 44.52897085 1312.26820354 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 259172 +BPFP 0.2460 bits/point +EBPFP 0.2460 equivalent bits/point +MSE 1312.268204 +---------------------- -------------------------------------------------------- +Time: 5.193s Load: 0.071s, Pack+Encode: 2.601s, Decode+Unpack: 2.521s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1312.2682 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02165456-tattoo_37.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n02165456-tattoo_37.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02219486-misc_3.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02219486-misc_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 51,424B, BPFP=0.0976 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 147,344B, BPFP=0.2797 +⌛️ [2/4] FRONTEND: Frontend time: 2.584s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.505s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10021695 12.72265245 + layer.39.0 75.73793580 2094.09766764 + ------------------------------------------------------------------------------------- + TOTAL 37.91907638 1053.41016005 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 198768 +BPFP 0.1886 bits/point +EBPFP 0.1886 equivalent bits/point +MSE 1053.410160 +---------------------- -------------------------------------------------------- +Time: 5.141s Load: 0.052s, Pack+Encode: 2.584s, Decode+Unpack: 2.505s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1053.4102 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02219486-misc_3.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n02219486-misc_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02226429-tattoo_0.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02226429-tattoo_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 59,048B, BPFP=0.1121 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 196,316B, BPFP=0.3726 +⌛️ [2/4] FRONTEND: Frontend time: 2.583s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.512s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09506506 12.48857830 + layer.39.0 201.13660107 2829.49222546 + ------------------------------------------------------------------------------------- + TOTAL 100.61583306 1420.99040188 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 255364 +BPFP 0.2424 bits/point +EBPFP 0.2424 equivalent bits/point +MSE 1420.990402 +---------------------- -------------------------------------------------------- +Time: 5.154s Load: 0.059s, Pack+Encode: 2.583s, Decode+Unpack: 2.512s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1420.9904 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02226429-tattoo_0.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n02226429-tattoo_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02233338-tattoo_5.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02233338-tattoo_5.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 57,956B, BPFP=0.1100 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 241,292B, BPFP=0.4580 +⌛️ [2/4] FRONTEND: Frontend time: 2.599s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.501s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09502332 12.47944379 + layer.39.0 172.43500972 3975.48032070 + ------------------------------------------------------------------------------------- + TOTAL 86.26501652 1993.97988224 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 299248 +BPFP 0.2840 bits/point +EBPFP 0.2840 equivalent bits/point +MSE 1993.979882 +---------------------- -------------------------------------------------------- +Time: 5.171s Load: 0.071s, Pack+Encode: 2.599s, Decode+Unpack: 2.501s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1993.9799 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02233338-tattoo_5.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n02233338-tattoo_5.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02279972-painting_2.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02279972-painting_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 83,092B, BPFP=0.1577 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 220,320B, BPFP=0.4182 +⌛️ [2/4] FRONTEND: Frontend time: 2.604s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.504s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11337867 12.51816273 + layer.39.0 361.17623299 3272.91496599 + ------------------------------------------------------------------------------------- + TOTAL 180.64480583 1642.71656436 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 303412 +BPFP 0.2880 bits/point +EBPFP 0.2880 equivalent bits/point +MSE 1642.716564 +---------------------- -------------------------------------------------------- +Time: 5.176s Load: 0.069s, Pack+Encode: 2.604s, Decode+Unpack: 2.504s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1642.7166 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02279972-painting_2.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n02279972-painting_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02317335-sculpture_0.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02317335-sculpture_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 53,824B, BPFP=0.1022 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 264,852B, BPFP=0.5027 +⌛️ [2/4] FRONTEND: Frontend time: 2.586s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.496s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09546056 12.63496872 + layer.39.0 1163.18707483 4083.60738581 + ------------------------------------------------------------------------------------- + TOTAL 581.64126769 2048.12117727 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 318676 +BPFP 0.3024 bits/point +EBPFP 0.3024 equivalent bits/point +MSE 2048.121177 +---------------------- -------------------------------------------------------- +Time: 5.151s Load: 0.069s, Pack+Encode: 2.586s, Decode+Unpack: 2.496s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2048.1212 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02317335-sculpture_0.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n02317335-sculpture_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02346627-painting_9.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02346627-painting_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 77,204B, BPFP=0.1465 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 206,300B, BPFP=0.3916 +⌛️ [2/4] FRONTEND: Frontend time: 2.615s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.500s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13205896 12.73511620 + layer.39.0 503.01482021 2838.79057337 + ------------------------------------------------------------------------------------- + TOTAL 251.57343959 1425.76284479 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 283504 +BPFP 0.2691 bits/point +EBPFP 0.2691 equivalent bits/point +MSE 1425.762845 +---------------------- -------------------------------------------------------- +Time: 5.165s Load: 0.050s, Pack+Encode: 2.615s, Decode+Unpack: 2.500s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1425.7628 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02346627-painting_9.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n02346627-painting_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02391049-deviantart_0.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02391049-deviantart_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 61,164B, BPFP=0.1161 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 169,376B, BPFP=0.3215 +⌛️ [2/4] FRONTEND: Frontend time: 2.584s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.489s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10116939 12.68138344 + layer.39.0 17.42674737 2653.28935860 + ------------------------------------------------------------------------------------- + TOTAL 8.76395838 1332.98537102 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 230540 +BPFP 0.2188 bits/point +EBPFP 0.2188 equivalent bits/point +MSE 1332.985371 +---------------------- -------------------------------------------------------- +Time: 5.143s Load: 0.070s, Pack+Encode: 2.584s, Decode+Unpack: 2.489s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1332.9854 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02391049-deviantart_0.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n02391049-deviantart_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02395406-sculpture_31.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02395406-sculpture_31.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 85,312B, BPFP=0.1619 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 169,344B, BPFP=0.3214 +⌛️ [2/4] FRONTEND: Frontend time: 2.606s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.514s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11469608 12.43971886 + layer.39.0 30.55020044 2643.81000972 + ------------------------------------------------------------------------------------- + TOTAL 15.33244826 1328.12486429 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 254656 +BPFP 0.2417 bits/point +EBPFP 0.2417 equivalent bits/point +MSE 1328.124864 +---------------------- -------------------------------------------------------- +Time: 5.190s Load: 0.070s, Pack+Encode: 2.606s, Decode+Unpack: 2.514s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1328.1249 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02395406-sculpture_31.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n02395406-sculpture_31.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02445715-art_3.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02445715-art_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 55,348B, BPFP=0.1051 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 196,532B, BPFP=0.3730 +⌛️ [2/4] FRONTEND: Frontend time: 2.586s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.507s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09587883 12.71349611 + layer.39.0 77.63827138 2989.64334305 + ------------------------------------------------------------------------------------- + TOTAL 38.86707511 1501.17841958 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 251880 +BPFP 0.2390 bits/point +EBPFP 0.2390 equivalent bits/point +MSE 1501.178420 +---------------------- -------------------------------------------------------- +Time: 5.164s Load: 0.070s, Pack+Encode: 2.586s, Decode+Unpack: 2.507s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1501.1784 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02445715-art_3.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n02445715-art_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02672831-sculpture_2.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02672831-sculpture_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 74,364B, BPFP=0.1411 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 268,848B, BPFP=0.5103 +⌛️ [2/4] FRONTEND: Frontend time: 2.600s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.496s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11638676 12.71447837 + layer.39.0 42.74346681 4293.53741497 + ------------------------------------------------------------------------------------- + TOTAL 21.42992678 2153.12594667 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 343212 +BPFP 0.3257 bits/point +EBPFP 0.3257 equivalent bits/point +MSE 2153.125947 +---------------------- -------------------------------------------------------- +Time: 5.164s Load: 0.069s, Pack+Encode: 2.600s, Decode+Unpack: 2.496s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2153.1259 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02672831-sculpture_2.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n02672831-sculpture_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02701002-videogame_1.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02701002-videogame_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 70,020B, BPFP=0.1329 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 236,312B, BPFP=0.4485 +⌛️ [2/4] FRONTEND: Frontend time: 2.608s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.522s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10320827 12.41761476 + layer.39.0 160.61054422 4463.26433431 + ------------------------------------------------------------------------------------- + TOTAL 80.35687624 2237.84097453 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 306332 +BPFP 0.2907 bits/point +EBPFP 0.2907 equivalent bits/point +MSE 2237.840975 +---------------------- -------------------------------------------------------- +Time: 5.181s Load: 0.052s, Pack+Encode: 2.608s, Decode+Unpack: 2.522s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2237.8410 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02701002-videogame_1.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n02701002-videogame_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02749479-misc_35.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02749479-misc_35.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 62,664B, BPFP=0.1189 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 193,508B, BPFP=0.3673 +⌛️ [2/4] FRONTEND: Frontend time: 2.584s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.480s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09764870 12.60012945 + layer.39.0 172.65676628 3208.97181730 + ------------------------------------------------------------------------------------- + TOTAL 86.37720749 1610.78597337 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 256172 +BPFP 0.2431 bits/point +EBPFP 0.2431 equivalent bits/point +MSE 1610.785973 +---------------------- -------------------------------------------------------- +Time: 5.135s Load: 0.072s, Pack+Encode: 2.584s, Decode+Unpack: 2.480s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1610.7860 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02749479-misc_35.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n02749479-misc_35.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02769748-cartoon_11.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02769748-cartoon_11.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.068s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 63,456B, BPFP=0.1204 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 195,648B, BPFP=0.3714 +⌛️ [2/4] FRONTEND: Frontend time: 2.583s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.504s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12263774 12.61033733 + layer.39.0 11.02823964 2686.70772595 + ------------------------------------------------------------------------------------- + TOTAL 5.57543869 1349.65903164 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 259104 +BPFP 0.2459 bits/point +EBPFP 0.2459 equivalent bits/point +MSE 1349.659032 +---------------------- -------------------------------------------------------- +Time: 5.155s Load: 0.068s, Pack+Encode: 2.583s, Decode+Unpack: 2.504s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1349.6590 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02769748-cartoon_11.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n02769748-cartoon_11.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02793495-sketch_11.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02793495-sketch_11.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 61,048B, BPFP=0.1159 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 167,884B, BPFP=0.3187 +⌛️ [2/4] FRONTEND: Frontend time: 2.575s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.504s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09793751 12.50340990 + layer.39.0 182.75789602 2302.54761905 + ------------------------------------------------------------------------------------- + TOTAL 91.42791676 1157.52551447 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 228932 +BPFP 0.2173 bits/point +EBPFP 0.2173 equivalent bits/point +MSE 1157.525514 +---------------------- -------------------------------------------------------- +Time: 5.140s Load: 0.060s, Pack+Encode: 2.575s, Decode+Unpack: 2.504s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1157.5255 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02793495-sketch_11.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n02793495-sketch_11.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02797295-misc_13.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02797295-misc_13.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 97,784B, BPFP=0.1856 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 266,648B, BPFP=0.5061 +⌛️ [2/4] FRONTEND: Frontend time: 2.608s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.515s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.17140635 12.52548363 + layer.39.0 172.50999150 4120.52721088 + ------------------------------------------------------------------------------------- + TOTAL 86.34069892 2066.52634726 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 364432 +BPFP 0.3459 bits/point +EBPFP 0.3459 equivalent bits/point +MSE 2066.526347 +---------------------- -------------------------------------------------------- +Time: 5.174s Load: 0.051s, Pack+Encode: 2.608s, Decode+Unpack: 2.515s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2066.5263 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02797295-misc_13.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n02797295-misc_13.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02802426-art_1.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02802426-art_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.056s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 94,992B, BPFP=0.1803 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 275,552B, BPFP=0.5230 +⌛️ [2/4] FRONTEND: Frontend time: 2.604s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.502s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.16523854 12.47874814 + layer.39.0 477.65184645 5011.80369291 + ------------------------------------------------------------------------------------- + TOTAL 238.90854250 2512.14122052 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 370544 +BPFP 0.3517 bits/point +EBPFP 0.3517 equivalent bits/point +MSE 2512.141221 +---------------------- -------------------------------------------------------- +Time: 5.162s Load: 0.056s, Pack+Encode: 2.604s, Decode+Unpack: 2.502s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2512.1412 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02802426-art_1.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n02802426-art_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02814860-sticker_8.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02814860-sticker_8.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 66,936B, BPFP=0.1270 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 182,264B, BPFP=0.3460 +⌛️ [2/4] FRONTEND: Frontend time: 2.595s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.511s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12757226 12.67487776 + layer.39.0 19.27598852 2140.48250729 + ------------------------------------------------------------------------------------- + TOTAL 9.70178039 1076.57869253 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 249200 +BPFP 0.2365 bits/point +EBPFP 0.2365 equivalent bits/point +MSE 1076.578693 +---------------------- -------------------------------------------------------- +Time: 5.157s Load: 0.052s, Pack+Encode: 2.595s, Decode+Unpack: 2.511s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1076.5787 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02814860-sticker_8.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n02814860-sticker_8.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02841315-graphic_4.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02841315-graphic_4.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 61,760B, BPFP=0.1172 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 262,948B, BPFP=0.4991 +⌛️ [2/4] FRONTEND: Frontend time: 2.588s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.496s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11826141 12.60899443 + layer.39.0 55.46440340 3624.78692906 + ------------------------------------------------------------------------------------- + TOTAL 27.79133240 1818.69796175 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 324708 +BPFP 0.3082 bits/point +EBPFP 0.3082 equivalent bits/point +MSE 1818.697962 +---------------------- -------------------------------------------------------- +Time: 5.135s Load: 0.051s, Pack+Encode: 2.588s, Decode+Unpack: 2.496s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1818.6980 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02841315-graphic_4.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n02841315-graphic_4.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02843684-cartoon_9.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02843684-cartoon_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.058s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 79,116B, BPFP=0.1502 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 187,732B, BPFP=0.3563 +⌛️ [2/4] FRONTEND: Frontend time: 2.597s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.513s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12386809 12.59025848 + layer.39.0 312.00962707 2480.06705539 + ------------------------------------------------------------------------------------- + TOTAL 156.06674758 1246.32865694 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 266848 +BPFP 0.2532 bits/point +EBPFP 0.2532 equivalent bits/point +MSE 1246.328657 +---------------------- -------------------------------------------------------- +Time: 5.169s Load: 0.058s, Pack+Encode: 2.597s, Decode+Unpack: 2.513s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1246.3287 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02843684-cartoon_9.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n02843684-cartoon_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02883205-sketch_15.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02883205-sketch_15.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 63,192B, BPFP=0.1199 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 192,724B, BPFP=0.3658 +⌛️ [2/4] FRONTEND: Frontend time: 2.606s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.494s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09796664 12.67505713 + layer.39.0 103.64267493 2739.39674441 + ------------------------------------------------------------------------------------- + TOTAL 51.87032078 1376.03590077 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 255916 +BPFP 0.2429 bits/point +EBPFP 0.2429 equivalent bits/point +MSE 1376.035901 +---------------------- -------------------------------------------------------- +Time: 5.169s Load: 0.069s, Pack+Encode: 2.606s, Decode+Unpack: 2.494s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1376.0359 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02883205-sketch_15.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n02883205-sketch_15.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02906734-graffiti_3.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02906734-graffiti_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 117,452B, BPFP=0.2229 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 259,576B, BPFP=0.4927 +⌛️ [2/4] FRONTEND: Frontend time: 2.616s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.543s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.17339475 8.66190343 + layer.39.0 166.12656402 3451.20310982 + ------------------------------------------------------------------------------------- + TOTAL 83.14997939 1729.93250662 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 377028 +BPFP 0.3578 bits/point +EBPFP 0.3578 equivalent bits/point +MSE 1729.932507 +---------------------- -------------------------------------------------------- +Time: 5.210s Load: 0.051s, Pack+Encode: 2.616s, Decode+Unpack: 2.543s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1729.9325 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02906734-graffiti_3.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n02906734-graffiti_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02909870-sketch_0.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02909870-sketch_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 88,316B, BPFP=0.1676 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 179,372B, BPFP=0.3405 +⌛️ [2/4] FRONTEND: Frontend time: 2.582s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.503s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.15317524 12.51774705 + layer.39.0 167.75886783 2341.29956268 + ------------------------------------------------------------------------------------- + TOTAL 83.95602154 1176.90865487 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 267688 +BPFP 0.2540 bits/point +EBPFP 0.2540 equivalent bits/point +MSE 1176.908655 +---------------------- -------------------------------------------------------- +Time: 5.144s Load: 0.059s, Pack+Encode: 2.582s, Decode+Unpack: 2.503s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1176.9087 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02909870-sketch_0.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n02909870-sketch_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02939185-painting_0.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02939185-painting_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 48,704B, BPFP=0.0924 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 190,372B, BPFP=0.3613 +⌛️ [2/4] FRONTEND: Frontend time: 2.577s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.508s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09512242 12.62914541 + layer.39.0 131.28711127 2723.46598639 + ------------------------------------------------------------------------------------- + TOTAL 65.69111684 1368.04756590 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 239076 +BPFP 0.2269 bits/point +EBPFP 0.2269 equivalent bits/point +MSE 1368.047566 +---------------------- -------------------------------------------------------- +Time: 5.157s Load: 0.072s, Pack+Encode: 2.577s, Decode+Unpack: 2.508s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1368.0476 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02939185-painting_0.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n02939185-painting_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02948072-misc_10.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02948072-misc_10.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 57,156B, BPFP=0.1085 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 225,388B, BPFP=0.4278 +⌛️ [2/4] FRONTEND: Frontend time: 2.600s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.520s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09566823 12.71444325 + layer.39.0 102.81622783 2977.50510204 + ------------------------------------------------------------------------------------- + TOTAL 51.45594803 1495.10977265 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 282544 +BPFP 0.2681 bits/point +EBPFP 0.2681 equivalent bits/point +MSE 1495.109773 +---------------------- -------------------------------------------------------- +Time: 5.180s Load: 0.059s, Pack+Encode: 2.600s, Decode+Unpack: 2.520s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1495.1098 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02948072-misc_10.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n02948072-misc_10.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02950826-art_1.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02950826-art_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 58,136B, BPFP=0.1103 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 276,696B, BPFP=0.5252 +⌛️ [2/4] FRONTEND: Frontend time: 2.605s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.519s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09506074 12.66209419 + layer.39.0 1071.96149174 4591.06511176 + ------------------------------------------------------------------------------------- + TOTAL 536.02827624 2301.86360297 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 334832 +BPFP 0.3178 bits/point +EBPFP 0.3178 equivalent bits/point +MSE 2301.863603 +---------------------- -------------------------------------------------------- +Time: 5.176s Load: 0.052s, Pack+Encode: 2.605s, Decode+Unpack: 2.519s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2301.8636 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02950826-art_1.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n02950826-art_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02951358-misc_1.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02951358-misc_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.057s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 59,040B, BPFP=0.1121 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 150,092B, BPFP=0.2849 +⌛️ [2/4] FRONTEND: Frontend time: 2.588s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.510s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09568294 12.65611524 + layer.39.0 598.97078474 2212.18561710 + ------------------------------------------------------------------------------------- + TOTAL 299.53323384 1112.42086617 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 209132 +BPFP 0.1985 bits/point +EBPFP 0.1985 equivalent bits/point +MSE 1112.420866 +---------------------- -------------------------------------------------------- +Time: 5.155s Load: 0.057s, Pack+Encode: 2.588s, Decode+Unpack: 2.510s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1112.4209 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02951358-misc_1.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n02951358-misc_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02966193-cartoon_22.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02966193-cartoon_22.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 91,944B, BPFP=0.1745 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 258,240B, BPFP=0.4902 +⌛️ [2/4] FRONTEND: Frontend time: 2.587s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.497s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10376222 12.60635990 + layer.39.0 767.85532070 4943.36831876 + ------------------------------------------------------------------------------------- + TOTAL 383.97954146 2477.98733933 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 350184 +BPFP 0.3323 bits/point +EBPFP 0.3323 equivalent bits/point +MSE 2477.987339 +---------------------- -------------------------------------------------------- +Time: 5.153s Load: 0.069s, Pack+Encode: 2.587s, Decode+Unpack: 2.497s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2477.9873 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02966193-cartoon_22.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n02966193-cartoon_22.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02980441-graphic_3.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02980441-graphic_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 56,476B, BPFP=0.1072 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 128,928B, BPFP=0.2447 +⌛️ [2/4] FRONTEND: Frontend time: 2.575s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.491s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09509088 12.53343754 + layer.39.0 13.13791359 1549.18901846 + ------------------------------------------------------------------------------------- + TOTAL 6.61650224 780.86122800 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 185404 +BPFP 0.1760 bits/point +EBPFP 0.1760 equivalent bits/point +MSE 780.861228 +---------------------- -------------------------------------------------------- +Time: 5.135s Load: 0.069s, Pack+Encode: 2.575s, Decode+Unpack: 2.491s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 780.8612 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02980441-graphic_3.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n02980441-graphic_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03124170-painting_15.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03124170-painting_15.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.057s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 65,948B, BPFP=0.1252 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 325,600B, BPFP=0.6180 +⌛️ [2/4] FRONTEND: Frontend time: 2.596s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.514s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10783903 12.72637079 + layer.39.0 326.57091229 8184.91253644 + ------------------------------------------------------------------------------------- + TOTAL 163.33937566 4098.81945362 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 391548 +BPFP 0.3716 bits/point +EBPFP 0.3716 equivalent bits/point +MSE 4098.819454 +---------------------- -------------------------------------------------------- +Time: 5.167s Load: 0.057s, Pack+Encode: 2.596s, Decode+Unpack: 2.514s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4098.8195 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03124170-painting_15.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n03124170-painting_15.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03345487-toy_17.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03345487-toy_17.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 71,300B, BPFP=0.1353 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 217,516B, BPFP=0.4129 +⌛️ [2/4] FRONTEND: Frontend time: 2.584s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.513s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10662318 12.58717125 + layer.39.0 198.63900024 3364.69727891 + ------------------------------------------------------------------------------------- + TOTAL 99.37281171 1688.64222508 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 288816 +BPFP 0.2741 bits/point +EBPFP 0.2741 equivalent bits/point +MSE 1688.642225 +---------------------- -------------------------------------------------------- +Time: 5.167s Load: 0.070s, Pack+Encode: 2.584s, Decode+Unpack: 2.513s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1688.6422 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03345487-toy_17.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n03345487-toy_17.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03372029-sketch_14.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03372029-sketch_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 71,540B, BPFP=0.1358 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 219,280B, BPFP=0.4162 +⌛️ [2/4] FRONTEND: Frontend time: 2.598s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.492s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12162214 12.52412366 + layer.39.0 228.06095117 3153.53158406 + ------------------------------------------------------------------------------------- + TOTAL 114.09128665 1583.02785386 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 290820 +BPFP 0.2760 bits/point +EBPFP 0.2760 equivalent bits/point +MSE 1583.027854 +---------------------- -------------------------------------------------------- +Time: 5.140s Load: 0.050s, Pack+Encode: 2.598s, Decode+Unpack: 2.492s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1583.0279 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03372029-sketch_14.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n03372029-sketch_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03424325-misc_4.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03424325-misc_4.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 66,380B, BPFP=0.1260 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 235,704B, BPFP=0.4474 +⌛️ [2/4] FRONTEND: Frontend time: 2.598s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.501s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10761499 12.63751215 + layer.39.0 21.03287666 4285.80029155 + ------------------------------------------------------------------------------------- + TOTAL 10.57024582 2149.21890185 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 302084 +BPFP 0.2867 bits/point +EBPFP 0.2867 equivalent bits/point +MSE 2149.218902 +---------------------- -------------------------------------------------------- +Time: 5.169s Load: 0.069s, Pack+Encode: 2.598s, Decode+Unpack: 2.501s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2149.2189 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03424325-misc_4.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n03424325-misc_4.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03467068-sketch_17.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03467068-sketch_17.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 58,004B, BPFP=0.1101 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 210,124B, BPFP=0.3988 +⌛️ [2/4] FRONTEND: Frontend time: 2.598s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.518s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09564773 12.62785282 + layer.39.0 208.14688107 2920.98931001 + ------------------------------------------------------------------------------------- + TOTAL 104.12126440 1466.80858141 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 268128 +BPFP 0.2545 bits/point +EBPFP 0.2545 equivalent bits/point +MSE 1466.808581 +---------------------- -------------------------------------------------------- +Time: 5.168s Load: 0.052s, Pack+Encode: 2.598s, Decode+Unpack: 2.518s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1466.8086 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03467068-sketch_17.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n03467068-sketch_17.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03481172-sketch_9.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03481172-sketch_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.057s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 76,380B, BPFP=0.1450 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 202,272B, BPFP=0.3839 +⌛️ [2/4] FRONTEND: Frontend time: 2.609s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.506s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14641065 12.55352113 + layer.39.0 516.28267736 3057.00971817 + ------------------------------------------------------------------------------------- + TOTAL 258.21454400 1534.78161965 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 278652 +BPFP 0.2645 bits/point +EBPFP 0.2645 equivalent bits/point +MSE 1534.781620 +---------------------- -------------------------------------------------------- +Time: 5.172s Load: 0.057s, Pack+Encode: 2.609s, Decode+Unpack: 2.506s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1534.7816 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03481172-sketch_9.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n03481172-sketch_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03494278-deviantart_3.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03494278-deviantart_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 60,564B, BPFP=0.1150 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 183,696B, BPFP=0.3487 +⌛️ [2/4] FRONTEND: Frontend time: 2.594s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.514s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09714438 12.52200255 + layer.39.0 11.38600982 2437.85179786 + ------------------------------------------------------------------------------------- + TOTAL 5.74157710 1225.18690021 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 244260 +BPFP 0.2318 bits/point +EBPFP 0.2318 equivalent bits/point +MSE 1225.186900 +---------------------- -------------------------------------------------------- +Time: 5.159s Load: 0.052s, Pack+Encode: 2.594s, Decode+Unpack: 2.514s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1225.1869 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03494278-deviantart_3.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n03494278-deviantart_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03495258-painting_3.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03495258-painting_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 72,448B, BPFP=0.1375 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 253,924B, BPFP=0.4820 +⌛️ [2/4] FRONTEND: Frontend time: 2.601s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.504s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10398556 12.66834932 + layer.39.0 359.17207240 3590.14042760 + ------------------------------------------------------------------------------------- + TOTAL 179.63802898 1801.40438846 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 326372 +BPFP 0.3097 bits/point +EBPFP 0.3097 equivalent bits/point +MSE 1801.404388 +---------------------- -------------------------------------------------------- +Time: 5.175s Load: 0.070s, Pack+Encode: 2.601s, Decode+Unpack: 2.504s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1801.4044 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03495258-painting_3.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n03495258-painting_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03498962-sketch_8.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03498962-sketch_8.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 108,028B, BPFP=0.2050 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 200,808B, BPFP=0.3811 +⌛️ [2/4] FRONTEND: Frontend time: 2.594s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.515s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.16074808 12.50966503 + layer.39.0 476.99061589 3190.60301263 + ------------------------------------------------------------------------------------- + TOTAL 238.57568198 1601.55633883 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 308836 +BPFP 0.2931 bits/point +EBPFP 0.2931 equivalent bits/point +MSE 1601.556339 +---------------------- -------------------------------------------------------- +Time: 5.178s Load: 0.070s, Pack+Encode: 2.594s, Decode+Unpack: 2.515s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1601.5563 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03498962-sketch_8.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n03498962-sketch_8.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03602883-misc_12.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03602883-misc_12.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 55,304B, BPFP=0.1050 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 155,580B, BPFP=0.2953 +⌛️ [2/4] FRONTEND: Frontend time: 2.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.504s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.09080038 12.70374567 + layer.39.0 100.93773536 2050.16666667 + ------------------------------------------------------------------------------------- + TOTAL 54.51426787 1031.43520617 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 210884 +BPFP 0.2001 bits/point +EBPFP 0.2001 equivalent bits/point +MSE 1031.435206 +---------------------- -------------------------------------------------------- +Time: 5.155s Load: 0.071s, Pack+Encode: 2.581s, Decode+Unpack: 2.504s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1031.4352 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03602883-misc_12.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n03602883-misc_12.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03630383-toy_1.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03630383-toy_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 51,148B, BPFP=0.0971 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 193,496B, BPFP=0.3673 +⌛️ [2/4] FRONTEND: Frontend time: 2.584s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.517s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09574974 12.44107978 + layer.39.0 14.66923857 2636.22667638 + ------------------------------------------------------------------------------------- + TOTAL 7.38249415 1324.33387808 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 244644 +BPFP 0.2322 bits/point +EBPFP 0.2322 equivalent bits/point +MSE 1324.333878 +---------------------- -------------------------------------------------------- +Time: 5.160s Load: 0.059s, Pack+Encode: 2.584s, Decode+Unpack: 2.517s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1324.3339 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03630383-toy_1.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n03630383-toy_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03649909-toy_22.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03649909-toy_22.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 50,904B, BPFP=0.0966 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 126,124B, BPFP=0.2394 +⌛️ [2/4] FRONTEND: Frontend time: 2.590s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.506s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09878858 12.65273191 + layer.39.0 29.68475348 1800.39589407 + ------------------------------------------------------------------------------------- + TOTAL 14.89177103 906.52431299 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 177028 +BPFP 0.1680 bits/point +EBPFP 0.1680 equivalent bits/point +MSE 906.524313 +---------------------- -------------------------------------------------------- +Time: 5.166s Load: 0.070s, Pack+Encode: 2.590s, Decode+Unpack: 2.506s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 906.5243 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03649909-toy_22.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n03649909-toy_22.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03676483-sculpture_3.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03676483-sculpture_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 53,448B, BPFP=0.1014 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 263,744B, BPFP=0.5006 +⌛️ [2/4] FRONTEND: Frontend time: 2.585s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.510s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09491264 12.62135758 + layer.39.0 32.22669916 3694.96501458 + ------------------------------------------------------------------------------------- + TOTAL 16.16080590 1853.79318608 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 317192 +BPFP 0.3010 bits/point +EBPFP 0.3010 equivalent bits/point +MSE 1853.793186 +---------------------- -------------------------------------------------------- +Time: 5.165s Load: 0.070s, Pack+Encode: 2.585s, Decode+Unpack: 2.510s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1853.7932 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03676483-sculpture_3.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n03676483-sculpture_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03710193-sketch_14.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03710193-sketch_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 76,096B, BPFP=0.1444 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 190,288B, BPFP=0.3612 +⌛️ [2/4] FRONTEND: Frontend time: 2.606s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.503s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.47394152 12.49450980 + layer.39.0 335.99814747 2575.81438290 + ------------------------------------------------------------------------------------- + TOTAL 168.23604450 1294.15444635 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 266384 +BPFP 0.2528 bits/point +EBPFP 0.2528 equivalent bits/point +MSE 1294.154446 +---------------------- -------------------------------------------------------- +Time: 5.179s Load: 0.070s, Pack+Encode: 2.606s, Decode+Unpack: 2.503s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1294.1544 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03710193-sketch_14.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n03710193-sketch_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03773504-graphic_4.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03773504-graphic_4.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 52,040B, BPFP=0.0988 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 145,904B, BPFP=0.2769 +⌛️ [2/4] FRONTEND: Frontend time: 2.585s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.486s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09681199 12.81238422 + layer.39.0 18.83313593 2026.64115646 + ------------------------------------------------------------------------------------- + TOTAL 9.46497396 1019.72677034 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 197944 +BPFP 0.1879 bits/point +EBPFP 0.1879 equivalent bits/point +MSE 1019.726770 +---------------------- -------------------------------------------------------- +Time: 5.123s Load: 0.052s, Pack+Encode: 2.585s, Decode+Unpack: 2.486s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1019.7268 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03773504-graphic_4.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n03773504-graphic_4.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03775071-painting_1.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03775071-painting_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 68,624B, BPFP=0.1303 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 188,424B, BPFP=0.3576 +⌛️ [2/4] FRONTEND: Frontend time: 2.591s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.520s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11048905 12.52565161 + layer.39.0 386.73560496 2733.74684159 + ------------------------------------------------------------------------------------- + TOTAL 193.42304701 1373.13624660 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 257048 +BPFP 0.2439 bits/point +EBPFP 0.2439 equivalent bits/point +MSE 1373.136247 +---------------------- -------------------------------------------------------- +Time: 5.161s Load: 0.050s, Pack+Encode: 2.591s, Decode+Unpack: 2.520s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1373.1362 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03775071-painting_1.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n03775071-painting_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03888257-cartoon_30.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03888257-cartoon_30.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 66,756B, BPFP=0.1267 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 200,332B, BPFP=0.3802 +⌛️ [2/4] FRONTEND: Frontend time: 2.582s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.482s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13203045 12.73432280 + layer.39.0 375.96832483 2731.85714286 + ------------------------------------------------------------------------------------- + TOTAL 188.05017764 1372.29573283 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 267088 +BPFP 0.2535 bits/point +EBPFP 0.2535 equivalent bits/point +MSE 1372.295733 +---------------------- -------------------------------------------------------- +Time: 5.134s Load: 0.069s, Pack+Encode: 2.582s, Decode+Unpack: 2.482s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1372.2957 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03888257-cartoon_30.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n03888257-cartoon_30.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03930630-toy_2.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03930630-toy_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 51,816B, BPFP=0.0984 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 197,444B, BPFP=0.3748 +⌛️ [2/4] FRONTEND: Frontend time: 2.582s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.500s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09699417 12.75773278 + layer.39.0 46.17573949 2676.19266278 + ------------------------------------------------------------------------------------- + TOTAL 23.13636683 1344.47519778 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 249260 +BPFP 0.2366 bits/point +EBPFP 0.2366 equivalent bits/point +MSE 1344.475198 +---------------------- -------------------------------------------------------- +Time: 5.153s Load: 0.070s, Pack+Encode: 2.582s, Decode+Unpack: 2.500s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1344.4752 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03930630-toy_2.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n03930630-toy_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04086273-sticker_5.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04086273-sticker_5.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 62,748B, BPFP=0.1191 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 158,068B, BPFP=0.3000 +⌛️ [2/4] FRONTEND: Frontend time: 2.597s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.503s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10161624 12.61805587 + layer.39.0 24.98063198 2276.79859086 + ------------------------------------------------------------------------------------- + TOTAL 12.54112411 1144.70832337 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 220816 +BPFP 0.2096 bits/point +EBPFP 0.2096 equivalent bits/point +MSE 1144.708323 +---------------------- -------------------------------------------------------- +Time: 5.151s Load: 0.051s, Pack+Encode: 2.597s, Decode+Unpack: 2.503s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1144.7083 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04086273-sticker_5.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n04086273-sticker_5.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04118538-sculpture_0.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04118538-sculpture_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 63,704B, BPFP=0.1209 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 230,032B, BPFP=0.4366 +⌛️ [2/4] FRONTEND: Frontend time: 2.589s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.527s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09846411 12.50106767 + layer.39.0 11.87055944 2913.38848397 + ------------------------------------------------------------------------------------- + TOTAL 5.98451177 1462.94477582 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 293736 +BPFP 0.2788 bits/point +EBPFP 0.2788 equivalent bits/point +MSE 1462.944776 +---------------------- -------------------------------------------------------- +Time: 5.188s Load: 0.071s, Pack+Encode: 2.589s, Decode+Unpack: 2.527s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1462.9448 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04118538-sculpture_0.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n04118538-sculpture_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04133789-cartoon_7.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04133789-cartoon_7.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 94,060B, BPFP=0.1785 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 243,576B, BPFP=0.4623 +⌛️ [2/4] FRONTEND: Frontend time: 2.595s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.524s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13739287 12.67741740 + layer.39.0 370.52532799 4270.70310982 + ------------------------------------------------------------------------------------- + TOTAL 185.33136043 2141.69026361 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 337636 +BPFP 0.3204 bits/point +EBPFP 0.3204 equivalent bits/point +MSE 2141.690264 +---------------------- -------------------------------------------------------- +Time: 5.171s Load: 0.052s, Pack+Encode: 2.595s, Decode+Unpack: 2.524s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2141.6903 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04133789-cartoon_7.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n04133789-cartoon_7.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04141076-cartoon_14.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04141076-cartoon_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.068s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 67,020B, BPFP=0.1272 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 212,084B, BPFP=0.4026 +⌛️ [2/4] FRONTEND: Frontend time: 2.602s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.492s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11960477 12.56234151 + layer.39.0 53.25505649 3373.91448008 + ------------------------------------------------------------------------------------- + TOTAL 26.68733063 1693.23841079 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 279104 +BPFP 0.2649 bits/point +EBPFP 0.2649 equivalent bits/point +MSE 1693.238411 +---------------------- -------------------------------------------------------- +Time: 5.162s Load: 0.068s, Pack+Encode: 2.602s, Decode+Unpack: 2.492s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1693.2384 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04141076-cartoon_14.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n04141076-cartoon_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04146614-misc_4.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04146614-misc_4.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 65,360B, BPFP=0.1241 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 207,852B, BPFP=0.3945 +⌛️ [2/4] FRONTEND: Frontend time: 2.585s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.488s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10047569 12.58065704 + layer.39.0 167.29959305 3104.49684159 + ------------------------------------------------------------------------------------- + TOTAL 83.70003437 1558.53874932 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 273212 +BPFP 0.2593 bits/point +EBPFP 0.2593 equivalent bits/point +MSE 1558.538749 +---------------------- -------------------------------------------------------- +Time: 5.143s Load: 0.070s, Pack+Encode: 2.585s, Decode+Unpack: 2.488s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1558.5387 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04146614-misc_4.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n04146614-misc_4.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04147183-art_9.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04147183-art_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 64,656B, BPFP=0.1227 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 179,328B, BPFP=0.3404 +⌛️ [2/4] FRONTEND: Frontend time: 2.583s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.501s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11332939 12.66414507 + layer.39.0 22.95352360 2910.53887269 + ------------------------------------------------------------------------------------- + TOTAL 11.53342649 1461.60150888 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 243984 +BPFP 0.2316 bits/point +EBPFP 0.2316 equivalent bits/point +MSE 1461.601509 +---------------------- -------------------------------------------------------- +Time: 5.155s Load: 0.071s, Pack+Encode: 2.583s, Decode+Unpack: 2.501s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1461.6015 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04147183-art_9.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n04147183-art_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04192698-videogame_16.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04192698-videogame_16.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.068s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 79,684B, BPFP=0.1512 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 198,392B, BPFP=0.3766 +⌛️ [2/4] FRONTEND: Frontend time: 2.592s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.517s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09706018 12.46336989 + layer.39.0 404.66927843 2893.02988338 + ------------------------------------------------------------------------------------- + TOTAL 202.38316930 1452.74662664 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 278076 +BPFP 0.2639 bits/point +EBPFP 0.2639 equivalent bits/point +MSE 1452.746627 +---------------------- -------------------------------------------------------- +Time: 5.178s Load: 0.068s, Pack+Encode: 2.592s, Decode+Unpack: 2.517s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1452.7466 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04192698-videogame_16.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n04192698-videogame_16.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04254680-deviantart_17.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04254680-deviantart_17.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 70,820B, BPFP=0.1344 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 200,448B, BPFP=0.3805 +⌛️ [2/4] FRONTEND: Frontend time: 2.598s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.516s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10685510 12.47001127 + layer.39.0 151.81593173 2529.54664723 + ------------------------------------------------------------------------------------- + TOTAL 75.96139341 1271.00832925 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 271268 +BPFP 0.2574 bits/point +EBPFP 0.2574 equivalent bits/point +MSE 1271.008329 +---------------------- -------------------------------------------------------- +Time: 5.184s Load: 0.069s, Pack+Encode: 2.598s, Decode+Unpack: 2.516s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1271.0083 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04254680-deviantart_17.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n04254680-deviantart_17.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04266014-painting_13.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04266014-painting_13.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 70,720B, BPFP=0.1342 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 165,908B, BPFP=0.3149 +⌛️ [2/4] FRONTEND: Frontend time: 2.587s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.535s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09568562 12.72598359 + layer.39.0 29.62437363 2477.17541302 + ------------------------------------------------------------------------------------- + TOTAL 14.86002963 1244.95069830 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 236628 +BPFP 0.2246 bits/point +EBPFP 0.2246 equivalent bits/point +MSE 1244.950698 +---------------------- -------------------------------------------------------- +Time: 5.193s Load: 0.072s, Pack+Encode: 2.587s, Decode+Unpack: 2.535s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1244.9507 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04266014-painting_13.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n04266014-painting_13.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04310018-art_3.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04310018-art_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 84,612B, BPFP=0.1606 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 207,296B, BPFP=0.3935 +⌛️ [2/4] FRONTEND: Frontend time: 2.601s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.514s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13375617 12.42127236 + layer.39.0 75.24515610 3289.09669582 + ------------------------------------------------------------------------------------- + TOTAL 37.68945614 1650.75898409 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 291908 +BPFP 0.2770 bits/point +EBPFP 0.2770 equivalent bits/point +MSE 1650.758984 +---------------------- -------------------------------------------------------- +Time: 5.186s Load: 0.070s, Pack+Encode: 2.601s, Decode+Unpack: 2.514s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1650.7590 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04310018-art_3.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n04310018-art_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04347754-sculpture_0.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04347754-sculpture_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 92,008B, BPFP=0.1746 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 167,008B, BPFP=0.3170 +⌛️ [2/4] FRONTEND: Frontend time: 2.608s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.508s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14257451 12.76677334 + layer.39.0 394.23636419 2421.10932945 + ------------------------------------------------------------------------------------- + TOTAL 197.18946935 1216.93805139 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 259016 +BPFP 0.2458 bits/point +EBPFP 0.2458 equivalent bits/point +MSE 1216.938051 +---------------------- -------------------------------------------------------- +Time: 5.188s Load: 0.071s, Pack+Encode: 2.608s, Decode+Unpack: 2.508s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1216.9381 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04347754-sculpture_0.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n04347754-sculpture_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04409515-deviantart_1.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04409515-deviantart_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 65,636B, BPFP=0.1246 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 173,952B, BPFP=0.3302 +⌛️ [2/4] FRONTEND: Frontend time: 2.613s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.512s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09627266 12.75041473 + layer.39.0 9.33068077 2075.36540330 + ------------------------------------------------------------------------------------- + TOTAL 4.71347671 1044.05790902 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 239588 +BPFP 0.2274 bits/point +EBPFP 0.2274 equivalent bits/point +MSE 1044.057909 +---------------------- -------------------------------------------------------- +Time: 5.194s Load: 0.070s, Pack+Encode: 2.613s, Decode+Unpack: 2.512s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1044.0579 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04409515-deviantart_1.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n04409515-deviantart_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04487394-deviantart_8.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04487394-deviantart_8.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 60,024B, BPFP=0.1139 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 198,016B, BPFP=0.3759 +⌛️ [2/4] FRONTEND: Frontend time: 2.590s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.492s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09911632 12.73373724 + layer.39.0 99.63155977 2716.01992225 + ------------------------------------------------------------------------------------- + TOTAL 49.86533804 1364.37682975 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 258040 +BPFP 0.2449 bits/point +EBPFP 0.2449 equivalent bits/point +MSE 1364.376830 +---------------------- -------------------------------------------------------- +Time: 5.134s Load: 0.051s, Pack+Encode: 2.590s, Decode+Unpack: 2.492s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1364.3768 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04487394-deviantart_8.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n04487394-deviantart_8.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04522168-painting_32.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04522168-painting_32.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 63,420B, BPFP=0.1204 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 154,280B, BPFP=0.2928 +⌛️ [2/4] FRONTEND: Frontend time: 2.599s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.493s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11740584 12.71969145 + layer.39.0 10.95138066 1963.26967930 + ------------------------------------------------------------------------------------- + TOTAL 5.53439325 987.99468537 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 217700 +BPFP 0.2066 bits/point +EBPFP 0.2066 equivalent bits/point +MSE 987.994685 +---------------------- -------------------------------------------------------- +Time: 5.162s Load: 0.070s, Pack+Encode: 2.599s, Decode+Unpack: 2.493s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 987.9947 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04522168-painting_32.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n04522168-painting_32.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04591713-painting_14.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04591713-painting_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 73,540B, BPFP=0.1396 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 205,040B, BPFP=0.3892 +⌛️ [2/4] FRONTEND: Frontend time: 2.613s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.502s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11212821 12.82624021 + layer.39.0 165.22564383 2467.06146744 + ------------------------------------------------------------------------------------- + TOTAL 82.66888602 1239.94385383 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 278580 +BPFP 0.2644 bits/point +EBPFP 0.2644 equivalent bits/point +MSE 1239.943854 +---------------------- -------------------------------------------------------- +Time: 5.166s Load: 0.051s, Pack+Encode: 2.613s, Decode+Unpack: 2.502s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1239.9439 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04591713-painting_14.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n04591713-painting_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07693725-sketch_15.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07693725-sketch_15.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 77,516B, BPFP=0.1471 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 196,728B, BPFP=0.3734 +⌛️ [2/4] FRONTEND: Frontend time: 2.582s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.527s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10569874 12.42982511 + layer.39.0 214.96065658 2979.80515063 + ------------------------------------------------------------------------------------- + TOTAL 107.53317766 1496.11748787 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 274244 +BPFP 0.2603 bits/point +EBPFP 0.2603 equivalent bits/point +MSE 1496.117488 +---------------------- -------------------------------------------------------- +Time: 5.180s Load: 0.070s, Pack+Encode: 2.582s, Decode+Unpack: 2.527s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1496.1175 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07693725-sketch_15.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n07693725-sketch_15.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07695742-videogame_1.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07695742-videogame_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 86,408B, BPFP=0.1640 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 201,100B, BPFP=0.3817 +⌛️ [2/4] FRONTEND: Frontend time: 2.608s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.493s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12460778 12.52978089 + layer.39.0 438.29433916 3113.13216715 + ------------------------------------------------------------------------------------- + TOTAL 219.20947347 1562.83097402 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 287508 +BPFP 0.2729 bits/point +EBPFP 0.2729 equivalent bits/point +MSE 1562.830974 +---------------------- -------------------------------------------------------- +Time: 5.170s Load: 0.069s, Pack+Encode: 2.608s, Decode+Unpack: 2.493s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1562.8310 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07695742-videogame_1.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n07695742-videogame_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697313-deviantart_7.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697313-deviantart_7.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 63,520B, BPFP=0.1206 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 180,364B, BPFP=0.3423 +⌛️ [2/4] FRONTEND: Frontend time: 2.594s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.488s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09520741 12.57444272 + layer.39.0 14.69109212 2715.35762877 + ------------------------------------------------------------------------------------- + TOTAL 7.39314977 1363.96603574 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 243884 +BPFP 0.2315 bits/point +EBPFP 0.2315 equivalent bits/point +MSE 1363.966036 +---------------------- -------------------------------------------------------- +Time: 5.152s Load: 0.070s, Pack+Encode: 2.594s, Decode+Unpack: 2.488s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1363.9660 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697313-deviantart_7.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n07697313-deviantart_7.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697537-deviantart_16.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697537-deviantart_16.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 73,624B, BPFP=0.1397 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 210,516B, BPFP=0.3996 +⌛️ [2/4] FRONTEND: Frontend time: 2.576s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.513s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09755328 12.39055952 + layer.39.0 90.32537658 3045.49489796 + ------------------------------------------------------------------------------------- + TOTAL 45.21146493 1528.94272874 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 284140 +BPFP 0.2697 bits/point +EBPFP 0.2697 equivalent bits/point +MSE 1528.942729 +---------------------- -------------------------------------------------------- +Time: 5.159s Load: 0.070s, Pack+Encode: 2.576s, Decode+Unpack: 2.513s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1528.9427 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697537-deviantart_16.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n07697537-deviantart_16.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714571-deviantart_0.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714571-deviantart_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 58,168B, BPFP=0.1104 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 235,156B, BPFP=0.4463 +⌛️ [2/4] FRONTEND: Frontend time: 2.606s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.486s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09528512 12.52080676 + layer.39.0 45.81401467 3644.61758989 + ------------------------------------------------------------------------------------- + TOTAL 22.95464989 1828.56919833 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 293324 +BPFP 0.2784 bits/point +EBPFP 0.2784 equivalent bits/point +MSE 1828.569198 +---------------------- -------------------------------------------------------- +Time: 5.161s Load: 0.070s, Pack+Encode: 2.606s, Decode+Unpack: 2.486s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1828.5692 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714571-deviantart_0.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n07714571-deviantart_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714990-toy_14.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714990-toy_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.068s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 58,132B, BPFP=0.1103 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 250,196B, BPFP=0.4749 +⌛️ [2/4] FRONTEND: Frontend time: 2.601s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.519s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09793257 8.66869287 + layer.39.0 322.50334062 4336.16132167 + ------------------------------------------------------------------------------------- + TOTAL 161.30063660 2172.41500727 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 308328 +BPFP 0.2926 bits/point +EBPFP 0.2926 equivalent bits/point +MSE 2172.415007 +---------------------- -------------------------------------------------------- +Time: 5.188s Load: 0.068s, Pack+Encode: 2.601s, Decode+Unpack: 2.519s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2172.4150 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714990-toy_14.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n07714990-toy_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07718472-cartoon_1.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07718472-cartoon_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 74,508B, BPFP=0.1414 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 149,836B, BPFP=0.2844 +⌛️ [2/4] FRONTEND: Frontend time: 2.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.501s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11235230 12.70029117 + layer.39.0 14.49942963 2024.38763362 + ------------------------------------------------------------------------------------- + TOTAL 7.30589096 1018.54396240 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 224344 +BPFP 0.2129 bits/point +EBPFP 0.2129 equivalent bits/point +MSE 1018.543962 +---------------------- -------------------------------------------------------- +Time: 5.134s Load: 0.052s, Pack+Encode: 2.581s, Decode+Unpack: 2.501s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1018.5440 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07718472-cartoon_1.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n07718472-cartoon_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07742313-deviantart_8.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07742313-deviantart_8.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.057s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 58,864B, BPFP=0.1117 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 161,300B, BPFP=0.3062 +⌛️ [2/4] FRONTEND: Frontend time: 2.570s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.494s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09669835 12.81574762 + layer.39.0 8.77690150 2030.05029155 + ------------------------------------------------------------------------------------- + TOTAL 4.43679992 1021.43301958 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 220164 +BPFP 0.2089 bits/point +EBPFP 0.2089 equivalent bits/point +MSE 1021.433020 +---------------------- -------------------------------------------------------- +Time: 5.122s Load: 0.057s, Pack+Encode: 2.570s, Decode+Unpack: 2.494s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1021.4330 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07742313-deviantart_8.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n07742313-deviantart_8.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07749582-sticker_0.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07749582-sticker_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 64,340B, BPFP=0.1221 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 229,316B, BPFP=0.4353 +⌛️ [2/4] FRONTEND: Frontend time: 2.600s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.511s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09550123 12.51922376 + layer.39.0 34.64631545 2655.78012634 + ------------------------------------------------------------------------------------- + TOTAL 17.37090834 1334.14967505 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 293656 +BPFP 0.2787 bits/point +EBPFP 0.2787 equivalent bits/point +MSE 1334.149675 +---------------------- -------------------------------------------------------- +Time: 5.161s Load: 0.050s, Pack+Encode: 2.600s, Decode+Unpack: 2.511s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1334.1497 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07749582-sticker_0.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n07749582-sticker_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07753275-painting_2.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07753275-painting_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 90,040B, BPFP=0.1709 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 227,400B, BPFP=0.4316 +⌛️ [2/4] FRONTEND: Frontend time: 2.623s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.530s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10429548 1.04856524 + layer.39.0 540.43106171 4669.89115646 + ------------------------------------------------------------------------------------- + TOTAL 270.26767859 2335.46986085 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 317440 +BPFP 0.3013 bits/point +EBPFP 0.3013 equivalent bits/point +MSE 2335.469861 +---------------------- -------------------------------------------------------- +Time: 5.203s Load: 0.050s, Pack+Encode: 2.623s, Decode+Unpack: 2.530s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2335.4699 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07753275-painting_2.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n07753275-painting_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07768694-painting_12.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07768694-painting_12.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 74,388B, BPFP=0.1412 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 232,420B, BPFP=0.4412 +⌛️ [2/4] FRONTEND: Frontend time: 2.602s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.502s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09821300 12.70120984 + layer.39.0 635.68343052 4439.77891156 + ------------------------------------------------------------------------------------- + TOTAL 317.89082176 2226.24006070 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 306808 +BPFP 0.2912 bits/point +EBPFP 0.2912 equivalent bits/point +MSE 2226.240061 +---------------------- -------------------------------------------------------- +Time: 5.154s Load: 0.051s, Pack+Encode: 2.602s, Decode+Unpack: 2.502s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2226.2401 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07768694-painting_12.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n07768694-painting_12.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07920052-painting_1.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07920052-painting_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 65,052B, BPFP=0.1235 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 221,876B, BPFP=0.4211 +⌛️ [2/4] FRONTEND: Frontend time: 2.576s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.498s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09582097 12.55323832 + layer.39.0 9.59182155 2338.77988338 + ------------------------------------------------------------------------------------- + TOTAL 4.84382126 1175.66656085 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 286928 +BPFP 0.2723 bits/point +EBPFP 0.2723 equivalent bits/point +MSE 1175.666561 +---------------------- -------------------------------------------------------- +Time: 5.145s Load: 0.071s, Pack+Encode: 2.576s, Decode+Unpack: 2.498s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1175.6666 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07920052-painting_1.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n07920052-painting_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09472597-painting_15.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09472597-painting_15.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 52,928B, BPFP=0.1005 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 155,236B, BPFP=0.2947 +⌛️ [2/4] FRONTEND: Frontend time: 2.574s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.495s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09164813 12.88160817 + layer.39.0 9.11265014 2022.74344023 + ------------------------------------------------------------------------------------- + TOTAL 4.60214913 1017.81252420 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 208164 +BPFP 0.1976 bits/point +EBPFP 0.1976 equivalent bits/point +MSE 1017.812524 +---------------------- -------------------------------------------------------- +Time: 5.139s Load: 0.071s, Pack+Encode: 2.574s, Decode+Unpack: 2.495s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1017.8125 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09472597-painting_15.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n09472597-painting_15.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09835506-videogame_3.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09835506-videogame_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 65,212B, BPFP=0.1238 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 226,680B, BPFP=0.4303 +⌛️ [2/4] FRONTEND: Frontend time: 2.580s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.479s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09585661 12.69277097 + layer.39.0 12.34450164 3190.40986395 + ------------------------------------------------------------------------------------- + TOTAL 6.22017912 1601.55131746 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 291892 +BPFP 0.2770 bits/point +EBPFP 0.2770 equivalent bits/point +MSE 1601.551317 +---------------------- -------------------------------------------------------- +Time: 5.111s Load: 0.052s, Pack+Encode: 2.580s, Decode+Unpack: 2.479s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1601.5513 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09835506-videogame_3.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n09835506-videogame_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n12267677-misc_105.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n12267677-misc_105.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 63,048B, BPFP=0.1197 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 200,892B, BPFP=0.3813 +⌛️ [2/4] FRONTEND: Frontend time: 2.585s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.494s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10166193 12.43140716 + layer.39.0 219.41089650 2955.04834791 + ------------------------------------------------------------------------------------- + TOTAL 109.75627921 1483.73987754 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 263940 +BPFP 0.2505 bits/point +EBPFP 0.2505 equivalent bits/point +MSE 1483.739878 +---------------------- -------------------------------------------------------- +Time: 5.129s Load: 0.050s, Pack+Encode: 2.585s, Decode+Unpack: 2.494s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1483.7399 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n12267677-misc_105.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kr/n12267677-misc_105.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 0.2579 bits/point +Avg EBPFP 0.2579 equivalent bits/point +Avg MSE 1566.640343 +Avg Time 5.166s +------------------------ ---------------------------- diff --git a/lambda0.004/elic-featurecoding-8bit-individual/cls_in1kval/dtufc_elic-featurecoding_dinov3-total_individual.log b/lambda0.004/elic-featurecoding-8bit-individual/cls_in1kval/dtufc_elic-featurecoding_dinov3-total_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..5876ed937a80604ba6d31e934251d0bf9ac6763d --- /dev/null +++ b/lambda0.004/elic-featurecoding-8bit-individual/cls_in1kval/dtufc_elic-featurecoding_dinov3-total_individual.log @@ -0,0 +1,9344 @@ +Experiment: dtufc_elic-featurecoding_dinov3-total_individual +Log file: output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/dtufc_elic-featurecoding_dinov3-total_individual.log +DTUFCCodecConfig: + arch: elic-featurecoding + handler: dinov3-total + checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.004_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.004_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 255 +Loaded elic-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.9' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.19' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.29' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.39' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json +Loaded per-key mappings: model=dinov3-total + Keys: ['layer.9', 'layer.19', 'layer.29', 'layer.39'] +---------------- -------------------------------------------------------------------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +Checkpoint codec_weights/elic_hybrid/elic2022-official_lambda0.004_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-DINOv3/imagenet1k-val +Output output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval +---------------- -------------------------------------------------------------------------------------------------------------------- +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02825657-ILSVRC2012_val_00001103.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02825657-ILSVRC2012_val_00001103.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 65,664B, BPFP=0.1246 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 156,984B, BPFP=0.2980 +⌛️ [2/4] FRONTEND: Frontend time: 2.933s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.555s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10264289 12.56238232 + layer.39.0 9.47367932 2260.32896016 + ------------------------------------------------------------------------------------- + TOTAL 4.78816110 1136.44567124 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 222648 +BPFP 0.2113 bits/point +EBPFP 0.2113 equivalent bits/point +MSE 1136.445671 +---------------------- -------------------------------------------------------- +Time: 5.561s Load: 0.072s, Pack+Encode: 2.933s, Decode+Unpack: 2.555s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1136.4457 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02825657-ILSVRC2012_val_00001103.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n02825657-ILSVRC2012_val_00001103.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02834397-ILSVRC2012_val_00001252.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02834397-ILSVRC2012_val_00001252.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 88,832B, BPFP=0.1686 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 221,268B, BPFP=0.4200 +⌛️ [2/4] FRONTEND: Frontend time: 2.587s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.498s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14789204 12.77998588 + layer.39.0 415.43227648 2733.31341108 + ------------------------------------------------------------------------------------- + TOTAL 207.79008426 1373.04669848 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 310100 +BPFP 0.2943 bits/point +EBPFP 0.2943 equivalent bits/point +MSE 1373.046698 +---------------------- -------------------------------------------------------- +Time: 5.143s Load: 0.059s, Pack+Encode: 2.587s, Decode+Unpack: 2.498s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1373.0467 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02834397-ILSVRC2012_val_00001252.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n02834397-ILSVRC2012_val_00001252.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02840245-ILSVRC2012_val_00003446.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02840245-ILSVRC2012_val_00003446.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 60,896B, BPFP=0.1156 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 174,884B, BPFP=0.3319 +⌛️ [2/4] FRONTEND: Frontend time: 2.578s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.487s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10761288 12.79978286 + layer.39.0 28.71820525 2330.91253644 + ------------------------------------------------------------------------------------- + TOTAL 14.41290906 1171.85615965 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 235780 +BPFP 0.2238 bits/point +EBPFP 0.2238 equivalent bits/point +MSE 1171.856160 +---------------------- -------------------------------------------------------- +Time: 5.116s Load: 0.052s, Pack+Encode: 2.578s, Decode+Unpack: 2.487s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1171.8562 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02840245-ILSVRC2012_val_00003446.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n02840245-ILSVRC2012_val_00003446.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02843684-ILSVRC2012_val_00000514.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02843684-ILSVRC2012_val_00000514.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 65,000B, BPFP=0.1234 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 193,728B, BPFP=0.3677 +⌛️ [2/4] FRONTEND: Frontend time: 2.614s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.509s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11482661 12.57799973 + layer.39.0 84.54469600 2501.65136054 + ------------------------------------------------------------------------------------- + TOTAL 42.32976130 1257.11468014 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 258728 +BPFP 0.2455 bits/point +EBPFP 0.2455 equivalent bits/point +MSE 1257.114680 +---------------------- -------------------------------------------------------- +Time: 5.175s Load: 0.051s, Pack+Encode: 2.614s, Decode+Unpack: 2.509s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1257.1147 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02843684-ILSVRC2012_val_00000514.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n02843684-ILSVRC2012_val_00000514.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02859443-ILSVRC2012_val_00000193.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02859443-ILSVRC2012_val_00000193.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 60,352B, BPFP=0.1146 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 117,804B, BPFP=0.2236 +⌛️ [2/4] FRONTEND: Frontend time: 2.583s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.503s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11417333 12.80414275 + layer.39.0 9.67809406 1531.55296404 + ------------------------------------------------------------------------------------- + TOTAL 4.89613370 772.17855340 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 178156 +BPFP 0.1691 bits/point +EBPFP 0.1691 equivalent bits/point +MSE 772.178553 +---------------------- -------------------------------------------------------- +Time: 5.156s Load: 0.070s, Pack+Encode: 2.583s, Decode+Unpack: 2.503s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 772.1786 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02859443-ILSVRC2012_val_00000193.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n02859443-ILSVRC2012_val_00000193.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02860847-ILSVRC2012_val_00000601.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02860847-ILSVRC2012_val_00000601.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 75,956B, BPFP=0.1442 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 188,340B, BPFP=0.3575 +⌛️ [2/4] FRONTEND: Frontend time: 2.610s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.501s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12653054 12.53063312 + layer.39.0 266.35249636 3799.61710398 + ------------------------------------------------------------------------------------- + TOTAL 133.23951345 1906.07386855 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 264296 +BPFP 0.2508 bits/point +EBPFP 0.2508 equivalent bits/point +MSE 1906.073869 +---------------------- -------------------------------------------------------- +Time: 5.161s Load: 0.051s, Pack+Encode: 2.610s, Decode+Unpack: 2.501s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1906.0739 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02860847-ILSVRC2012_val_00000601.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n02860847-ILSVRC2012_val_00000601.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02865351-ILSVRC2012_val_00000763.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02865351-ILSVRC2012_val_00000763.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 62,876B, BPFP=0.1193 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 200,200B, BPFP=0.3800 +⌛️ [2/4] FRONTEND: Frontend time: 2.587s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.514s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09467571 0.94406356 + layer.39.0 15.47581086 3049.96865889 + ------------------------------------------------------------------------------------- + TOTAL 7.78524328 1525.45636123 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 263076 +BPFP 0.2497 bits/point +EBPFP 0.2497 equivalent bits/point +MSE 1525.456361 +---------------------- -------------------------------------------------------- +Time: 5.152s Load: 0.052s, Pack+Encode: 2.587s, Decode+Unpack: 2.514s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1525.4564 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02865351-ILSVRC2012_val_00000763.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n02865351-ILSVRC2012_val_00000763.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02869837-ILSVRC2012_val_00000906.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02869837-ILSVRC2012_val_00000906.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 68,488B, BPFP=0.1300 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 280,816B, BPFP=0.5330 +⌛️ [2/4] FRONTEND: Frontend time: 2.611s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.503s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09659988 12.66261901 + layer.39.0 16.39405483 3299.26190476 + ------------------------------------------------------------------------------------- + TOTAL 8.24532736 1655.96226189 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 349304 +BPFP 0.3315 bits/point +EBPFP 0.3315 equivalent bits/point +MSE 1655.962262 +---------------------- -------------------------------------------------------- +Time: 5.184s Load: 0.070s, Pack+Encode: 2.611s, Decode+Unpack: 2.503s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1655.9623 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02869837-ILSVRC2012_val_00000906.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n02869837-ILSVRC2012_val_00000906.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02870880-ILSVRC2012_val_00003274.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02870880-ILSVRC2012_val_00003274.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 81,004B, BPFP=0.1538 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 180,648B, BPFP=0.3429 +⌛️ [2/4] FRONTEND: Frontend time: 2.604s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.515s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10254154 12.68463105 + layer.39.0 9.36513093 2355.21525753 + ------------------------------------------------------------------------------------- + TOTAL 4.73383623 1183.94994429 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 261652 +BPFP 0.2483 bits/point +EBPFP 0.2483 equivalent bits/point +MSE 1183.949944 +---------------------- -------------------------------------------------------- +Time: 5.170s Load: 0.051s, Pack+Encode: 2.604s, Decode+Unpack: 2.515s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1183.9499 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02870880-ILSVRC2012_val_00003274.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n02870880-ILSVRC2012_val_00003274.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02871525-ILSVRC2012_val_00000879.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02871525-ILSVRC2012_val_00000879.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 97,308B, BPFP=0.1847 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 206,252B, BPFP=0.3915 +⌛️ [2/4] FRONTEND: Frontend time: 2.605s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.523s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.17072899 1.13366047 + layer.39.0 20.29403547 2796.98493683 + ------------------------------------------------------------------------------------- + TOTAL 10.23238223 1399.05929865 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 303560 +BPFP 0.2881 bits/point +EBPFP 0.2881 equivalent bits/point +MSE 1399.059299 +---------------------- -------------------------------------------------------- +Time: 5.178s Load: 0.050s, Pack+Encode: 2.605s, Decode+Unpack: 2.523s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1399.0593 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02871525-ILSVRC2012_val_00000879.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n02871525-ILSVRC2012_val_00000879.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02877765-ILSVRC2012_val_00000634.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02877765-ILSVRC2012_val_00000634.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 60,744B, BPFP=0.1153 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 235,980B, BPFP=0.4479 +⌛️ [2/4] FRONTEND: Frontend time: 2.599s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.496s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10908128 12.51342228 + layer.39.0 364.97770894 5312.99416910 + ------------------------------------------------------------------------------------- + TOTAL 182.54339511 2662.75379569 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 296724 +BPFP 0.2816 bits/point +EBPFP 0.2816 equivalent bits/point +MSE 2662.753796 +---------------------- -------------------------------------------------------- +Time: 5.147s Load: 0.052s, Pack+Encode: 2.599s, Decode+Unpack: 2.496s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2662.7538 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02877765-ILSVRC2012_val_00000634.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n02877765-ILSVRC2012_val_00000634.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02879718-ILSVRC2012_val_00001354.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02879718-ILSVRC2012_val_00001354.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 64,788B, BPFP=0.1230 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 234,468B, BPFP=0.4450 +⌛️ [2/4] FRONTEND: Frontend time: 2.595s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.500s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10948122 12.55395864 + layer.39.0 55.92460444 4467.76724976 + ------------------------------------------------------------------------------------- + TOTAL 28.01704283 2240.16060420 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 299256 +BPFP 0.2840 bits/point +EBPFP 0.2840 equivalent bits/point +MSE 2240.160604 +---------------------- -------------------------------------------------------- +Time: 5.166s Load: 0.071s, Pack+Encode: 2.595s, Decode+Unpack: 2.500s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2240.1606 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02879718-ILSVRC2012_val_00001354.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n02879718-ILSVRC2012_val_00001354.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02883205-ILSVRC2012_val_00000126.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02883205-ILSVRC2012_val_00000126.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 48,252B, BPFP=0.0916 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 180,264B, BPFP=0.3422 +⌛️ [2/4] FRONTEND: Frontend time: 2.602s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.484s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.06711708 12.91817659 + layer.39.0 7.82069686 2228.84207969 + ------------------------------------------------------------------------------------- + TOTAL 7.94390697 1120.88012814 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 228516 +BPFP 0.2169 bits/point +EBPFP 0.2169 equivalent bits/point +MSE 1120.880128 +---------------------- -------------------------------------------------------- +Time: 5.137s Load: 0.051s, Pack+Encode: 2.602s, Decode+Unpack: 2.484s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1120.8801 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02883205-ILSVRC2012_val_00000126.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n02883205-ILSVRC2012_val_00000126.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892201-ILSVRC2012_val_00001145.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892201-ILSVRC2012_val_00001145.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.055s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 82,904B, BPFP=0.1574 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 205,040B, BPFP=0.3892 +⌛️ [2/4] FRONTEND: Frontend time: 2.587s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.513s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11297333 12.62108426 + layer.39.0 15.09638643 2659.06924198 + ------------------------------------------------------------------------------------- + TOTAL 7.60467988 1335.84516312 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 287944 +BPFP 0.2733 bits/point +EBPFP 0.2733 equivalent bits/point +MSE 1335.845163 +---------------------- -------------------------------------------------------- +Time: 5.154s Load: 0.055s, Pack+Encode: 2.587s, Decode+Unpack: 2.513s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1335.8452 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892201-ILSVRC2012_val_00001145.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n02892201-ILSVRC2012_val_00001145.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892767-ILSVRC2012_val_00000808.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892767-ILSVRC2012_val_00000808.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 54,656B, BPFP=0.1037 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 271,120B, BPFP=0.5146 +⌛️ [2/4] FRONTEND: Frontend time: 2.582s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.495s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09598007 12.62034306 + layer.39.0 31.15013059 3489.20845481 + ------------------------------------------------------------------------------------- + TOTAL 15.62305533 1750.91439893 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 325776 +BPFP 0.3092 bits/point +EBPFP 0.3092 equivalent bits/point +MSE 1750.914399 +---------------------- -------------------------------------------------------- +Time: 5.130s Load: 0.052s, Pack+Encode: 2.582s, Decode+Unpack: 2.495s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1750.9144 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892767-ILSVRC2012_val_00000808.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n02892767-ILSVRC2012_val_00000808.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02895154-ILSVRC2012_val_00000080.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02895154-ILSVRC2012_val_00000080.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 60,076B, BPFP=0.1140 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 279,364B, BPFP=0.5303 +⌛️ [2/4] FRONTEND: Frontend time: 2.593s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.505s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09530723 12.60290919 + layer.39.0 971.40427600 5276.83333333 + ------------------------------------------------------------------------------------- + TOTAL 485.74979162 2644.71812126 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 339440 +BPFP 0.3221 bits/point +EBPFP 0.3221 equivalent bits/point +MSE 2644.718121 +---------------------- -------------------------------------------------------- +Time: 5.159s Load: 0.061s, Pack+Encode: 2.593s, Decode+Unpack: 2.505s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2644.7181 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02895154-ILSVRC2012_val_00000080.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n02895154-ILSVRC2012_val_00000080.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02906734-ILSVRC2012_val_00002937.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02906734-ILSVRC2012_val_00002937.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 62,640B, BPFP=0.1189 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 166,832B, BPFP=0.3167 +⌛️ [2/4] FRONTEND: Frontend time: 2.597s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.497s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09767962 12.60787647 + layer.39.0 32.09536716 2425.75315841 + ------------------------------------------------------------------------------------- + TOTAL 16.09652339 1219.18051744 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 229472 +BPFP 0.2178 bits/point +EBPFP 0.2178 equivalent bits/point +MSE 1219.180517 +---------------------- -------------------------------------------------------- +Time: 5.144s Load: 0.050s, Pack+Encode: 2.597s, Decode+Unpack: 2.497s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1219.1805 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02906734-ILSVRC2012_val_00002937.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n02906734-ILSVRC2012_val_00002937.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02910353-ILSVRC2012_val_00000558.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02910353-ILSVRC2012_val_00000558.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 75,712B, BPFP=0.1437 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 215,380B, BPFP=0.4088 +⌛️ [2/4] FRONTEND: Frontend time: 2.583s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.482s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11017090 12.55424809 + layer.39.0 483.40066205 3118.49902818 + ------------------------------------------------------------------------------------- + TOTAL 241.75541648 1565.52663814 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 291092 +BPFP 0.2763 bits/point +EBPFP 0.2763 equivalent bits/point +MSE 1565.526638 +---------------------- -------------------------------------------------------- +Time: 5.126s Load: 0.061s, Pack+Encode: 2.583s, Decode+Unpack: 2.482s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1565.5266 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02910353-ILSVRC2012_val_00000558.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n02910353-ILSVRC2012_val_00000558.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02916936-ILSVRC2012_val_00000366.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02916936-ILSVRC2012_val_00000366.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 61,268B, BPFP=0.1163 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 226,444B, BPFP=0.4298 +⌛️ [2/4] FRONTEND: Frontend time: 2.584s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.495s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10657579 12.59308567 + layer.39.0 435.18944363 4018.49271137 + ------------------------------------------------------------------------------------- + TOTAL 217.64800971 2015.54289852 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 287712 +BPFP 0.2731 bits/point +EBPFP 0.2731 equivalent bits/point +MSE 2015.542899 +---------------------- -------------------------------------------------------- +Time: 5.141s Load: 0.062s, Pack+Encode: 2.584s, Decode+Unpack: 2.495s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2015.5429 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02916936-ILSVRC2012_val_00000366.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n02916936-ILSVRC2012_val_00000366.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02917067-ILSVRC2012_val_00000562.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02917067-ILSVRC2012_val_00000562.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 81,116B, BPFP=0.1540 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 227,984B, BPFP=0.4327 +⌛️ [2/4] FRONTEND: Frontend time: 2.615s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.504s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10760244 12.40752931 + layer.39.0 37.55795979 3857.41302235 + ------------------------------------------------------------------------------------- + TOTAL 18.83278111 1934.91027583 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 309100 +BPFP 0.2933 bits/point +EBPFP 0.2933 equivalent bits/point +MSE 1934.910276 +---------------------- -------------------------------------------------------- +Time: 5.179s Load: 0.061s, Pack+Encode: 2.615s, Decode+Unpack: 2.504s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1934.9103 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02917067-ILSVRC2012_val_00000562.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n02917067-ILSVRC2012_val_00000562.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02930766-ILSVRC2012_val_00000056.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02930766-ILSVRC2012_val_00000056.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 74,056B, BPFP=0.1406 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 241,152B, BPFP=0.4577 +⌛️ [2/4] FRONTEND: Frontend time: 2.602s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.503s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10591127 12.61770947 + layer.39.0 18.32421875 3314.87730807 + ------------------------------------------------------------------------------------- + TOTAL 9.21506501 1663.74750877 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 315208 +BPFP 0.2991 bits/point +EBPFP 0.2991 equivalent bits/point +MSE 1663.747509 +---------------------- -------------------------------------------------------- +Time: 5.154s Load: 0.050s, Pack+Encode: 2.602s, Decode+Unpack: 2.503s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1663.7475 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02930766-ILSVRC2012_val_00000056.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n02930766-ILSVRC2012_val_00000056.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02939185-ILSVRC2012_val_00000302.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02939185-ILSVRC2012_val_00000302.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 61,352B, BPFP=0.1165 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 189,284B, BPFP=0.3593 +⌛️ [2/4] FRONTEND: Frontend time: 2.578s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.496s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09694758 12.59451303 + layer.39.0 25.52453269 3152.45432459 + ------------------------------------------------------------------------------------- + TOTAL 12.81074014 1582.52441881 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 250636 +BPFP 0.2379 bits/point +EBPFP 0.2379 equivalent bits/point +MSE 1582.524419 +---------------------- -------------------------------------------------------- +Time: 5.143s Load: 0.069s, Pack+Encode: 2.578s, Decode+Unpack: 2.496s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1582.5244 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02939185-ILSVRC2012_val_00000302.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n02939185-ILSVRC2012_val_00000302.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02950826-ILSVRC2012_val_00000392.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02950826-ILSVRC2012_val_00000392.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 69,244B, BPFP=0.1314 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 211,916B, BPFP=0.4022 +⌛️ [2/4] FRONTEND: Frontend time: 2.621s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.505s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10873010 12.48159146 + layer.39.0 707.96944849 4069.24586978 + ------------------------------------------------------------------------------------- + TOTAL 354.03908930 2040.86373062 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 281160 +BPFP 0.2668 bits/point +EBPFP 0.2668 equivalent bits/point +MSE 2040.863731 +---------------------- -------------------------------------------------------- +Time: 5.175s Load: 0.050s, Pack+Encode: 2.621s, Decode+Unpack: 2.505s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2040.8637 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02950826-ILSVRC2012_val_00000392.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n02950826-ILSVRC2012_val_00000392.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951358-ILSVRC2012_val_00000347.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951358-ILSVRC2012_val_00000347.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 71,312B, BPFP=0.1354 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 173,748B, BPFP=0.3298 +⌛️ [2/4] FRONTEND: Frontend time: 2.599s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.480s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12200860 12.45962972 + layer.39.0 237.66299198 3560.27672498 + ------------------------------------------------------------------------------------- + TOTAL 118.89250029 1786.36817735 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 245060 +BPFP 0.2326 bits/point +EBPFP 0.2326 equivalent bits/point +MSE 1786.368177 +---------------------- -------------------------------------------------------- +Time: 5.140s Load: 0.061s, Pack+Encode: 2.599s, Decode+Unpack: 2.480s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1786.3682 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951358-ILSVRC2012_val_00000347.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n02951358-ILSVRC2012_val_00000347.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951585-ILSVRC2012_val_00000101.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951585-ILSVRC2012_val_00000101.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 54,572B, BPFP=0.1036 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 196,968B, BPFP=0.3739 +⌛️ [2/4] FRONTEND: Frontend time: 2.598s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.487s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.07385432 12.73835433 + layer.39.0 181.90962099 3277.66253644 + ------------------------------------------------------------------------------------- + TOTAL 94.99173765 1645.20044538 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 251540 +BPFP 0.2387 bits/point +EBPFP 0.2387 equivalent bits/point +MSE 1645.200445 +---------------------- -------------------------------------------------------- +Time: 5.147s Load: 0.062s, Pack+Encode: 2.598s, Decode+Unpack: 2.487s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1645.2004 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951585-ILSVRC2012_val_00000101.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n02951585-ILSVRC2012_val_00000101.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02963159-ILSVRC2012_val_00000061.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02963159-ILSVRC2012_val_00000061.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 65,812B, BPFP=0.1249 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 196,364B, BPFP=0.3727 +⌛️ [2/4] FRONTEND: Frontend time: 2.600s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.512s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11232698 12.57715793 + layer.39.0 24.77479842 2276.95602527 + ------------------------------------------------------------------------------------- + TOTAL 12.44356270 1144.76659160 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 262176 +BPFP 0.2488 bits/point +EBPFP 0.2488 equivalent bits/point +MSE 1144.766592 +---------------------- -------------------------------------------------------- +Time: 5.164s Load: 0.052s, Pack+Encode: 2.600s, Decode+Unpack: 2.512s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1144.7666 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02963159-ILSVRC2012_val_00000061.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n02963159-ILSVRC2012_val_00000061.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02965783-ILSVRC2012_val_00000213.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02965783-ILSVRC2012_val_00000213.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 58,804B, BPFP=0.1116 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 238,872B, BPFP=0.4534 +⌛️ [2/4] FRONTEND: Frontend time: 2.592s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.510s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09516161 12.61854653 + layer.39.0 223.32294704 3130.79251701 + ------------------------------------------------------------------------------------- + TOTAL 111.70905432 1571.70553177 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 297676 +BPFP 0.2825 bits/point +EBPFP 0.2825 equivalent bits/point +MSE 1571.705532 +---------------------- -------------------------------------------------------- +Time: 5.164s Load: 0.061s, Pack+Encode: 2.592s, Decode+Unpack: 2.510s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1571.7055 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02965783-ILSVRC2012_val_00000213.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n02965783-ILSVRC2012_val_00000213.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966193-ILSVRC2012_val_00000074.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966193-ILSVRC2012_val_00000074.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 82,800B, BPFP=0.1572 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 338,592B, BPFP=0.6427 +⌛️ [2/4] FRONTEND: Frontend time: 2.586s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.512s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12190965 1.09031803 + layer.39.0 378.75431244 7292.87755102 + ------------------------------------------------------------------------------------- + TOTAL 189.43811104 3646.98393453 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 421392 +BPFP 0.3999 bits/point +EBPFP 0.3999 equivalent bits/point +MSE 3646.983935 +---------------------- -------------------------------------------------------- +Time: 5.150s Load: 0.052s, Pack+Encode: 2.586s, Decode+Unpack: 2.512s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3646.9839 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966193-ILSVRC2012_val_00000074.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n02966193-ILSVRC2012_val_00000074.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966687-ILSVRC2012_val_00001041.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966687-ILSVRC2012_val_00001041.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 72,716B, BPFP=0.1380 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 250,556B, BPFP=0.4756 +⌛️ [2/4] FRONTEND: Frontend time: 2.625s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.511s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12487827 12.56795698 + layer.39.0 254.07423773 3687.29689018 + ------------------------------------------------------------------------------------- + TOTAL 127.09955800 1849.93242358 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 323272 +BPFP 0.3068 bits/point +EBPFP 0.3068 equivalent bits/point +MSE 1849.932424 +---------------------- -------------------------------------------------------- +Time: 5.188s Load: 0.051s, Pack+Encode: 2.625s, Decode+Unpack: 2.511s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1849.9324 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966687-ILSVRC2012_val_00001041.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n02966687-ILSVRC2012_val_00001041.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02971356-ILSVRC2012_val_00000019.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02971356-ILSVRC2012_val_00000019.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 57,780B, BPFP=0.1097 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 151,200B, BPFP=0.2870 +⌛️ [2/4] FRONTEND: Frontend time: 2.598s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.517s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09754465 12.62267011 + layer.39.0 24.51746044 1940.26214772 + ------------------------------------------------------------------------------------- + TOTAL 12.30750255 976.44240891 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 208980 +BPFP 0.1983 bits/point +EBPFP 0.1983 equivalent bits/point +MSE 976.442409 +---------------------- -------------------------------------------------------- +Time: 5.186s Load: 0.071s, Pack+Encode: 2.598s, Decode+Unpack: 2.517s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 976.4424 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02971356-ILSVRC2012_val_00000019.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n02971356-ILSVRC2012_val_00000019.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02978881-ILSVRC2012_val_00000353.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02978881-ILSVRC2012_val_00000353.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 67,384B, BPFP=0.1279 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 248,648B, BPFP=0.4720 +⌛️ [2/4] FRONTEND: Frontend time: 2.598s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.503s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09975241 12.36916720 + layer.39.0 226.62124939 3696.09305151 + ------------------------------------------------------------------------------------- + TOTAL 113.36050090 1854.23110935 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 316032 +BPFP 0.2999 bits/point +EBPFP 0.2999 equivalent bits/point +MSE 1854.231109 +---------------------- -------------------------------------------------------- +Time: 5.151s Load: 0.051s, Pack+Encode: 2.598s, Decode+Unpack: 2.503s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1854.2311 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02978881-ILSVRC2012_val_00000353.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n02978881-ILSVRC2012_val_00000353.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02980441-ILSVRC2012_val_00000122.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02980441-ILSVRC2012_val_00000122.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 55,252B, BPFP=0.1049 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 152,484B, BPFP=0.2894 +⌛️ [2/4] FRONTEND: Frontend time: 2.589s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.479s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10186533 12.62129115 + layer.39.0 8.25151846 2235.62220603 + ------------------------------------------------------------------------------------- + TOTAL 4.17669190 1124.12174859 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 207736 +BPFP 0.1971 bits/point +EBPFP 0.1971 equivalent bits/point +MSE 1124.121749 +---------------------- -------------------------------------------------------- +Time: 5.129s Load: 0.061s, Pack+Encode: 2.589s, Decode+Unpack: 2.479s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1124.1217 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02980441-ILSVRC2012_val_00000122.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n02980441-ILSVRC2012_val_00000122.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02988304-ILSVRC2012_val_00003491.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02988304-ILSVRC2012_val_00003491.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 71,196B, BPFP=0.1351 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 200,912B, BPFP=0.3813 +⌛️ [2/4] FRONTEND: Frontend time: 2.597s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.511s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10176498 12.47346199 + layer.39.0 516.16180758 3983.51943635 + ------------------------------------------------------------------------------------- + TOTAL 258.13178628 1997.99644917 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 272108 +BPFP 0.2582 bits/point +EBPFP 0.2582 equivalent bits/point +MSE 1997.996449 +---------------------- -------------------------------------------------------- +Time: 5.160s Load: 0.052s, Pack+Encode: 2.597s, Decode+Unpack: 2.511s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1997.9964 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02988304-ILSVRC2012_val_00003491.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n02988304-ILSVRC2012_val_00003491.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992211-ILSVRC2012_val_00000108.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992211-ILSVRC2012_val_00000108.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.056s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 70,200B, BPFP=0.1332 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 242,212B, BPFP=0.4597 +⌛️ [2/4] FRONTEND: Frontend time: 2.574s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.516s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10107529 12.77353905 + layer.39.0 89.13089923 4934.33138970 + ------------------------------------------------------------------------------------- + TOTAL 44.61598726 2473.55246437 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 312412 +BPFP 0.2965 bits/point +EBPFP 0.2965 equivalent bits/point +MSE 2473.552464 +---------------------- -------------------------------------------------------- +Time: 5.146s Load: 0.056s, Pack+Encode: 2.574s, Decode+Unpack: 2.516s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2473.5525 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992211-ILSVRC2012_val_00000108.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n02992211-ILSVRC2012_val_00000108.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992529-ILSVRC2012_val_00000089.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992529-ILSVRC2012_val_00000089.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.058s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 66,572B, BPFP=0.1264 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 268,112B, BPFP=0.5089 +⌛️ [2/4] FRONTEND: Frontend time: 2.610s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.504s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11197385 12.59066657 + layer.39.0 964.25631681 5050.17978620 + ------------------------------------------------------------------------------------- + TOTAL 482.18414533 2531.38522638 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 334684 +BPFP 0.3176 bits/point +EBPFP 0.3176 equivalent bits/point +MSE 2531.385226 +---------------------- -------------------------------------------------------- +Time: 5.171s Load: 0.058s, Pack+Encode: 2.610s, Decode+Unpack: 2.504s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2531.3852 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992529-ILSVRC2012_val_00000089.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n02992529-ILSVRC2012_val_00000089.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02999410-ILSVRC2012_val_00000376.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02999410-ILSVRC2012_val_00000376.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 69,140B, BPFP=0.1312 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 191,496B, BPFP=0.3635 +⌛️ [2/4] FRONTEND: Frontend time: 2.583s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.491s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10398186 12.62878287 + layer.39.0 145.78410471 2789.15257532 + ------------------------------------------------------------------------------------- + TOTAL 72.94404329 1400.89067910 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 260636 +BPFP 0.2474 bits/point +EBPFP 0.2474 equivalent bits/point +MSE 1400.890679 +---------------------- -------------------------------------------------------- +Time: 5.136s Load: 0.061s, Pack+Encode: 2.583s, Decode+Unpack: 2.491s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1400.8907 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02999410-ILSVRC2012_val_00000376.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n02999410-ILSVRC2012_val_00000376.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000134-ILSVRC2012_val_00001094.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000134-ILSVRC2012_val_00001094.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 55,588B, BPFP=0.1055 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 259,884B, BPFP=0.4933 +⌛️ [2/4] FRONTEND: Frontend time: 2.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.510s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09696872 12.70464916 + layer.39.0 22.81329530 3216.12925170 + ------------------------------------------------------------------------------------- + TOTAL 11.45513201 1614.41695043 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 315472 +BPFP 0.2994 bits/point +EBPFP 0.2994 equivalent bits/point +MSE 1614.416950 +---------------------- -------------------------------------------------------- +Time: 5.162s Load: 0.070s, Pack+Encode: 2.581s, Decode+Unpack: 2.510s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1614.4170 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000134-ILSVRC2012_val_00001094.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03000134-ILSVRC2012_val_00001094.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000247-ILSVRC2012_val_00002280.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000247-ILSVRC2012_val_00002280.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.058s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 155,700B, BPFP=0.2955 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 200,536B, BPFP=0.3806 +⌛️ [2/4] FRONTEND: Frontend time: 2.589s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.509s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.29135144 12.90547843 + layer.39.0 428.26293732 3512.19582119 + ------------------------------------------------------------------------------------- + TOTAL 214.27714438 1762.55064981 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 356236 +BPFP 0.3381 bits/point +EBPFP 0.3381 equivalent bits/point +MSE 1762.550650 +---------------------- -------------------------------------------------------- +Time: 5.155s Load: 0.058s, Pack+Encode: 2.589s, Decode+Unpack: 2.509s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1762.5506 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000247-ILSVRC2012_val_00002280.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03000247-ILSVRC2012_val_00002280.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000684-ILSVRC2012_val_00000537.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000684-ILSVRC2012_val_00000537.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.057s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 82,796B, BPFP=0.1572 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 258,740B, BPFP=0.4911 +⌛️ [2/4] FRONTEND: Frontend time: 2.612s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.523s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13150742 12.70484656 + layer.39.0 55.24585459 2947.35276968 + ------------------------------------------------------------------------------------- + TOTAL 27.68868101 1480.02880812 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 341536 +BPFP 0.3241 bits/point +EBPFP 0.3241 equivalent bits/point +MSE 1480.028808 +---------------------- -------------------------------------------------------- +Time: 5.192s Load: 0.057s, Pack+Encode: 2.612s, Decode+Unpack: 2.523s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1480.0288 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000684-ILSVRC2012_val_00000537.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03000684-ILSVRC2012_val_00000537.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03014705-ILSVRC2012_val_00001168.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03014705-ILSVRC2012_val_00001168.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 55,584B, BPFP=0.1055 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 203,728B, BPFP=0.3867 +⌛️ [2/4] FRONTEND: Frontend time: 2.574s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.495s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09787338 12.59396069 + layer.39.0 322.89622813 3457.47667638 + ------------------------------------------------------------------------------------- + TOTAL 161.49705076 1735.03531854 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 259312 +BPFP 0.2461 bits/point +EBPFP 0.2461 equivalent bits/point +MSE 1735.035319 +---------------------- -------------------------------------------------------- +Time: 5.130s Load: 0.061s, Pack+Encode: 2.574s, Decode+Unpack: 2.495s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1735.0353 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03014705-ILSVRC2012_val_00001168.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03014705-ILSVRC2012_val_00001168.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03017168-ILSVRC2012_val_00001601.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03017168-ILSVRC2012_val_00001601.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 65,296B, BPFP=0.1239 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 235,256B, BPFP=0.4465 +⌛️ [2/4] FRONTEND: Frontend time: 2.598s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.486s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10213913 12.17212555 + layer.39.0 475.40952988 4564.93731778 + ------------------------------------------------------------------------------------- + TOTAL 237.75583451 2288.55472167 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 300552 +BPFP 0.2852 bits/point +EBPFP 0.2852 equivalent bits/point +MSE 2288.554722 +---------------------- -------------------------------------------------------- +Time: 5.146s Load: 0.062s, Pack+Encode: 2.598s, Decode+Unpack: 2.486s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2288.5547 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03017168-ILSVRC2012_val_00001601.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03017168-ILSVRC2012_val_00001601.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03018349-ILSVRC2012_val_00000346.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03018349-ILSVRC2012_val_00000346.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 60,148B, BPFP=0.1142 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 304,304B, BPFP=0.5776 +⌛️ [2/4] FRONTEND: Frontend time: 2.612s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.504s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09959339 12.47993444 + layer.39.0 56.59841169 3693.79081633 + ------------------------------------------------------------------------------------- + TOTAL 28.34900254 1853.13537538 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 364452 +BPFP 0.3459 bits/point +EBPFP 0.3459 equivalent bits/point +MSE 1853.135375 +---------------------- -------------------------------------------------------- +Time: 5.166s Load: 0.050s, Pack+Encode: 2.612s, Decode+Unpack: 2.504s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1853.1354 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03018349-ILSVRC2012_val_00000346.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03018349-ILSVRC2012_val_00000346.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03026506-ILSVRC2012_val_00001908.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03026506-ILSVRC2012_val_00001908.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 60,424B, BPFP=0.1147 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 279,484B, BPFP=0.5305 +⌛️ [2/4] FRONTEND: Frontend time: 2.601s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.503s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10977067 12.49668405 + layer.39.0 668.54063411 5592.68367347 + ------------------------------------------------------------------------------------- + TOTAL 334.32520239 2802.59017876 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 339908 +BPFP 0.3226 bits/point +EBPFP 0.3226 equivalent bits/point +MSE 2802.590179 +---------------------- -------------------------------------------------------- +Time: 5.174s Load: 0.070s, Pack+Encode: 2.601s, Decode+Unpack: 2.503s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2802.5902 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03026506-ILSVRC2012_val_00001908.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03026506-ILSVRC2012_val_00001908.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03028079-ILSVRC2012_val_00003351.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03028079-ILSVRC2012_val_00003351.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 58,148B, BPFP=0.1104 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 166,172B, BPFP=0.3154 +⌛️ [2/4] FRONTEND: Frontend time: 2.615s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.508s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10934904 12.80434679 + layer.39.0 15.31112010 2444.71841594 + ------------------------------------------------------------------------------------- + TOTAL 7.71023457 1228.76138137 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 224320 +BPFP 0.2129 bits/point +EBPFP 0.2129 equivalent bits/point +MSE 1228.761381 +---------------------- -------------------------------------------------------- +Time: 5.173s Load: 0.050s, Pack+Encode: 2.615s, Decode+Unpack: 2.508s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1228.7614 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03028079-ILSVRC2012_val_00003351.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03028079-ILSVRC2012_val_00003351.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03032252-ILSVRC2012_val_00000086.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03032252-ILSVRC2012_val_00000086.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 89,748B, BPFP=0.1703 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 237,536B, BPFP=0.4509 +⌛️ [2/4] FRONTEND: Frontend time: 2.623s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.525s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13507480 12.94903559 + layer.39.0 103.55165816 3166.70189504 + ------------------------------------------------------------------------------------- + TOTAL 51.84336648 1589.82546531 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 327284 +BPFP 0.3106 bits/point +EBPFP 0.3106 equivalent bits/point +MSE 1589.825465 +---------------------- -------------------------------------------------------- +Time: 5.199s Load: 0.052s, Pack+Encode: 2.623s, Decode+Unpack: 2.525s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1589.8255 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03032252-ILSVRC2012_val_00000086.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03032252-ILSVRC2012_val_00000086.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03041632-ILSVRC2012_val_00000564.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03041632-ILSVRC2012_val_00000564.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 68,144B, BPFP=0.1293 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 216,340B, BPFP=0.4106 +⌛️ [2/4] FRONTEND: Frontend time: 2.597s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.507s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10123130 12.62002228 + layer.39.0 371.34277818 5353.80709427 + ------------------------------------------------------------------------------------- + TOTAL 185.72200474 2683.21355827 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 284484 +BPFP 0.2700 bits/point +EBPFP 0.2700 equivalent bits/point +MSE 2683.213558 +---------------------- -------------------------------------------------------- +Time: 5.175s Load: 0.071s, Pack+Encode: 2.597s, Decode+Unpack: 2.507s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2683.2136 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03041632-ILSVRC2012_val_00000564.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03041632-ILSVRC2012_val_00000564.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03042490-ILSVRC2012_val_00001426.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03042490-ILSVRC2012_val_00001426.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 66,772B, BPFP=0.1267 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 197,676B, BPFP=0.3752 +⌛️ [2/4] FRONTEND: Frontend time: 2.582s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.497s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10706725 12.88336294 + layer.39.0 141.71039845 3024.86564626 + ------------------------------------------------------------------------------------- + TOTAL 70.90873285 1518.87450460 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 264448 +BPFP 0.2510 bits/point +EBPFP 0.2510 equivalent bits/point +MSE 1518.874505 +---------------------- -------------------------------------------------------- +Time: 5.131s Load: 0.051s, Pack+Encode: 2.582s, Decode+Unpack: 2.497s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1518.8745 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03042490-ILSVRC2012_val_00001426.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03042490-ILSVRC2012_val_00001426.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03047690-ILSVRC2012_val_00001500.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03047690-ILSVRC2012_val_00001500.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 56,508B, BPFP=0.1073 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 277,796B, BPFP=0.5273 +⌛️ [2/4] FRONTEND: Frontend time: 2.591s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.483s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09570478 12.48529842 + layer.39.0 226.76483540 5098.29446064 + ------------------------------------------------------------------------------------- + TOTAL 113.43027009 2555.38987953 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 334304 +BPFP 0.3173 bits/point +EBPFP 0.3173 equivalent bits/point +MSE 2555.389880 +---------------------- -------------------------------------------------------- +Time: 5.125s Load: 0.051s, Pack+Encode: 2.591s, Decode+Unpack: 2.483s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2555.3899 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03047690-ILSVRC2012_val_00001500.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03047690-ILSVRC2012_val_00001500.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03062245-ILSVRC2012_val_00000344.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03062245-ILSVRC2012_val_00000344.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 52,856B, BPFP=0.1003 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 168,756B, BPFP=0.3203 +⌛️ [2/4] FRONTEND: Frontend time: 2.574s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.492s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09619164 12.69402750 + layer.39.0 46.71096787 2324.38168124 + ------------------------------------------------------------------------------------- + TOTAL 23.40357976 1168.53785437 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 221612 +BPFP 0.2103 bits/point +EBPFP 0.2103 equivalent bits/point +MSE 1168.537854 +---------------------- -------------------------------------------------------- +Time: 5.127s Load: 0.061s, Pack+Encode: 2.574s, Decode+Unpack: 2.492s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1168.5379 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03062245-ILSVRC2012_val_00000344.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03062245-ILSVRC2012_val_00000344.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063599-ILSVRC2012_val_00000164.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063599-ILSVRC2012_val_00000164.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 61,992B, BPFP=0.1177 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 267,864B, BPFP=0.5084 +⌛️ [2/4] FRONTEND: Frontend time: 2.590s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.505s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10111790 12.46776869 + layer.39.0 9.80528160 3535.61564626 + ------------------------------------------------------------------------------------- + TOTAL 4.95319975 1774.04170748 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 329856 +BPFP 0.3130 bits/point +EBPFP 0.3130 equivalent bits/point +MSE 1774.041707 +---------------------- -------------------------------------------------------- +Time: 5.146s Load: 0.051s, Pack+Encode: 2.590s, Decode+Unpack: 2.505s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1774.0417 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063599-ILSVRC2012_val_00000164.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03063599-ILSVRC2012_val_00000164.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063689-ILSVRC2012_val_00001940.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063689-ILSVRC2012_val_00001940.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 60,544B, BPFP=0.1149 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 249,328B, BPFP=0.4732 +⌛️ [2/4] FRONTEND: Frontend time: 2.605s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.492s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09645106 12.60083174 + layer.39.0 18.48014797 4137.32264334 + ------------------------------------------------------------------------------------- + TOTAL 9.28829952 2074.96173754 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 309872 +BPFP 0.2941 bits/point +EBPFP 0.2941 equivalent bits/point +MSE 2074.961738 +---------------------- -------------------------------------------------------- +Time: 5.158s Load: 0.060s, Pack+Encode: 2.605s, Decode+Unpack: 2.492s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2074.9617 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063689-ILSVRC2012_val_00001940.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03063689-ILSVRC2012_val_00001940.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03065424-ILSVRC2012_val_00000915.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03065424-ILSVRC2012_val_00000915.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 90,932B, BPFP=0.1726 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 288,172B, BPFP=0.5470 +⌛️ [2/4] FRONTEND: Frontend time: 2.591s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.486s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12384982 12.68830099 + layer.39.0 2154.15986395 5834.76870748 + ------------------------------------------------------------------------------------- + TOTAL 1077.14185688 2923.72850424 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 379104 +BPFP 0.3598 bits/point +EBPFP 0.3598 equivalent bits/point +MSE 2923.728504 +---------------------- -------------------------------------------------------- +Time: 5.139s Load: 0.062s, Pack+Encode: 2.591s, Decode+Unpack: 2.486s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2923.7285 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03065424-ILSVRC2012_val_00000915.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03065424-ILSVRC2012_val_00000915.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03075370-ILSVRC2012_val_00004971.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03075370-ILSVRC2012_val_00004971.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 54,424B, BPFP=0.1033 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 185,732B, BPFP=0.3525 +⌛️ [2/4] FRONTEND: Frontend time: 2.606s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.503s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10672879 12.87503701 + layer.39.0 301.29020894 2665.27551020 + ------------------------------------------------------------------------------------- + TOTAL 150.69846886 1339.07527361 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 240156 +BPFP 0.2279 bits/point +EBPFP 0.2279 equivalent bits/point +MSE 1339.075274 +---------------------- -------------------------------------------------------- +Time: 5.159s Load: 0.051s, Pack+Encode: 2.606s, Decode+Unpack: 2.503s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1339.0753 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03075370-ILSVRC2012_val_00004971.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03075370-ILSVRC2012_val_00004971.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03089624-ILSVRC2012_val_00001190.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03089624-ILSVRC2012_val_00001190.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 67,864B, BPFP=0.1288 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 231,096B, BPFP=0.4386 +⌛️ [2/4] FRONTEND: Frontend time: 2.593s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.492s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10385029 12.49968207 + layer.39.0 606.38896987 4529.39698737 + ------------------------------------------------------------------------------------- + TOTAL 303.24641008 2270.94833472 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 298960 +BPFP 0.2837 bits/point +EBPFP 0.2837 equivalent bits/point +MSE 2270.948335 +---------------------- -------------------------------------------------------- +Time: 5.135s Load: 0.050s, Pack+Encode: 2.593s, Decode+Unpack: 2.492s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2270.9483 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03089624-ILSVRC2012_val_00001190.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03089624-ILSVRC2012_val_00001190.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03095699-ILSVRC2012_val_00000403.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03095699-ILSVRC2012_val_00000403.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 83,976B, BPFP=0.1594 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 251,360B, BPFP=0.4771 +⌛️ [2/4] FRONTEND: Frontend time: 2.576s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.482s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12139760 12.70875186 + layer.39.0 62.59250486 4802.66423712 + ------------------------------------------------------------------------------------- + TOTAL 31.35695123 2407.68649449 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 335336 +BPFP 0.3182 bits/point +EBPFP 0.3182 equivalent bits/point +MSE 2407.686494 +---------------------- -------------------------------------------------------- +Time: 5.110s Load: 0.051s, Pack+Encode: 2.576s, Decode+Unpack: 2.482s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2407.6865 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03095699-ILSVRC2012_val_00000403.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03095699-ILSVRC2012_val_00000403.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03100240-ILSVRC2012_val_00001201.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03100240-ILSVRC2012_val_00001201.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 68,800B, BPFP=0.1306 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 167,992B, BPFP=0.3189 +⌛️ [2/4] FRONTEND: Frontend time: 2.587s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.505s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10258218 12.49723070 + layer.39.0 42.98202138 2325.67687075 + ------------------------------------------------------------------------------------- + TOTAL 21.54230178 1169.08705072 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 236792 +BPFP 0.2247 bits/point +EBPFP 0.2247 equivalent bits/point +MSE 1169.087051 +---------------------- -------------------------------------------------------- +Time: 5.163s Load: 0.071s, Pack+Encode: 2.587s, Decode+Unpack: 2.505s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1169.0871 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03100240-ILSVRC2012_val_00001201.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03100240-ILSVRC2012_val_00001201.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03109150-ILSVRC2012_val_00000678.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03109150-ILSVRC2012_val_00000678.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 68,984B, BPFP=0.1309 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 237,116B, BPFP=0.4501 +⌛️ [2/4] FRONTEND: Frontend time: 2.602s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.491s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09720685 12.53482219 + layer.39.0 496.21158285 5306.36200194 + ------------------------------------------------------------------------------------- + TOTAL 248.15439485 2659.44841207 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 306100 +BPFP 0.2905 bits/point +EBPFP 0.2905 equivalent bits/point +MSE 2659.448412 +---------------------- -------------------------------------------------------- +Time: 5.144s Load: 0.052s, Pack+Encode: 2.602s, Decode+Unpack: 2.491s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2659.4484 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03109150-ILSVRC2012_val_00000678.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03109150-ILSVRC2012_val_00000678.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03110669-ILSVRC2012_val_00002171.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03110669-ILSVRC2012_val_00002171.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 87,924B, BPFP=0.1669 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 283,476B, BPFP=0.5381 +⌛️ [2/4] FRONTEND: Frontend time: 2.596s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.489s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.15128201 12.66261426 + layer.39.0 15.00769387 3696.57774538 + ------------------------------------------------------------------------------------- + TOTAL 7.57948794 1854.62017982 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 371400 +BPFP 0.3525 bits/point +EBPFP 0.3525 equivalent bits/point +MSE 1854.620180 +---------------------- -------------------------------------------------------- +Time: 5.154s Load: 0.070s, Pack+Encode: 2.596s, Decode+Unpack: 2.489s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1854.6202 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03110669-ILSVRC2012_val_00002171.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03110669-ILSVRC2012_val_00002171.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124043-ILSVRC2012_val_00000766.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124043-ILSVRC2012_val_00000766.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 73,320B, BPFP=0.1392 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 256,780B, BPFP=0.4874 +⌛️ [2/4] FRONTEND: Frontend time: 2.595s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.514s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11473456 12.49034636 + layer.39.0 54.83309418 5438.34013605 + ------------------------------------------------------------------------------------- + TOTAL 27.47391437 2725.41524121 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 330100 +BPFP 0.3133 bits/point +EBPFP 0.3133 equivalent bits/point +MSE 2725.415241 +---------------------- -------------------------------------------------------- +Time: 5.169s Load: 0.061s, Pack+Encode: 2.595s, Decode+Unpack: 2.514s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2725.4152 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124043-ILSVRC2012_val_00000766.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03124043-ILSVRC2012_val_00000766.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124170-ILSVRC2012_val_00001875.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124170-ILSVRC2012_val_00001875.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 61,072B, BPFP=0.1159 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 150,912B, BPFP=0.2864 +⌛️ [2/4] FRONTEND: Frontend time: 2.604s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.496s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11393612 12.62289313 + layer.39.0 9.06747107 2308.78765792 + ------------------------------------------------------------------------------------- + TOTAL 4.59070360 1160.70527553 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 211984 +BPFP 0.2012 bits/point +EBPFP 0.2012 equivalent bits/point +MSE 1160.705276 +---------------------- -------------------------------------------------------- +Time: 5.170s Load: 0.070s, Pack+Encode: 2.604s, Decode+Unpack: 2.496s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1160.7053 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124170-ILSVRC2012_val_00001875.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03124170-ILSVRC2012_val_00001875.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03126707-ILSVRC2012_val_00000020.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03126707-ILSVRC2012_val_00000020.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 75,216B, BPFP=0.1428 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 159,880B, BPFP=0.3035 +⌛️ [2/4] FRONTEND: Frontend time: 2.590s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.501s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.15273996 12.77672877 + layer.39.0 1033.15269679 2781.17808552 + ------------------------------------------------------------------------------------- + TOTAL 516.65271838 1396.97740715 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 235096 +BPFP 0.2231 bits/point +EBPFP 0.2231 equivalent bits/point +MSE 1396.977407 +---------------------- -------------------------------------------------------- +Time: 5.163s Load: 0.071s, Pack+Encode: 2.590s, Decode+Unpack: 2.501s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1396.9774 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03126707-ILSVRC2012_val_00000020.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03126707-ILSVRC2012_val_00000020.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03127747-ILSVRC2012_val_00001689.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03127747-ILSVRC2012_val_00001689.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 61,020B, BPFP=0.1158 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 203,260B, BPFP=0.3858 +⌛️ [2/4] FRONTEND: Frontend time: 2.580s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.516s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10152024 12.43658702 + layer.39.0 322.92343902 3013.14358601 + ------------------------------------------------------------------------------------- + TOTAL 161.51247963 1512.79008651 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 264280 +BPFP 0.2508 bits/point +EBPFP 0.2508 equivalent bits/point +MSE 1512.790087 +---------------------- -------------------------------------------------------- +Time: 5.167s Load: 0.071s, Pack+Encode: 2.580s, Decode+Unpack: 2.516s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1512.7901 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03127747-ILSVRC2012_val_00001689.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03127747-ILSVRC2012_val_00001689.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03131574-ILSVRC2012_val_00003036.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03131574-ILSVRC2012_val_00003036.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 59,184B, BPFP=0.1123 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 247,708B, BPFP=0.4702 +⌛️ [2/4] FRONTEND: Frontend time: 2.583s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.486s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09568423 12.51359310 + layer.39.0 163.24681122 6348.38192420 + ------------------------------------------------------------------------------------- + TOTAL 81.67124773 3180.44775865 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 306892 +BPFP 0.2913 bits/point +EBPFP 0.2913 equivalent bits/point +MSE 3180.447759 +---------------------- -------------------------------------------------------- +Time: 5.120s Load: 0.051s, Pack+Encode: 2.583s, Decode+Unpack: 2.486s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3180.4478 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03131574-ILSVRC2012_val_00003036.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03131574-ILSVRC2012_val_00003036.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03133878-ILSVRC2012_val_00000534.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03133878-ILSVRC2012_val_00000534.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 81,072B, BPFP=0.1539 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 254,340B, BPFP=0.4828 +⌛️ [2/4] FRONTEND: Frontend time: 2.587s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.506s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11186348 12.46860954 + layer.39.0 28.46096218 5550.45432459 + ------------------------------------------------------------------------------------- + TOTAL 14.28641283 2781.46146706 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 335412 +BPFP 0.3183 bits/point +EBPFP 0.3183 equivalent bits/point +MSE 2781.461467 +---------------------- -------------------------------------------------------- +Time: 5.164s Load: 0.071s, Pack+Encode: 2.587s, Decode+Unpack: 2.506s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2781.4615 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03133878-ILSVRC2012_val_00000534.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03133878-ILSVRC2012_val_00000534.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03134739-ILSVRC2012_val_00000249.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03134739-ILSVRC2012_val_00000249.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 56,716B, BPFP=0.1077 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 299,456B, BPFP=0.5684 +⌛️ [2/4] FRONTEND: Frontend time: 2.577s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.503s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09967384 12.64402636 + layer.39.0 372.24465500 4849.64237123 + ------------------------------------------------------------------------------------- + TOTAL 186.17216442 2431.14319880 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 356172 +BPFP 0.3380 bits/point +EBPFP 0.3380 equivalent bits/point +MSE 2431.143199 +---------------------- -------------------------------------------------------- +Time: 5.152s Load: 0.072s, Pack+Encode: 2.577s, Decode+Unpack: 2.503s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2431.1432 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03134739-ILSVRC2012_val_00000249.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03134739-ILSVRC2012_val_00000249.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03141823-ILSVRC2012_val_00001337.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03141823-ILSVRC2012_val_00001337.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.058s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 65,052B, BPFP=0.1235 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 314,000B, BPFP=0.5960 +⌛️ [2/4] FRONTEND: Frontend time: 2.602s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.505s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10422104 12.42717634 + layer.39.0 29.45558301 4027.09450923 + ------------------------------------------------------------------------------------- + TOTAL 14.77990203 2019.76084279 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 379052 +BPFP 0.3597 bits/point +EBPFP 0.3597 equivalent bits/point +MSE 2019.760843 +---------------------- -------------------------------------------------------- +Time: 5.165s Load: 0.058s, Pack+Encode: 2.602s, Decode+Unpack: 2.505s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2019.7608 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03141823-ILSVRC2012_val_00001337.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03141823-ILSVRC2012_val_00001337.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03160309-ILSVRC2012_val_00000330.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03160309-ILSVRC2012_val_00000330.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 63,460B, BPFP=0.1205 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 133,608B, BPFP=0.2536 +⌛️ [2/4] FRONTEND: Frontend time: 2.570s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.511s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09980877 12.63648624 + layer.39.0 30.04123011 1820.36686103 + ------------------------------------------------------------------------------------- + TOTAL 15.07051944 916.50167363 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 197068 +BPFP 0.1870 bits/point +EBPFP 0.1870 equivalent bits/point +MSE 916.501674 +---------------------- -------------------------------------------------------- +Time: 5.142s Load: 0.061s, Pack+Encode: 2.570s, Decode+Unpack: 2.511s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 916.5017 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03160309-ILSVRC2012_val_00000330.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03160309-ILSVRC2012_val_00000330.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03187595-ILSVRC2012_val_00000137.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03187595-ILSVRC2012_val_00000137.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 62,228B, BPFP=0.1181 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 248,944B, BPFP=0.4725 +⌛️ [2/4] FRONTEND: Frontend time: 2.585s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.500s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10716813 12.58539845 + layer.39.0 12.39187394 3463.05417881 + ------------------------------------------------------------------------------------- + TOTAL 6.24952103 1737.81978863 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 311172 +BPFP 0.2953 bits/point +EBPFP 0.2953 equivalent bits/point +MSE 1737.819789 +---------------------- -------------------------------------------------------- +Time: 5.146s Load: 0.061s, Pack+Encode: 2.585s, Decode+Unpack: 2.500s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1737.8198 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03187595-ILSVRC2012_val_00000137.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03187595-ILSVRC2012_val_00000137.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03188531-ILSVRC2012_val_00000493.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03188531-ILSVRC2012_val_00000493.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 53,804B, BPFP=0.1021 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 216,804B, BPFP=0.4115 +⌛️ [2/4] FRONTEND: Frontend time: 2.599s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.494s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09509044 12.68938005 + layer.39.0 10.77256154 2645.19193392 + ------------------------------------------------------------------------------------- + TOTAL 5.43382599 1328.94065698 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 270608 +BPFP 0.2568 bits/point +EBPFP 0.2568 equivalent bits/point +MSE 1328.940657 +---------------------- -------------------------------------------------------- +Time: 5.154s Load: 0.061s, Pack+Encode: 2.599s, Decode+Unpack: 2.494s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1328.9407 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03188531-ILSVRC2012_val_00000493.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03188531-ILSVRC2012_val_00000493.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03196217-ILSVRC2012_val_00003643.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03196217-ILSVRC2012_val_00003643.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 56,124B, BPFP=0.1065 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 231,280B, BPFP=0.4390 +⌛️ [2/4] FRONTEND: Frontend time: 2.578s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.511s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09478207 12.70790532 + layer.39.0 65.57403274 3224.19873664 + ------------------------------------------------------------------------------------- + TOTAL 32.83440740 1618.45332098 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 287404 +BPFP 0.2728 bits/point +EBPFP 0.2728 equivalent bits/point +MSE 1618.453321 +---------------------- -------------------------------------------------------- +Time: 5.150s Load: 0.061s, Pack+Encode: 2.578s, Decode+Unpack: 2.511s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1618.4533 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03196217-ILSVRC2012_val_00003643.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03196217-ILSVRC2012_val_00003643.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03201208-ILSVRC2012_val_00000241.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03201208-ILSVRC2012_val_00000241.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 64,616B, BPFP=0.1226 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 179,736B, BPFP=0.3412 +⌛️ [2/4] FRONTEND: Frontend time: 2.609s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.503s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10331685 12.50564774 + layer.39.0 136.59314261 2241.81875607 + ------------------------------------------------------------------------------------- + TOTAL 68.34822973 1127.16220191 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 244352 +BPFP 0.2319 bits/point +EBPFP 0.2319 equivalent bits/point +MSE 1127.162202 +---------------------- -------------------------------------------------------- +Time: 5.163s Load: 0.051s, Pack+Encode: 2.609s, Decode+Unpack: 2.503s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1127.1622 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03201208-ILSVRC2012_val_00000241.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03201208-ILSVRC2012_val_00000241.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03207743-ILSVRC2012_val_00000256.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03207743-ILSVRC2012_val_00000256.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 96,580B, BPFP=0.1833 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 178,364B, BPFP=0.3385 +⌛️ [2/4] FRONTEND: Frontend time: 2.617s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.528s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09674843 12.72200426 + layer.39.0 189.63590258 2581.10179786 + ------------------------------------------------------------------------------------- + TOTAL 94.86632550 1296.91190106 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 274944 +BPFP 0.2609 bits/point +EBPFP 0.2609 equivalent bits/point +MSE 1296.911901 +---------------------- -------------------------------------------------------- +Time: 5.214s Load: 0.069s, Pack+Encode: 2.617s, Decode+Unpack: 2.528s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1296.9119 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03207743-ILSVRC2012_val_00000256.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03207743-ILSVRC2012_val_00000256.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03216828-ILSVRC2012_val_00001729.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03216828-ILSVRC2012_val_00001729.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.054s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 72,932B, BPFP=0.1384 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 173,592B, BPFP=0.3295 +⌛️ [2/4] FRONTEND: Frontend time: 2.598s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.501s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10800209 12.65294639 + layer.39.0 31.30713223 2438.21622935 + ------------------------------------------------------------------------------------- + TOTAL 15.70756716 1225.43458787 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 246524 +BPFP 0.2340 bits/point +EBPFP 0.2340 equivalent bits/point +MSE 1225.434588 +---------------------- -------------------------------------------------------- +Time: 5.153s Load: 0.054s, Pack+Encode: 2.598s, Decode+Unpack: 2.501s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1225.4346 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03216828-ILSVRC2012_val_00001729.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03216828-ILSVRC2012_val_00001729.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03218198-ILSVRC2012_val_00002266.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03218198-ILSVRC2012_val_00002266.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 70,272B, BPFP=0.1334 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 274,412B, BPFP=0.5209 +⌛️ [2/4] FRONTEND: Frontend time: 2.597s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.492s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11617067 12.64896232 + layer.39.0 195.83184524 5037.65451895 + ------------------------------------------------------------------------------------- + TOTAL 97.97400795 2525.15174063 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 344684 +BPFP 0.3271 bits/point +EBPFP 0.3271 equivalent bits/point +MSE 2525.151741 +---------------------- -------------------------------------------------------- +Time: 5.139s Load: 0.050s, Pack+Encode: 2.597s, Decode+Unpack: 2.492s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2525.1517 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03218198-ILSVRC2012_val_00002266.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03218198-ILSVRC2012_val_00002266.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03220513-ILSVRC2012_val_00001868.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03220513-ILSVRC2012_val_00001868.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 147,384B, BPFP=0.2797 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 256,168B, BPFP=0.4862 +⌛️ [2/4] FRONTEND: Frontend time: 2.600s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.532s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.20032125 12.94792331 + layer.39.0 377.00176142 3438.56268222 + ------------------------------------------------------------------------------------- + TOTAL 188.60104134 1725.75530276 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 403552 +BPFP 0.3830 bits/point +EBPFP 0.3830 equivalent bits/point +MSE 1725.755303 +---------------------- -------------------------------------------------------- +Time: 5.181s Load: 0.050s, Pack+Encode: 2.600s, Decode+Unpack: 2.532s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1725.7553 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03220513-ILSVRC2012_val_00001868.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03220513-ILSVRC2012_val_00001868.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03223299-ILSVRC2012_val_00001893.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03223299-ILSVRC2012_val_00001893.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 63,916B, BPFP=0.1213 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 173,968B, BPFP=0.3302 +⌛️ [2/4] FRONTEND: Frontend time: 2.582s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.506s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10735053 12.57445601 + layer.39.0 354.51621720 2521.72351798 + ------------------------------------------------------------------------------------- + TOTAL 177.31178386 1267.14898699 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 237884 +BPFP 0.2258 bits/point +EBPFP 0.2258 equivalent bits/point +MSE 1267.148987 +---------------------- -------------------------------------------------------- +Time: 5.159s Load: 0.070s, Pack+Encode: 2.582s, Decode+Unpack: 2.506s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1267.1490 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03223299-ILSVRC2012_val_00001893.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03223299-ILSVRC2012_val_00001893.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03240683-ILSVRC2012_val_00000504.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03240683-ILSVRC2012_val_00000504.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 64,528B, BPFP=0.1225 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 203,704B, BPFP=0.3866 +⌛️ [2/4] FRONTEND: Frontend time: 2.576s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.517s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10065408 12.51267823 + layer.39.0 443.53838678 3506.65889213 + ------------------------------------------------------------------------------------- + TOTAL 221.81952043 1759.58578518 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 268232 +BPFP 0.2546 bits/point +EBPFP 0.2546 equivalent bits/point +MSE 1759.585785 +---------------------- -------------------------------------------------------- +Time: 5.144s Load: 0.051s, Pack+Encode: 2.576s, Decode+Unpack: 2.517s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1759.5858 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03240683-ILSVRC2012_val_00000504.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03240683-ILSVRC2012_val_00000504.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03250847-ILSVRC2012_val_00000542.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03250847-ILSVRC2012_val_00000542.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 61,784B, BPFP=0.1173 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 308,652B, BPFP=0.5858 +⌛️ [2/4] FRONTEND: Frontend time: 2.595s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.515s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10136319 12.49929012 + layer.39.0 140.24735787 4901.31049563 + ------------------------------------------------------------------------------------- + TOTAL 70.17436053 2456.90489287 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 370436 +BPFP 0.3516 bits/point +EBPFP 0.3516 equivalent bits/point +MSE 2456.904893 +---------------------- -------------------------------------------------------- +Time: 5.160s Load: 0.050s, Pack+Encode: 2.595s, Decode+Unpack: 2.515s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2456.9049 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03250847-ILSVRC2012_val_00000542.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03250847-ILSVRC2012_val_00000542.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03255030-ILSVRC2012_val_00001045.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03255030-ILSVRC2012_val_00001045.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 59,400B, BPFP=0.1127 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 199,492B, BPFP=0.3787 +⌛️ [2/4] FRONTEND: Frontend time: 2.602s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.505s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10050351 12.61511669 + layer.39.0 12.06722622 2728.23372206 + ------------------------------------------------------------------------------------- + TOTAL 6.08386487 1370.42441938 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 258892 +BPFP 0.2457 bits/point +EBPFP 0.2457 equivalent bits/point +MSE 1370.424419 +---------------------- -------------------------------------------------------- +Time: 5.177s Load: 0.070s, Pack+Encode: 2.602s, Decode+Unpack: 2.505s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1370.4244 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03255030-ILSVRC2012_val_00001045.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03255030-ILSVRC2012_val_00001045.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03271574-ILSVRC2012_val_00000942.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03271574-ILSVRC2012_val_00000942.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 61,344B, BPFP=0.1164 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 227,752B, BPFP=0.4323 +⌛️ [2/4] FRONTEND: Frontend time: 2.575s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.479s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10164264 12.60705744 + layer.39.0 660.63544704 3739.01700680 + ------------------------------------------------------------------------------------- + TOTAL 330.36854484 1875.81203212 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 289096 +BPFP 0.2744 bits/point +EBPFP 0.2744 equivalent bits/point +MSE 1875.812032 +---------------------- -------------------------------------------------------- +Time: 5.113s Load: 0.059s, Pack+Encode: 2.575s, Decode+Unpack: 2.479s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1875.8120 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03271574-ILSVRC2012_val_00000942.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03271574-ILSVRC2012_val_00000942.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272010-ILSVRC2012_val_00000374.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272010-ILSVRC2012_val_00000374.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 59,092B, BPFP=0.1122 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 221,096B, BPFP=0.4197 +⌛️ [2/4] FRONTEND: Frontend time: 2.579s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.488s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10420663 12.80561471 + layer.39.0 9.63653369 3084.69800777 + ------------------------------------------------------------------------------------- + TOTAL 4.87037016 1548.75181124 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 280188 +BPFP 0.2659 bits/point +EBPFP 0.2659 equivalent bits/point +MSE 1548.751811 +---------------------- -------------------------------------------------------- +Time: 5.128s Load: 0.061s, Pack+Encode: 2.579s, Decode+Unpack: 2.488s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1548.7518 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272010-ILSVRC2012_val_00000374.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03272010-ILSVRC2012_val_00000374.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272562-ILSVRC2012_val_00001699.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272562-ILSVRC2012_val_00001699.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 79,104B, BPFP=0.1501 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 175,236B, BPFP=0.3326 +⌛️ [2/4] FRONTEND: Frontend time: 2.588s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.483s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11399285 12.37973951 + layer.39.0 12.79457642 2370.84985423 + ------------------------------------------------------------------------------------- + TOTAL 6.45428464 1191.61479687 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 254340 +BPFP 0.2414 bits/point +EBPFP 0.2414 equivalent bits/point +MSE 1191.614797 +---------------------- -------------------------------------------------------- +Time: 5.143s Load: 0.071s, Pack+Encode: 2.588s, Decode+Unpack: 2.483s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1191.6148 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272562-ILSVRC2012_val_00001699.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03272562-ILSVRC2012_val_00001699.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03290653-ILSVRC2012_val_00000199.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03290653-ILSVRC2012_val_00000199.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 64,144B, BPFP=0.1218 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 208,348B, BPFP=0.3955 +⌛️ [2/4] FRONTEND: Frontend time: 2.582s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.499s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09581849 12.43854394 + layer.39.0 9.30266794 2400.85179786 + ------------------------------------------------------------------------------------- + TOTAL 4.69924322 1206.64517090 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 272492 +BPFP 0.2586 bits/point +EBPFP 0.2586 equivalent bits/point +MSE 1206.645171 +---------------------- -------------------------------------------------------- +Time: 5.133s Load: 0.051s, Pack+Encode: 2.582s, Decode+Unpack: 2.499s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1206.6452 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03290653-ILSVRC2012_val_00000199.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03290653-ILSVRC2012_val_00000199.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03291819-ILSVRC2012_val_00000419.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03291819-ILSVRC2012_val_00000419.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 52,888B, BPFP=0.1004 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 167,692B, BPFP=0.3183 +⌛️ [2/4] FRONTEND: Frontend time: 2.596s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.483s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10621172 12.31814869 + layer.39.0 31.36357166 2180.76117590 + ------------------------------------------------------------------------------------- + TOTAL 15.73489169 1096.53966229 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 220580 +BPFP 0.2093 bits/point +EBPFP 0.2093 equivalent bits/point +MSE 1096.539662 +---------------------- -------------------------------------------------------- +Time: 5.131s Load: 0.052s, Pack+Encode: 2.596s, Decode+Unpack: 2.483s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1096.5397 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03291819-ILSVRC2012_val_00000419.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03291819-ILSVRC2012_val_00000419.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03314780-ILSVRC2012_val_00000624.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03314780-ILSVRC2012_val_00000624.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 60,512B, BPFP=0.1149 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 284,828B, BPFP=0.5406 +⌛️ [2/4] FRONTEND: Frontend time: 2.595s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.495s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10172509 12.54274762 + layer.39.0 35.60390853 5049.18075802 + ------------------------------------------------------------------------------------- + TOTAL 17.85281681 2530.86175282 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 345340 +BPFP 0.3277 bits/point +EBPFP 0.3277 equivalent bits/point +MSE 2530.861753 +---------------------- -------------------------------------------------------- +Time: 5.150s Load: 0.061s, Pack+Encode: 2.595s, Decode+Unpack: 2.495s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2530.8618 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03314780-ILSVRC2012_val_00000624.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03314780-ILSVRC2012_val_00000624.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03325584-ILSVRC2012_val_00001256.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03325584-ILSVRC2012_val_00001256.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 69,148B, BPFP=0.1312 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 243,780B, BPFP=0.4627 +⌛️ [2/4] FRONTEND: Frontend time: 2.614s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.514s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11348933 12.77164666 + layer.39.0 26.85401292 3938.44922255 + ------------------------------------------------------------------------------------- + TOTAL 13.48375113 1975.61043460 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 312928 +BPFP 0.2970 bits/point +EBPFP 0.2970 equivalent bits/point +MSE 1975.610435 +---------------------- -------------------------------------------------------- +Time: 5.178s Load: 0.050s, Pack+Encode: 2.614s, Decode+Unpack: 2.514s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1975.6104 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03325584-ILSVRC2012_val_00001256.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03325584-ILSVRC2012_val_00001256.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03337140-ILSVRC2012_val_00000132.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03337140-ILSVRC2012_val_00000132.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.056s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 61,656B, BPFP=0.1170 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 184,104B, BPFP=0.3494 +⌛️ [2/4] FRONTEND: Frontend time: 2.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.502s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09852950 12.57270123 + layer.39.0 10.39905343 2541.26724976 + ------------------------------------------------------------------------------------- + TOTAL 5.24879146 1276.91997550 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 245760 +BPFP 0.2332 bits/point +EBPFP 0.2332 equivalent bits/point +MSE 1276.919975 +---------------------- -------------------------------------------------------- +Time: 5.140s Load: 0.056s, Pack+Encode: 2.581s, Decode+Unpack: 2.502s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1276.9200 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03337140-ILSVRC2012_val_00000132.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03337140-ILSVRC2012_val_00000132.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03344393-ILSVRC2012_val_00000288.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03344393-ILSVRC2012_val_00000288.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 63,820B, BPFP=0.1211 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 194,172B, BPFP=0.3686 +⌛️ [2/4] FRONTEND: Frontend time: 2.594s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.510s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09830858 12.70790532 + layer.39.0 109.00505649 2675.91958212 + ------------------------------------------------------------------------------------- + TOTAL 54.55168253 1344.31374372 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 257992 +BPFP 0.2448 bits/point +EBPFP 0.2448 equivalent bits/point +MSE 1344.313744 +---------------------- -------------------------------------------------------- +Time: 5.164s Load: 0.060s, Pack+Encode: 2.594s, Decode+Unpack: 2.510s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1344.3137 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03344393-ILSVRC2012_val_00000288.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03344393-ILSVRC2012_val_00000288.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03345487-ILSVRC2012_val_00000764.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03345487-ILSVRC2012_val_00000764.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 63,108B, BPFP=0.1198 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 232,468B, BPFP=0.4412 +⌛️ [2/4] FRONTEND: Frontend time: 2.583s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.503s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10639974 12.76788372 + layer.39.0 14.55993569 4084.56365403 + ------------------------------------------------------------------------------------- + TOTAL 7.33316771 2048.66576887 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 295576 +BPFP 0.2805 bits/point +EBPFP 0.2805 equivalent bits/point +MSE 2048.665769 +---------------------- -------------------------------------------------------- +Time: 5.147s Load: 0.061s, Pack+Encode: 2.583s, Decode+Unpack: 2.503s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2048.6658 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03345487-ILSVRC2012_val_00000764.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03345487-ILSVRC2012_val_00000764.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03347037-ILSVRC2012_val_00000743.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03347037-ILSVRC2012_val_00000743.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 92,320B, BPFP=0.1752 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 241,716B, BPFP=0.4588 +⌛️ [2/4] FRONTEND: Frontend time: 2.612s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.510s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14351733 12.54144649 + layer.39.0 355.98426871 4110.33041788 + ------------------------------------------------------------------------------------- + TOTAL 178.06389302 2061.43593219 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 334036 +BPFP 0.3170 bits/point +EBPFP 0.3170 equivalent bits/point +MSE 2061.435932 +---------------------- -------------------------------------------------------- +Time: 5.172s Load: 0.051s, Pack+Encode: 2.612s, Decode+Unpack: 2.510s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2061.4359 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03347037-ILSVRC2012_val_00000743.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03347037-ILSVRC2012_val_00000743.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03355925-ILSVRC2012_val_00000445.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03355925-ILSVRC2012_val_00000445.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 55,064B, BPFP=0.1045 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 141,012B, BPFP=0.2677 +⌛️ [2/4] FRONTEND: Frontend time: 2.585s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.501s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09979894 12.72583743 + layer.39.0 9.06502540 1945.46404276 + ------------------------------------------------------------------------------------- + TOTAL 4.58241217 979.09494010 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 196076 +BPFP 0.1861 bits/point +EBPFP 0.1861 equivalent bits/point +MSE 979.094940 +---------------------- -------------------------------------------------------- +Time: 5.137s Load: 0.051s, Pack+Encode: 2.585s, Decode+Unpack: 2.501s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 979.0949 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03355925-ILSVRC2012_val_00000445.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03355925-ILSVRC2012_val_00000445.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03376595-ILSVRC2012_val_00001616.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03376595-ILSVRC2012_val_00001616.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 73,100B, BPFP=0.1387 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 263,928B, BPFP=0.5010 +⌛️ [2/4] FRONTEND: Frontend time: 2.594s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.478s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09988844 12.52968788 + layer.39.0 1408.20760447 5832.40573372 + ------------------------------------------------------------------------------------- + TOTAL 704.15374646 2922.46771080 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 337028 +BPFP 0.3199 bits/point +EBPFP 0.3199 equivalent bits/point +MSE 2922.467711 +---------------------- -------------------------------------------------------- +Time: 5.133s Load: 0.061s, Pack+Encode: 2.594s, Decode+Unpack: 2.478s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2922.4677 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03376595-ILSVRC2012_val_00001616.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03376595-ILSVRC2012_val_00001616.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03379051-ILSVRC2012_val_00002562.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03379051-ILSVRC2012_val_00002562.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 65,844B, BPFP=0.1250 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 281,308B, BPFP=0.5339 +⌛️ [2/4] FRONTEND: Frontend time: 2.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.489s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10889592 12.63231520 + layer.39.0 102.95462828 4075.29616132 + ------------------------------------------------------------------------------------- + TOTAL 51.53176210 2043.96423826 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 347152 +BPFP 0.3295 bits/point +EBPFP 0.3295 equivalent bits/point +MSE 2043.964238 +---------------------- -------------------------------------------------------- +Time: 5.122s Load: 0.052s, Pack+Encode: 2.581s, Decode+Unpack: 2.489s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2043.9642 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03379051-ILSVRC2012_val_00002562.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03379051-ILSVRC2012_val_00002562.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388043-ILSVRC2012_val_00001018.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388043-ILSVRC2012_val_00001018.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 62,416B, BPFP=0.1185 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 173,008B, BPFP=0.3284 +⌛️ [2/4] FRONTEND: Frontend time: 2.585s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.506s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09747427 12.69305568 + layer.39.0 21.12933142 2366.32021380 + ------------------------------------------------------------------------------------- + TOTAL 10.61340285 1189.50663474 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 235424 +BPFP 0.2234 bits/point +EBPFP 0.2234 equivalent bits/point +MSE 1189.506635 +---------------------- -------------------------------------------------------- +Time: 5.142s Load: 0.051s, Pack+Encode: 2.585s, Decode+Unpack: 2.506s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1189.5066 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388043-ILSVRC2012_val_00001018.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03388043-ILSVRC2012_val_00001018.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388183-ILSVRC2012_val_00002799.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388183-ILSVRC2012_val_00002799.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 62,748B, BPFP=0.1191 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 237,412B, BPFP=0.4506 +⌛️ [2/4] FRONTEND: Frontend time: 2.596s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.492s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10066175 12.65869568 + layer.39.0 786.68810739 4237.31632653 + ------------------------------------------------------------------------------------- + TOTAL 393.39438457 2124.98751110 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 300160 +BPFP 0.2849 bits/point +EBPFP 0.2849 equivalent bits/point +MSE 2124.987511 +---------------------- -------------------------------------------------------- +Time: 5.149s Load: 0.061s, Pack+Encode: 2.596s, Decode+Unpack: 2.492s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2124.9875 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388183-ILSVRC2012_val_00002799.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03388183-ILSVRC2012_val_00002799.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388549-ILSVRC2012_val_00002945.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388549-ILSVRC2012_val_00002945.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 59,312B, BPFP=0.1126 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 222,976B, BPFP=0.4232 +⌛️ [2/4] FRONTEND: Frontend time: 2.583s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.514s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09849939 12.57197142 + layer.39.0 10.79426799 3603.14261419 + ------------------------------------------------------------------------------------- + TOTAL 5.44638369 1807.85729281 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 282288 +BPFP 0.2679 bits/point +EBPFP 0.2679 equivalent bits/point +MSE 1807.857293 +---------------------- -------------------------------------------------------- +Time: 5.167s Load: 0.070s, Pack+Encode: 2.583s, Decode+Unpack: 2.514s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1807.8573 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388549-ILSVRC2012_val_00002945.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03388549-ILSVRC2012_val_00002945.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03393912-ILSVRC2012_val_00000047.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03393912-ILSVRC2012_val_00000047.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 60,000B, BPFP=0.1139 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 219,100B, BPFP=0.4159 +⌛️ [2/4] FRONTEND: Frontend time: 2.605s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.514s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09729456 12.61172957 + layer.39.0 38.26720800 5248.19679300 + ------------------------------------------------------------------------------------- + TOTAL 19.18225128 2630.40426129 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 279100 +BPFP 0.2649 bits/point +EBPFP 0.2649 equivalent bits/point +MSE 2630.404261 +---------------------- -------------------------------------------------------- +Time: 5.189s Load: 0.070s, Pack+Encode: 2.605s, Decode+Unpack: 2.514s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2630.4043 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03393912-ILSVRC2012_val_00000047.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03393912-ILSVRC2012_val_00000047.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03394916-ILSVRC2012_val_00000957.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03394916-ILSVRC2012_val_00000957.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 56,480B, BPFP=0.1072 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 199,208B, BPFP=0.3781 +⌛️ [2/4] FRONTEND: Frontend time: 2.605s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.527s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10421823 12.78897614 + layer.39.0 9.72561820 2829.48736638 + ------------------------------------------------------------------------------------- + TOTAL 4.91491822 1421.13817126 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 255688 +BPFP 0.2427 bits/point +EBPFP 0.2427 equivalent bits/point +MSE 1421.138171 +---------------------- -------------------------------------------------------- +Time: 5.191s Load: 0.059s, Pack+Encode: 2.605s, Decode+Unpack: 2.527s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1421.1382 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03394916-ILSVRC2012_val_00000957.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03394916-ILSVRC2012_val_00000957.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03404251-ILSVRC2012_val_00000641.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03404251-ILSVRC2012_val_00000641.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 58,260B, BPFP=0.1106 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 265,088B, BPFP=0.5032 +⌛️ [2/4] FRONTEND: Frontend time: 2.596s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.495s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10764784 12.59634088 + layer.39.0 585.45553936 5634.88095238 + ------------------------------------------------------------------------------------- + TOTAL 292.78159360 2823.73864663 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 323348 +BPFP 0.3069 bits/point +EBPFP 0.3069 equivalent bits/point +MSE 2823.738647 +---------------------- -------------------------------------------------------- +Time: 5.142s Load: 0.052s, Pack+Encode: 2.596s, Decode+Unpack: 2.495s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2823.7386 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03404251-ILSVRC2012_val_00000641.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03404251-ILSVRC2012_val_00000641.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03417042-ILSVRC2012_val_00001144.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03417042-ILSVRC2012_val_00001144.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 62,732B, BPFP=0.1191 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 232,064B, BPFP=0.4405 +⌛️ [2/4] FRONTEND: Frontend time: 2.592s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.497s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10091509 12.29787719 + layer.39.0 202.93364310 4563.10641399 + ------------------------------------------------------------------------------------- + TOTAL 101.51727910 2287.70214559 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 294796 +BPFP 0.2798 bits/point +EBPFP 0.2798 equivalent bits/point +MSE 2287.702146 +---------------------- -------------------------------------------------------- +Time: 5.160s Load: 0.071s, Pack+Encode: 2.592s, Decode+Unpack: 2.497s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2287.7021 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03417042-ILSVRC2012_val_00001144.zst + to output-fixed/dinov3-total/lambda0.004/elic-featurecoding-8bit-layerwise/cls_in1kval/n03417042-ILSVRC2012_val_00001144.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 0.2746 bits/point +Avg EBPFP 0.2746 equivalent bits/point +Avg MSE 1798.697304 +Avg Time 5.158s +------------------------ ---------------------------- diff --git a/lambda0.004/hyperprior-featurecoding-8bit-individual/cls_in1ka/dtufc_hyperprior-featurecoding_dinov3-total_individual.log b/lambda0.004/hyperprior-featurecoding-8bit-individual/cls_in1ka/dtufc_hyperprior-featurecoding_dinov3-total_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..695401595e6136f7897cb1012dc7a3a7a49ac1f1 --- /dev/null +++ b/lambda0.004/hyperprior-featurecoding-8bit-individual/cls_in1ka/dtufc_hyperprior-featurecoding_dinov3-total_individual.log @@ -0,0 +1,9344 @@ +Experiment: dtufc_hyperprior-featurecoding_dinov3-total_individual +Log file: output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/dtufc_hyperprior-featurecoding_dinov3-total_individual.log +DTUFCCodecConfig: + arch: hyperprior-featurecoding + handler: dinov3-total + checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.004_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.004_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 559 +Loaded hyperprior-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.9' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.19' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.29' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.39' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json +Loaded per-key mappings: model=dinov3-total + Keys: ['layer.9', 'layer.19', 'layer.29', 'layer.39'] +---------------- ------------------------------------------------------------------------------------------------------------------------------ +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +Checkpoint codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.004_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-DINOv3/imagenet1k-a +Output output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka +---------------- ------------------------------------------------------------------------------------------------------------------------------ +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01498041-0.006639_koala _ American bullfrog_0.6658246.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01498041-0.006639_koala _ American bullfrog_0.6658246.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.091s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 115,280B, BPFP=0.2188 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 187,952B, BPFP=0.3567 +⌛️ [2/4] FRONTEND: Frontend time: 0.791s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.062s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09594801 13.02785528 + layer.39.0 58.94484178 3324.46428571 + ------------------------------------------------------------------------------------- + TOTAL 29.52039490 1668.74607050 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 303232 +BPFP 0.2878 bits/point +EBPFP 0.2878 equivalent bits/point +MSE 1668.746070 +---------------------- -------------------------------------------------------- +Time: 1.943s Load: 0.091s, Pack+Encode: 0.791s, Decode+Unpack: 1.062s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1668.7461 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01498041-0.006639_koala _ American bullfrog_0.6658246.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01498041-0.006639_koala _ American bullfrog_0.6658246.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01531178-0.000765_fox squirrel _ ant_0.9486805.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01531178-0.000765_fox squirrel _ ant_0.9486805.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 114,120B, BPFP=0.2166 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 203,776B, BPFP=0.3868 +⌛️ [2/4] FRONTEND: Frontend time: 0.582s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.027s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09773727 12.88363532 + layer.39.0 17.17825445 3923.00242954 + ------------------------------------------------------------------------------------- + TOTAL 8.63799586 1967.94303243 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 317896 +BPFP 0.3017 bits/point +EBPFP 0.3017 equivalent bits/point +MSE 1967.943032 +---------------------- -------------------------------------------------------- +Time: 1.680s Load: 0.070s, Pack+Encode: 0.582s, Decode+Unpack: 1.027s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1967.9430 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01531178-0.000765_fox squirrel _ ant_0.9486805.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01531178-0.000765_fox squirrel _ ant_0.9486805.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01534433-0.004573_stingray _ stingray_0.97124094.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01534433-0.004573_stingray _ stingray_0.97124094.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.079s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 100,476B, BPFP=0.1907 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 157,264B, BPFP=0.2985 +⌛️ [2/4] FRONTEND: Frontend time: 0.546s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.017s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09515371 13.21237530 + layer.39.0 6.87362484 2549.10617104 + ------------------------------------------------------------------------------------- + TOTAL 3.48438928 1281.15927317 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 257740 +BPFP 0.2446 bits/point +EBPFP 0.2446 equivalent bits/point +MSE 1281.159273 +---------------------- -------------------------------------------------------- +Time: 1.642s Load: 0.079s, Pack+Encode: 0.546s, Decode+Unpack: 1.017s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1281.1593 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01534433-0.004573_stingray _ stingray_0.97124094.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01534433-0.004573_stingray _ stingray_0.97124094.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01558993-0.000522_bow _ bow_0.9033333.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01558993-0.000522_bow _ bow_0.9033333.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 122,764B, BPFP=0.2330 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 155,456B, BPFP=0.2951 +⌛️ [2/4] FRONTEND: Frontend time: 0.580s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.018s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09874929 13.06738376 + layer.39.0 7.31778236 2435.97011662 + ------------------------------------------------------------------------------------- + TOTAL 3.70826583 1224.51875019 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 278220 +BPFP 0.2640 bits/point +EBPFP 0.2640 equivalent bits/point +MSE 1224.518750 +---------------------- -------------------------------------------------------- +Time: 1.667s Load: 0.069s, Pack+Encode: 0.580s, Decode+Unpack: 1.018s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1224.5188 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01558993-0.000522_bow _ bow_0.9033333.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01558993-0.000522_bow _ bow_0.9033333.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01580077-0.004583_African bush elephant _ African bush elephant_0.59939015.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01580077-0.004583_African bush elephant _ African bush elephant_0.59939015.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 121,436B, BPFP=0.2305 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 177,144B, BPFP=0.3362 +⌛️ [2/4] FRONTEND: Frontend time: 0.580s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.034s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10720986 12.85797517 + layer.39.0 24.46209533 2890.61758989 + ------------------------------------------------------------------------------------- + TOTAL 12.28465260 1451.73778253 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 298580 +BPFP 0.2834 bits/point +EBPFP 0.2834 equivalent bits/point +MSE 1451.737783 +---------------------- -------------------------------------------------------- +Time: 1.683s Load: 0.069s, Pack+Encode: 0.580s, Decode+Unpack: 1.034s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1451.7378 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01580077-0.004583_African bush elephant _ African bush elephant_0.59939015.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01580077-0.004583_African bush elephant _ African bush elephant_0.59939015.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01614925-0.049724_white-headed capuchin _ white-headed capuchin_0.9309422.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01614925-0.049724_white-headed capuchin _ white-headed capuchin_0.9309422.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 121,784B, BPFP=0.2312 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 155,288B, BPFP=0.2947 +⌛️ [2/4] FRONTEND: Frontend time: 0.583s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.044s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09739119 13.63920429 + layer.39.0 8.81423010 2949.25194363 + ------------------------------------------------------------------------------------- + TOTAL 4.45581065 1481.44557396 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 277072 +BPFP 0.2630 bits/point +EBPFP 0.2630 equivalent bits/point +MSE 1481.445574 +---------------------- -------------------------------------------------------- +Time: 1.697s Load: 0.070s, Pack+Encode: 0.583s, Decode+Unpack: 1.044s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1481.4456 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01614925-0.049724_white-headed capuchin _ white-headed capuchin_0.9309422.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01614925-0.049724_white-headed capuchin _ white-headed capuchin_0.9309422.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01631663-0.004606_American bullfrog _ American bullfrog_0.8789855.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01631663-0.004606_American bullfrog _ American bullfrog_0.8789855.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.079s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 112,344B, BPFP=0.2132 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 176,792B, BPFP=0.3356 +⌛️ [2/4] FRONTEND: Frontend time: 0.525s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.979s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09716670 13.13765545 + layer.39.0 20.45897868 2790.97424684 + ------------------------------------------------------------------------------------- + TOTAL 10.27807269 1402.05595115 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 289136 +BPFP 0.2744 bits/point +EBPFP 0.2744 equivalent bits/point +MSE 1402.055951 +---------------------- -------------------------------------------------------- +Time: 1.583s Load: 0.079s, Pack+Encode: 0.525s, Decode+Unpack: 0.979s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1402.0560 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01631663-0.004606_American bullfrog _ American bullfrog_0.8789855.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01631663-0.004606_American bullfrog _ American bullfrog_0.8789855.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01669191-0.029754_sandal _ sandal_0.38198605.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01669191-0.029754_sandal _ sandal_0.38198605.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 143,128B, BPFP=0.2717 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 161,544B, BPFP=0.3066 +⌛️ [2/4] FRONTEND: Frontend time: 0.558s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.030s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10877632 74.89133868 + layer.39.0 13.16500205 2480.64067055 + ------------------------------------------------------------------------------------- + TOTAL 6.63688918 1277.76600462 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 304672 +BPFP 0.2891 bits/point +EBPFP 0.2891 equivalent bits/point +MSE 1277.766005 +---------------------- -------------------------------------------------------- +Time: 1.639s Load: 0.051s, Pack+Encode: 0.558s, Decode+Unpack: 1.030s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1277.7660 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01669191-0.029754_sandal _ sandal_0.38198605.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01669191-0.029754_sandal _ sandal_0.38198605.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770081-0.000571_syringe _ syringe_0.7369336.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770081-0.000571_syringe _ syringe_0.7369336.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 107,564B, BPFP=0.2042 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 190,896B, BPFP=0.3623 +⌛️ [2/4] FRONTEND: Frontend time: 0.518s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.984s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09508557 13.02083713 + layer.39.0 60.03878538 3065.70383868 + ------------------------------------------------------------------------------------- + TOTAL 30.06693547 1539.36233790 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 298460 +BPFP 0.2833 bits/point +EBPFP 0.2833 equivalent bits/point +MSE 1539.362338 +---------------------- -------------------------------------------------------- +Time: 1.574s Load: 0.071s, Pack+Encode: 0.518s, Decode+Unpack: 0.984s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1539.3623 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770081-0.000571_syringe _ syringe_0.7369336.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01770081-0.000571_syringe _ syringe_0.7369336.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770393-0.000908_harvestman _ harvestman_0.8782497.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770393-0.000908_harvestman _ harvestman_0.8782497.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 119,480B, BPFP=0.2268 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 190,572B, BPFP=0.3617 +⌛️ [2/4] FRONTEND: Frontend time: 0.527s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.971s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11350316 12.93392971 + layer.39.0 19.73148992 3483.88824101 + ------------------------------------------------------------------------------------- + TOTAL 9.92249654 1748.41108536 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 310052 +BPFP 0.2943 bits/point +EBPFP 0.2943 equivalent bits/point +MSE 1748.411085 +---------------------- -------------------------------------------------------- +Time: 1.569s Load: 0.071s, Pack+Encode: 0.527s, Decode+Unpack: 0.971s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1748.4111 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770393-0.000908_harvestman _ harvestman_0.8782497.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01770393-0.000908_harvestman _ harvestman_0.8782497.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01784675-0.027853_syringe _ syringe_0.9584382.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01784675-0.027853_syringe _ syringe_0.9584382.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 134,204B, BPFP=0.2547 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 171,404B, BPFP=0.3253 +⌛️ [2/4] FRONTEND: Frontend time: 0.525s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.975s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11002613 37.42539025 + layer.39.0 26.08665877 2904.63386783 + ------------------------------------------------------------------------------------- + TOTAL 13.09834245 1471.02962904 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 305608 +BPFP 0.2900 bits/point +EBPFP 0.2900 equivalent bits/point +MSE 1471.029629 +---------------------- -------------------------------------------------------- +Time: 1.551s Load: 0.051s, Pack+Encode: 0.525s, Decode+Unpack: 0.975s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1471.0296 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01784675-0.027853_syringe _ syringe_0.9584382.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01784675-0.027853_syringe _ syringe_0.9584382.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01819313-0.053742_koala _ koala_0.98647016.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01819313-0.053742_koala _ koala_0.98647016.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 134,976B, BPFP=0.2562 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 196,620B, BPFP=0.3732 +⌛️ [2/4] FRONTEND: Frontend time: 0.552s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.992s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14565475 13.05204917 + layer.39.0 25.01023445 3232.14965986 + ------------------------------------------------------------------------------------- + TOTAL 12.57794460 1622.60085452 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 331596 +BPFP 0.3147 bits/point +EBPFP 0.3147 equivalent bits/point +MSE 1622.600855 +---------------------- -------------------------------------------------------- +Time: 1.596s Load: 0.052s, Pack+Encode: 0.552s, Decode+Unpack: 0.992s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1622.6009 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01819313-0.053742_koala _ koala_0.98647016.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01819313-0.053742_koala _ koala_0.98647016.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01820546-0.012522_toucan _ toucan_0.63882655.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01820546-0.012522_toucan _ toucan_0.63882655.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 113,920B, BPFP=0.2162 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 176,104B, BPFP=0.3343 +⌛️ [2/4] FRONTEND: Frontend time: 0.511s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.973s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09696376 12.89744385 + layer.39.0 16.65489097 3154.77283771 + ------------------------------------------------------------------------------------- + TOTAL 8.37592737 1583.83514078 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 290024 +BPFP 0.2752 bits/point +EBPFP 0.2752 equivalent bits/point +MSE 1583.835141 +---------------------- -------------------------------------------------------- +Time: 1.546s Load: 0.061s, Pack+Encode: 0.511s, Decode+Unpack: 0.973s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1583.8351 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01820546-0.012522_toucan _ toucan_0.63882655.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01820546-0.012522_toucan _ toucan_0.63882655.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01833805-0.013248_toucan _ lorikeet_0.77773976.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01833805-0.013248_toucan _ lorikeet_0.77773976.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 119,336B, BPFP=0.2265 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 185,516B, BPFP=0.3521 +⌛️ [2/4] FRONTEND: Frontend time: 0.516s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.975s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09866240 12.92841199 + layer.39.0 7.67772963 3255.49392614 + ------------------------------------------------------------------------------------- + TOTAL 3.88819601 1634.21116907 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 304852 +BPFP 0.2893 bits/point +EBPFP 0.2893 equivalent bits/point +MSE 1634.211169 +---------------------- -------------------------------------------------------- +Time: 1.560s Load: 0.069s, Pack+Encode: 0.516s, Decode+Unpack: 0.975s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1634.2112 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01833805-0.013248_toucan _ lorikeet_0.77773976.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01833805-0.013248_toucan _ lorikeet_0.77773976.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01843383-0.013605_white-headed capuchin _ white-headed capuchin_0.9984768.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01843383-0.013605_white-headed capuchin _ white-headed capuchin_0.9984768.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 125,004B, BPFP=0.2373 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 173,660B, BPFP=0.3296 +⌛️ [2/4] FRONTEND: Frontend time: 0.579s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.028s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11910487 12.98397356 + layer.39.0 9.20068692 3140.30806608 + ------------------------------------------------------------------------------------- + TOTAL 4.65989589 1576.64601982 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 298664 +BPFP 0.2834 bits/point +EBPFP 0.2834 equivalent bits/point +MSE 1576.646020 +---------------------- -------------------------------------------------------- +Time: 1.657s Load: 0.050s, Pack+Encode: 0.579s, Decode+Unpack: 1.028s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1576.6460 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01843383-0.013605_white-headed capuchin _ white-headed capuchin_0.9984768.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01843383-0.013605_white-headed capuchin _ white-headed capuchin_0.9984768.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01924916-0.000644_jay _ jay_0.82223135.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01924916-0.000644_jay _ jay_0.82223135.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 133,032B, BPFP=0.2525 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 159,780B, BPFP=0.3033 +⌛️ [2/4] FRONTEND: Frontend time: 0.534s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.979s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11488669 12.82853499 + layer.39.0 141.08750911 2631.92978620 + ------------------------------------------------------------------------------------- + TOTAL 70.60119790 1322.37916059 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 292812 +BPFP 0.2779 bits/point +EBPFP 0.2779 equivalent bits/point +MSE 1322.379161 +---------------------- -------------------------------------------------------- +Time: 1.563s Load: 0.051s, Pack+Encode: 0.534s, Decode+Unpack: 0.979s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1322.3792 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01924916-0.000644_jay _ jay_0.82223135.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01924916-0.000644_jay _ jay_0.82223135.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01944390-0.002567_American robin _ American robin_0.5629079.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01944390-0.002567_American robin _ American robin_0.5629079.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 119,916B, BPFP=0.2276 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 172,976B, BPFP=0.3283 +⌛️ [2/4] FRONTEND: Frontend time: 0.573s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.041s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10732387 37.22762163 + layer.39.0 16.74672581 3387.92565598 + ------------------------------------------------------------------------------------- + TOTAL 8.42702484 1712.57663880 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 292892 +BPFP 0.2780 bits/point +EBPFP 0.2780 equivalent bits/point +MSE 1712.576639 +---------------------- -------------------------------------------------------- +Time: 1.666s Load: 0.051s, Pack+Encode: 0.573s, Decode+Unpack: 1.041s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1712.5766 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01944390-0.002567_American robin _ American robin_0.5629079.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01944390-0.002567_American robin _ American robin_0.5629079.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01985128-0.001579_centipede _ centipede_0.85936093.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01985128-0.001579_centipede _ centipede_0.85936093.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 109,920B, BPFP=0.2086 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 168,064B, BPFP=0.3190 +⌛️ [2/4] FRONTEND: Frontend time: 0.587s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.011s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09645609 13.13732708 + layer.39.0 23.47999613 2740.99076774 + ------------------------------------------------------------------------------------- + TOTAL 11.78822611 1377.06404741 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 277984 +BPFP 0.2638 bits/point +EBPFP 0.2638 equivalent bits/point +MSE 1377.064047 +---------------------- -------------------------------------------------------- +Time: 1.668s Load: 0.070s, Pack+Encode: 0.587s, Decode+Unpack: 1.011s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1377.0640 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01985128-0.001579_centipede _ centipede_0.85936093.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01985128-0.001579_centipede _ centipede_0.85936093.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02037110-0.003503_snowmobile _ snowmobile_0.5200631.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02037110-0.003503_snowmobile _ snowmobile_0.5200631.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 94,300B, BPFP=0.1790 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 161,512B, BPFP=0.3066 +⌛️ [2/4] FRONTEND: Frontend time: 0.517s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.977s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09471867 13.22757608 + layer.39.0 17.04498261 2628.45724004 + ------------------------------------------------------------------------------------- + TOTAL 8.56985064 1320.84240806 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 255812 +BPFP 0.2428 bits/point +EBPFP 0.2428 equivalent bits/point +MSE 1320.842408 +---------------------- -------------------------------------------------------- +Time: 1.565s Load: 0.070s, Pack+Encode: 0.517s, Decode+Unpack: 0.977s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1320.8424 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02037110-0.003503_snowmobile _ snowmobile_0.5200631.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02037110-0.003503_snowmobile _ snowmobile_0.5200631.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02123394-0.015363_marmot _ marmot_0.82052565.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02123394-0.015363_marmot _ marmot_0.82052565.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 114,844B, BPFP=0.2180 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 201,000B, BPFP=0.3815 +⌛️ [2/4] FRONTEND: Frontend time: 0.544s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.042s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10209646 12.95769463 + layer.39.0 11.38238543 3287.25413022 + ------------------------------------------------------------------------------------- + TOTAL 5.74224095 1650.10591243 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 315844 +BPFP 0.2997 bits/point +EBPFP 0.2997 equivalent bits/point +MSE 1650.105912 +---------------------- -------------------------------------------------------- +Time: 1.637s Load: 0.051s, Pack+Encode: 0.544s, Decode+Unpack: 1.042s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1650.1059 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02123394-0.015363_marmot _ marmot_0.82052565.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02123394-0.015363_marmot _ marmot_0.82052565.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02165456-0.000157_corn _ corn_0.9868978.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02165456-0.000157_corn _ corn_0.9868978.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 122,236B, BPFP=0.2320 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 203,000B, BPFP=0.3853 +⌛️ [2/4] FRONTEND: Frontend time: 0.531s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.002s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10346756 12.92003101 + layer.39.0 776.17699223 4229.19047619 + ------------------------------------------------------------------------------------- + TOTAL 388.14022989 2121.05525360 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 325236 +BPFP 0.3087 bits/point +EBPFP 0.3087 equivalent bits/point +MSE 2121.055254 +---------------------- -------------------------------------------------------- +Time: 1.584s Load: 0.051s, Pack+Encode: 0.531s, Decode+Unpack: 1.002s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2121.0553 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02165456-0.000157_corn _ corn_0.9868978.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02165456-0.000157_corn _ corn_0.9868978.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02219486-0.000060_cliff _ cliff_0.99684334.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02219486-0.000060_cliff _ cliff_0.99684334.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 108,104B, BPFP=0.2052 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 151,516B, BPFP=0.2876 +⌛️ [2/4] FRONTEND: Frontend time: 0.570s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.009s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09584527 12.82440856 + layer.39.0 31.94620460 2547.14358601 + ------------------------------------------------------------------------------------- + TOTAL 16.02102494 1279.98399728 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 259620 +BPFP 0.2464 bits/point +EBPFP 0.2464 equivalent bits/point +MSE 1279.983997 +---------------------- -------------------------------------------------------- +Time: 1.631s Load: 0.052s, Pack+Encode: 0.570s, Decode+Unpack: 1.009s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1279.9840 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02219486-0.000060_cliff _ cliff_0.99684334.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02219486-0.000060_cliff _ cliff_0.99684334.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02226429-0.000085_stick insect _ stick insect_0.99900275.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02226429-0.000085_stick insect _ stick insect_0.99900275.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 111,604B, BPFP=0.2118 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 191,800B, BPFP=0.3641 +⌛️ [2/4] FRONTEND: Frontend time: 0.589s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.019s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09547379 13.00793872 + layer.39.0 19.16722850 3180.48202138 + ------------------------------------------------------------------------------------- + TOTAL 9.63135114 1596.74498005 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 303404 +BPFP 0.2879 bits/point +EBPFP 0.2879 equivalent bits/point +MSE 1596.744980 +---------------------- -------------------------------------------------------- +Time: 1.678s Load: 0.070s, Pack+Encode: 0.589s, Decode+Unpack: 1.019s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1596.7450 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02226429-0.000085_stick insect _ stick insect_0.99900275.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02226429-0.000085_stick insect _ stick insect_0.99900275.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02231487-0.000080_manhole cover _ manhole cover_0.7554716.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02231487-0.000080_manhole cover _ manhole cover_0.7554716.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 112,516B, BPFP=0.2136 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 191,628B, BPFP=0.3637 +⌛️ [2/4] FRONTEND: Frontend time: 0.582s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.047s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09512618 13.11533118 + layer.39.0 210.79875790 3218.41933916 + ------------------------------------------------------------------------------------- + TOTAL 105.44694204 1615.76733517 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 304144 +BPFP 0.2886 bits/point +EBPFP 0.2886 equivalent bits/point +MSE 1615.767335 +---------------------- -------------------------------------------------------- +Time: 1.699s Load: 0.070s, Pack+Encode: 0.582s, Decode+Unpack: 1.047s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1615.7673 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02231487-0.000080_manhole cover _ manhole cover_0.7554716.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02231487-0.000080_manhole cover _ manhole cover_0.7554716.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02233338-0.002901_manhole cover _ manhole cover_0.69458175.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02233338-0.002901_manhole cover _ manhole cover_0.69458175.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 111,828B, BPFP=0.2123 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 187,828B, BPFP=0.3565 +⌛️ [2/4] FRONTEND: Frontend time: 0.508s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.978s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09539769 13.06729455 + layer.39.0 58.97704841 2547.41545190 + ------------------------------------------------------------------------------------- + TOTAL 29.53622305 1280.24137322 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 299656 +BPFP 0.2844 bits/point +EBPFP 0.2844 equivalent bits/point +MSE 1280.241373 +---------------------- -------------------------------------------------------- +Time: 1.555s Load: 0.069s, Pack+Encode: 0.508s, Decode+Unpack: 0.978s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1280.2414 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02233338-0.002901_manhole cover _ manhole cover_0.69458175.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02233338-0.002901_manhole cover _ manhole cover_0.69458175.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02236044-0.000522_sundial _ sundial_0.96381366.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02236044-0.000522_sundial _ sundial_0.96381366.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 120,656B, BPFP=0.2290 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 191,964B, BPFP=0.3644 +⌛️ [2/4] FRONTEND: Frontend time: 0.573s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.013s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09795647 12.93092125 + layer.39.0 53.12385356 3717.96793003 + ------------------------------------------------------------------------------------- + TOTAL 26.61090502 1865.44942564 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 312620 +BPFP 0.2967 bits/point +EBPFP 0.2967 equivalent bits/point +MSE 1865.449426 +---------------------- -------------------------------------------------------- +Time: 1.636s Load: 0.050s, Pack+Encode: 0.573s, Decode+Unpack: 1.013s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1865.4494 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02236044-0.000522_sundial _ sundial_0.96381366.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02236044-0.000522_sundial _ sundial_0.96381366.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02259212-0.000032_chain _ chain_0.6590295.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02259212-0.000032_chain _ chain_0.6590295.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.054s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 112,764B, BPFP=0.2140 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 193,396B, BPFP=0.3671 +⌛️ [2/4] FRONTEND: Frontend time: 0.537s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.984s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09523673 12.96301590 + layer.39.0 80.66082058 3639.01141885 + ------------------------------------------------------------------------------------- + TOTAL 40.37802865 1825.98721738 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 306160 +BPFP 0.2906 bits/point +EBPFP 0.2906 equivalent bits/point +MSE 1825.987217 +---------------------- -------------------------------------------------------- +Time: 1.575s Load: 0.054s, Pack+Encode: 0.537s, Decode+Unpack: 0.984s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1825.9872 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02259212-0.000032_chain _ chain_0.6590295.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02259212-0.000032_chain _ chain_0.6590295.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02279972-0.000576_apron _ apron_0.7661352.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02279972-0.000576_apron _ apron_0.7661352.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 135,748B, BPFP=0.2577 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 204,608B, BPFP=0.3884 +⌛️ [2/4] FRONTEND: Frontend time: 0.579s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.020s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12772729 73.73458759 + layer.39.0 1038.59135083 3425.73736638 + ------------------------------------------------------------------------------------- + TOTAL 519.35953906 1749.73597698 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 340356 +BPFP 0.3230 bits/point +EBPFP 0.3230 equivalent bits/point +MSE 1749.735977 +---------------------- -------------------------------------------------------- +Time: 1.670s Load: 0.070s, Pack+Encode: 0.579s, Decode+Unpack: 1.020s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1749.7360 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02279972-0.000576_apron _ apron_0.7661352.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02279972-0.000576_apron _ apron_0.7661352.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02280649-0.001599_custard apple _ leafhopper_0.9128452.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02280649-0.001599_custard apple _ leafhopper_0.9128452.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 107,680B, BPFP=0.2044 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 199,196B, BPFP=0.3781 +⌛️ [2/4] FRONTEND: Frontend time: 0.585s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.029s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09488542 13.13813092 + layer.39.0 1031.59973275 3978.66690962 + ------------------------------------------------------------------------------------- + TOTAL 515.84730909 1995.90252027 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 306876 +BPFP 0.2912 bits/point +EBPFP 0.2912 equivalent bits/point +MSE 1995.902520 +---------------------- -------------------------------------------------------- +Time: 1.684s Load: 0.070s, Pack+Encode: 0.585s, Decode+Unpack: 1.029s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1995.9025 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02280649-0.001599_custard apple _ leafhopper_0.9128452.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02280649-0.001599_custard apple _ leafhopper_0.9128452.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02317335-0.033681_flatworm _ flatworm_0.6070924.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02317335-0.033681_flatworm _ flatworm_0.6070924.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 103,524B, BPFP=0.1965 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 198,804B, BPFP=0.3773 +⌛️ [2/4] FRONTEND: Frontend time: 0.522s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.002s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09575805 13.17805610 + layer.39.0 62.35741238 3018.71525753 + ------------------------------------------------------------------------------------- + TOTAL 31.22658522 1515.94665682 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 302328 +BPFP 0.2869 bits/point +EBPFP 0.2869 equivalent bits/point +MSE 1515.946657 +---------------------- -------------------------------------------------------- +Time: 1.574s Load: 0.050s, Pack+Encode: 0.522s, Decode+Unpack: 1.002s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1515.9467 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02317335-0.033681_flatworm _ flatworm_0.6070924.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02317335-0.033681_flatworm _ flatworm_0.6070924.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02325366-0.000350_flatworm _ flatworm_0.87634724.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02325366-0.000350_flatworm _ flatworm_0.87634724.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 96,788B, BPFP=0.1837 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 173,920B, BPFP=0.3301 +⌛️ [2/4] FRONTEND: Frontend time: 0.532s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.012s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09712043 13.25864860 + layer.39.0 30.59439155 2639.56778426 + ------------------------------------------------------------------------------------- + TOTAL 15.34575599 1326.41321643 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 270708 +BPFP 0.2569 bits/point +EBPFP 0.2569 equivalent bits/point +MSE 1326.413216 +---------------------- -------------------------------------------------------- +Time: 1.595s Load: 0.051s, Pack+Encode: 0.532s, Decode+Unpack: 1.012s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1326.4132 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02325366-0.000350_flatworm _ flatworm_0.87634724.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02325366-0.000350_flatworm _ flatworm_0.87634724.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02346627-0.011107_fountain _ skunk_0.28641737.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02346627-0.011107_fountain _ skunk_0.28641737.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 96,332B, BPFP=0.1828 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 161,164B, BPFP=0.3059 +⌛️ [2/4] FRONTEND: Frontend time: 0.564s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.998s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09705289 13.13679562 + layer.39.0 9.52721088 2587.10349854 + ------------------------------------------------------------------------------------- + TOTAL 4.81213189 1300.12014708 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 257496 +BPFP 0.2444 bits/point +EBPFP 0.2444 equivalent bits/point +MSE 1300.120147 +---------------------- -------------------------------------------------------- +Time: 1.613s Load: 0.051s, Pack+Encode: 0.564s, Decode+Unpack: 0.998s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1300.1201 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02346627-0.011107_fountain _ skunk_0.28641737.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02346627-0.011107_fountain _ skunk_0.28641737.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02445715-0.036432_shovel _ tarantula_0.26785344.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02445715-0.036432_shovel _ tarantula_0.26785344.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 115,728B, BPFP=0.2197 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 153,596B, BPFP=0.2915 +⌛️ [2/4] FRONTEND: Frontend time: 0.565s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.000s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09708806 12.94093363 + layer.39.0 8.00606437 2556.82774538 + ------------------------------------------------------------------------------------- + TOTAL 4.05157622 1284.88433951 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 269324 +BPFP 0.2556 bits/point +EBPFP 0.2556 equivalent bits/point +MSE 1284.884340 +---------------------- -------------------------------------------------------- +Time: 1.615s Load: 0.050s, Pack+Encode: 0.565s, Decode+Unpack: 1.000s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1284.8843 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02445715-0.036432_shovel _ tarantula_0.26785344.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02445715-0.036432_shovel _ tarantula_0.26785344.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02454379-0.082010_koala _ koala_0.7052893.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02454379-0.082010_koala _ koala_0.7052893.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 151,180B, BPFP=0.2870 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 160,520B, BPFP=0.3047 +⌛️ [2/4] FRONTEND: Frontend time: 0.558s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.998s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14585212 26.27146350 + layer.39.0 44.19989826 2744.25218659 + ------------------------------------------------------------------------------------- + TOTAL 22.17287519 1385.26182504 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 311700 +BPFP 0.2958 bits/point +EBPFP 0.2958 equivalent bits/point +MSE 1385.261825 +---------------------- -------------------------------------------------------- +Time: 1.605s Load: 0.050s, Pack+Encode: 0.558s, Decode+Unpack: 0.998s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1385.2618 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02454379-0.082010_koala _ koala_0.7052893.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02454379-0.082010_koala _ koala_0.7052893.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02782093-0.007542_American alligator _ American alligator_0.49872985.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02782093-0.007542_American alligator _ American alligator_0.49872985.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 106,568B, BPFP=0.2023 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 184,296B, BPFP=0.3498 +⌛️ [2/4] FRONTEND: Frontend time: 0.553s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.992s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09848133 13.20674749 + layer.39.0 9.18780844 3100.84985423 + ------------------------------------------------------------------------------------- + TOTAL 4.64314488 1557.02830086 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 290864 +BPFP 0.2760 bits/point +EBPFP 0.2760 equivalent bits/point +MSE 1557.028301 +---------------------- -------------------------------------------------------- +Time: 1.616s Load: 0.071s, Pack+Encode: 0.553s, Decode+Unpack: 0.992s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1557.0283 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02782093-0.007542_American alligator _ American alligator_0.49872985.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02782093-0.007542_American alligator _ American alligator_0.49872985.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02787622-0.004599_marimba _ accordion_0.25991488.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02787622-0.004599_marimba _ accordion_0.25991488.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 127,192B, BPFP=0.2414 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 213,080B, BPFP=0.4044 +⌛️ [2/4] FRONTEND: Frontend time: 0.559s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.004s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12856446 37.46069075 + layer.39.0 1004.59450923 4558.07677357 + ------------------------------------------------------------------------------------- + TOTAL 502.36153685 2297.76873216 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 340272 +BPFP 0.3229 bits/point +EBPFP 0.3229 equivalent bits/point +MSE 2297.768732 +---------------------- -------------------------------------------------------- +Time: 1.613s Load: 0.051s, Pack+Encode: 0.559s, Decode+Unpack: 1.004s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2297.7687 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02787622-0.004599_marimba _ accordion_0.25991488.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02787622-0.004599_marimba _ accordion_0.25991488.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02793495-0.000130_flatworm _ stingray_0.5528234.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02793495-0.000130_flatworm _ stingray_0.5528234.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 97,472B, BPFP=0.1850 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 157,560B, BPFP=0.2991 +⌛️ [2/4] FRONTEND: Frontend time: 0.594s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.021s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09706621 13.25670782 + layer.39.0 8.05872662 2434.36637512 + ------------------------------------------------------------------------------------- + TOTAL 4.07789641 1223.81154147 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 255032 +BPFP 0.2420 bits/point +EBPFP 0.2420 equivalent bits/point +MSE 1223.811541 +---------------------- -------------------------------------------------------- +Time: 1.685s Load: 0.070s, Pack+Encode: 0.594s, Decode+Unpack: 1.021s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1223.8115 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02793495-0.000130_flatworm _ stingray_0.5528234.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02793495-0.000130_flatworm _ stingray_0.5528234.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02797295-0.002775_hair dryer _ box turtle_0.2407761.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02797295-0.002775_hair dryer _ box turtle_0.2407761.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 122,528B, BPFP=0.2326 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 261,644B, BPFP=0.4966 +⌛️ [2/4] FRONTEND: Frontend time: 0.505s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.998s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11161610 12.93664491 + layer.39.0 373.09438776 4213.50388727 + ------------------------------------------------------------------------------------- + TOTAL 186.60300193 2113.22026609 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 384172 +BPFP 0.3646 bits/point +EBPFP 0.3646 equivalent bits/point +MSE 2113.220266 +---------------------- -------------------------------------------------------- +Time: 1.554s Load: 0.051s, Pack+Encode: 0.505s, Decode+Unpack: 0.998s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2113.2203 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02797295-0.002775_hair dryer _ box turtle_0.2407761.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02797295-0.002775_hair dryer _ box turtle_0.2407761.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02814860-0.006340_fountain _ fountain_0.7891514.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02814860-0.006340_fountain _ fountain_0.7891514.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 97,756B, BPFP=0.1855 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 168,092B, BPFP=0.3191 +⌛️ [2/4] FRONTEND: Frontend time: 0.585s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.007s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.04615183 13.53923238 + layer.39.0 7.48662090 2962.88119534 + ------------------------------------------------------------------------------------- + TOTAL 7.76638637 1488.21021386 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 265848 +BPFP 0.2523 bits/point +EBPFP 0.2523 equivalent bits/point +MSE 1488.210214 +---------------------- -------------------------------------------------------- +Time: 1.661s Load: 0.069s, Pack+Encode: 0.585s, Decode+Unpack: 1.007s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1488.2102 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02814860-0.006340_fountain _ fountain_0.7891514.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02814860-0.006340_fountain _ fountain_0.7891514.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02879718-0.003578_maraca _ maraca_0.6809677.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02879718-0.003578_maraca _ maraca_0.6809677.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.078s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 116,528B, BPFP=0.2212 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 323,844B, BPFP=0.6147 +⌛️ [2/4] FRONTEND: Frontend time: 0.577s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.005s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10989876 13.14687728 + layer.39.0 33.03751367 4296.15451895 + ------------------------------------------------------------------------------------- + TOTAL 16.57370621 2154.65069811 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 440372 +BPFP 0.4179 bits/point +EBPFP 0.4179 equivalent bits/point +MSE 2154.650698 +---------------------- -------------------------------------------------------- +Time: 1.659s Load: 0.078s, Pack+Encode: 0.577s, Decode+Unpack: 1.005s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2154.6507 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02879718-0.003578_maraca _ maraca_0.6809677.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02879718-0.003578_maraca _ maraca_0.6809677.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02883205-0.000262_syringe _ syringe_0.7098205.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02883205-0.000262_syringe _ syringe_0.7098205.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 114,884B, BPFP=0.2181 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 198,736B, BPFP=0.3772 +⌛️ [2/4] FRONTEND: Frontend time: 0.520s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.977s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09610580 13.03728685 + layer.39.0 8.14318931 3096.28449951 + ------------------------------------------------------------------------------------- + TOTAL 4.11964755 1554.66089318 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 313620 +BPFP 0.2976 bits/point +EBPFP 0.2976 equivalent bits/point +MSE 1554.660893 +---------------------- -------------------------------------------------------- +Time: 1.567s Load: 0.069s, Pack+Encode: 0.520s, Decode+Unpack: 0.977s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1554.6609 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02883205-0.000262_syringe _ syringe_0.7098205.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02883205-0.000262_syringe _ syringe_0.7098205.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02906734-0.000163_stethoscope _ stethoscope_0.9290994.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02906734-0.000163_stethoscope _ stethoscope_0.9290994.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 129,220B, BPFP=0.2453 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 224,328B, BPFP=0.4258 +⌛️ [2/4] FRONTEND: Frontend time: 0.570s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.995s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12024398 24.82497229 + layer.39.0 47.23105336 3734.07871720 + ------------------------------------------------------------------------------------- + TOTAL 23.67564867 1879.45184474 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 353548 +BPFP 0.3355 bits/point +EBPFP 0.3355 equivalent bits/point +MSE 1879.451845 +---------------------- -------------------------------------------------------- +Time: 1.617s Load: 0.052s, Pack+Encode: 0.570s, Decode+Unpack: 0.995s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1879.4518 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02906734-0.000163_stethoscope _ stethoscope_0.9290994.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02906734-0.000163_stethoscope _ stethoscope_0.9290994.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02948072-0.002303_digital clock _ digital clock_0.928374.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02948072-0.002303_digital clock _ digital clock_0.928374.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 112,680B, BPFP=0.2139 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 220,164B, BPFP=0.4179 +⌛️ [2/4] FRONTEND: Frontend time: 0.579s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.003s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09670976 13.27919153 + layer.39.0 81.62974520 3195.01409135 + ------------------------------------------------------------------------------------- + TOTAL 40.86322748 1604.14664144 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 332844 +BPFP 0.3159 bits/point +EBPFP 0.3159 equivalent bits/point +MSE 1604.146641 +---------------------- -------------------------------------------------------- +Time: 1.653s Load: 0.071s, Pack+Encode: 0.579s, Decode+Unpack: 1.003s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1604.1466 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02948072-0.002303_digital clock _ digital clock_0.928374.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02948072-0.002303_digital clock _ digital clock_0.928374.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02999410-0.000148_chest _ chest_0.9948565.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02999410-0.000148_chest _ chest_0.9948565.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.057s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 110,980B, BPFP=0.2106 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 171,844B, BPFP=0.3262 +⌛️ [2/4] FRONTEND: Frontend time: 0.582s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.040s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10256943 13.05812872 + layer.39.0 13.72598738 2567.67055394 + ------------------------------------------------------------------------------------- + TOTAL 6.91427841 1290.36434133 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 282824 +BPFP 0.2684 bits/point +EBPFP 0.2684 equivalent bits/point +MSE 1290.364341 +---------------------- -------------------------------------------------------- +Time: 1.679s Load: 0.057s, Pack+Encode: 0.582s, Decode+Unpack: 1.040s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1290.3643 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02999410-0.000148_chest _ chest_0.9948565.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02999410-0.000148_chest _ chest_0.9948565.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03026506-0.001828_basketball _ basketball_0.6904969.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03026506-0.001828_basketball _ basketball_0.6904969.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 105,568B, BPFP=0.2004 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 202,600B, BPFP=0.3846 +⌛️ [2/4] FRONTEND: Frontend time: 0.517s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.969s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09484169 13.04740551 + layer.39.0 87.31533194 3426.48153547 + ------------------------------------------------------------------------------------- + TOTAL 43.70508681 1719.76447049 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 308168 +BPFP 0.2925 bits/point +EBPFP 0.2925 equivalent bits/point +MSE 1719.764470 +---------------------- -------------------------------------------------------- +Time: 1.555s Load: 0.069s, Pack+Encode: 0.517s, Decode+Unpack: 0.969s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1719.7645 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03026506-0.001828_basketball _ basketball_0.6904969.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03026506-0.001828_basketball _ basketball_0.6904969.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03124043-0.001154_bow tie _ bow tie_0.9622156.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03124043-0.001154_bow tie _ bow tie_0.9622156.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 106,064B, BPFP=0.2013 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 204,208B, BPFP=0.3876 +⌛️ [2/4] FRONTEND: Frontend time: 0.560s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.976s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09893820 13.08167821 + layer.39.0 13.24554141 3539.77672498 + ------------------------------------------------------------------------------------- + TOTAL 6.67223981 1776.42920159 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 310272 +BPFP 0.2945 bits/point +EBPFP 0.2945 equivalent bits/point +MSE 1776.429202 +---------------------- -------------------------------------------------------- +Time: 1.596s Load: 0.060s, Pack+Encode: 0.560s, Decode+Unpack: 0.976s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1776.4292 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03124043-0.001154_bow tie _ bow tie_0.9622156.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03124043-0.001154_bow tie _ bow tie_0.9622156.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03196217-0.015958_soap dispenser _ envelope_0.80274254.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03196217-0.015958_soap dispenser _ envelope_0.80274254.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 109,900B, BPFP=0.2086 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 202,240B, BPFP=0.3839 +⌛️ [2/4] FRONTEND: Frontend time: 0.533s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.006s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10340443 13.20207726 + layer.39.0 8.70910111 3329.86248785 + ------------------------------------------------------------------------------------- + TOTAL 4.40625277 1671.53228256 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 312140 +BPFP 0.2962 bits/point +EBPFP 0.2962 equivalent bits/point +MSE 1671.532283 +---------------------- -------------------------------------------------------- +Time: 1.590s Load: 0.051s, Pack+Encode: 0.533s, Decode+Unpack: 1.006s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1671.5323 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03196217-0.015958_soap dispenser _ envelope_0.80274254.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03196217-0.015958_soap dispenser _ envelope_0.80274254.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03223299-0.001528_hot dog _ hot dog_0.9147216.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03223299-0.001528_hot dog _ hot dog_0.9147216.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 121,596B, BPFP=0.2308 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 180,036B, BPFP=0.3417 +⌛️ [2/4] FRONTEND: Frontend time: 0.513s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.978s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10130972 12.84252004 + layer.39.0 352.09596696 3311.48712342 + ------------------------------------------------------------------------------------- + TOTAL 176.09863834 1662.16482173 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 301632 +BPFP 0.2863 bits/point +EBPFP 0.2863 equivalent bits/point +MSE 1662.164822 +---------------------- -------------------------------------------------------- +Time: 1.561s Load: 0.071s, Pack+Encode: 0.513s, Decode+Unpack: 0.978s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1662.1648 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03223299-0.001528_hot dog _ hot dog_0.9147216.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03223299-0.001528_hot dog _ hot dog_0.9147216.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03255030-0.005469_bubble _ bubble_0.9381716.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03255030-0.005469_bubble _ bubble_0.9381716.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 113,652B, BPFP=0.2157 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 249,976B, BPFP=0.4745 +⌛️ [2/4] FRONTEND: Frontend time: 0.603s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.057s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09675161 13.10615680 + layer.39.0 42.23478499 3677.94582119 + ------------------------------------------------------------------------------------- + TOTAL 21.16576830 1845.52598899 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 363628 +BPFP 0.3451 bits/point +EBPFP 0.3451 equivalent bits/point +MSE 1845.525989 +---------------------- -------------------------------------------------------- +Time: 1.729s Load: 0.069s, Pack+Encode: 0.603s, Decode+Unpack: 1.057s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1845.5260 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03255030-0.005469_bubble _ bubble_0.9381716.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03255030-0.005469_bubble _ bubble_0.9381716.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03325584-0.000773_candle _ candle_0.810919.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03325584-0.000773_candle _ candle_0.810919.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 118,796B, BPFP=0.2255 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 231,372B, BPFP=0.4392 +⌛️ [2/4] FRONTEND: Frontend time: 0.578s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.007s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10394677 61.74069181 + layer.39.0 140.58187561 4403.74538387 + ------------------------------------------------------------------------------------- + TOTAL 70.34291119 2232.74303784 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 350168 +BPFP 0.3323 bits/point +EBPFP 0.3323 equivalent bits/point +MSE 2232.743038 +---------------------- -------------------------------------------------------- +Time: 1.656s Load: 0.070s, Pack+Encode: 0.578s, Decode+Unpack: 1.007s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2232.7430 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03325584-0.000773_candle _ candle_0.810919.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03325584-0.000773_candle _ candle_0.810919.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03355925-0.004997_spider web _ spider web_0.9142101.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03355925-0.004997_spider web _ spider web_0.9142101.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 91,096B, BPFP=0.1729 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 144,236B, BPFP=0.2738 +⌛️ [2/4] FRONTEND: Frontend time: 0.552s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.982s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09873271 13.46807713 + layer.39.0 6.60211199 2596.30903790 + ------------------------------------------------------------------------------------- + TOTAL 3.35042235 1304.88855752 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 235332 +BPFP 0.2233 bits/point +EBPFP 0.2233 equivalent bits/point +MSE 1304.888558 +---------------------- -------------------------------------------------------- +Time: 1.584s Load: 0.050s, Pack+Encode: 0.552s, Decode+Unpack: 0.982s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1304.8886 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03355925-0.004997_spider web _ spider web_0.9142101.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03355925-0.004997_spider web _ spider web_0.9142101.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03384352-0.002273_viaduct _ viaduct_0.6161024.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03384352-0.002273_viaduct _ viaduct_0.6161024.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 109,856B, BPFP=0.2085 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 183,408B, BPFP=0.3481 +⌛️ [2/4] FRONTEND: Frontend time: 0.576s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.998s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09647940 13.04783353 + layer.39.0 175.50411504 3185.79591837 + ------------------------------------------------------------------------------------- + TOTAL 87.80029722 1599.42187595 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 293264 +BPFP 0.2783 bits/point +EBPFP 0.2783 equivalent bits/point +MSE 1599.421876 +---------------------- -------------------------------------------------------- +Time: 1.626s Load: 0.051s, Pack+Encode: 0.576s, Decode+Unpack: 0.998s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1599.4219 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03384352-0.002273_viaduct _ viaduct_0.6161024.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03384352-0.002273_viaduct _ viaduct_0.6161024.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03388043-0.005154_candle _ candle_0.9636924.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03388043-0.005154_candle _ candle_0.9636924.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 112,336B, BPFP=0.2132 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 195,976B, BPFP=0.3720 +⌛️ [2/4] FRONTEND: Frontend time: 0.571s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.982s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09640297 13.07775677 + layer.39.0 7.87377147 2825.41277940 + ------------------------------------------------------------------------------------- + TOTAL 3.98508722 1419.24526808 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 308312 +BPFP 0.2926 bits/point +EBPFP 0.2926 equivalent bits/point +MSE 1419.245268 +---------------------- -------------------------------------------------------- +Time: 1.633s Load: 0.080s, Pack+Encode: 0.571s, Decode+Unpack: 0.982s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1419.2453 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03388043-0.005154_candle _ candle_0.9636924.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03388043-0.005154_candle _ candle_0.9636924.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03417042-0.001187_tank _ tank_0.70379025.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03417042-0.001187_tank _ tank_0.70379025.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 109,416B, BPFP=0.2077 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 189,120B, BPFP=0.3590 +⌛️ [2/4] FRONTEND: Frontend time: 0.565s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.042s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09848782 13.16548796 + layer.39.0 16.63742104 3346.34766764 + ------------------------------------------------------------------------------------- + TOTAL 8.36795443 1679.75657780 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 298536 +BPFP 0.2833 bits/point +EBPFP 0.2833 equivalent bits/point +MSE 1679.756578 +---------------------- -------------------------------------------------------- +Time: 1.657s Load: 0.050s, Pack+Encode: 0.565s, Decode+Unpack: 1.042s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1679.7566 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03417042-0.001187_tank _ tank_0.70379025.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03417042-0.001187_tank _ tank_0.70379025.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03444034-0.002100_maraca _ maraca_0.502369.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03444034-0.002100_maraca _ maraca_0.502369.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 123,732B, BPFP=0.2349 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 316,472B, BPFP=0.6007 +⌛️ [2/4] FRONTEND: Frontend time: 0.506s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.986s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11197850 12.94608597 + layer.39.0 347.54634354 4600.59718173 + ------------------------------------------------------------------------------------- + TOTAL 173.82916102 2306.77163385 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 440204 +BPFP 0.4178 bits/point +EBPFP 0.4178 equivalent bits/point +MSE 2306.771634 +---------------------- -------------------------------------------------------- +Time: 1.543s Load: 0.050s, Pack+Encode: 0.506s, Decode+Unpack: 0.986s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2306.7716 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03444034-0.002100_maraca _ maraca_0.502369.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03444034-0.002100_maraca _ maraca_0.502369.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03445924-0.003505_baseball player _ baseball player_0.6723684.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03445924-0.003505_baseball player _ baseball player_0.6723684.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 108,488B, BPFP=0.2059 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 202,036B, BPFP=0.3835 +⌛️ [2/4] FRONTEND: Frontend time: 0.509s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.988s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09665277 12.90802186 + layer.39.0 26.28463618 3692.29397473 + ------------------------------------------------------------------------------------- + TOTAL 13.19064447 1852.60099830 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 310524 +BPFP 0.2947 bits/point +EBPFP 0.2947 equivalent bits/point +MSE 1852.600998 +---------------------- -------------------------------------------------------- +Time: 1.568s Load: 0.070s, Pack+Encode: 0.509s, Decode+Unpack: 0.988s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1852.6010 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03445924-0.003505_baseball player _ baseball player_0.6723684.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03445924-0.003505_baseball player _ baseball player_0.6723684.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03452741-0.002771_chain _ chain_0.9575044.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03452741-0.002771_chain _ chain_0.9575044.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 121,344B, BPFP=0.2303 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 227,020B, BPFP=0.4309 +⌛️ [2/4] FRONTEND: Frontend time: 0.588s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.997s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12351380 25.63322628 + layer.39.0 42.82565370 4108.13508260 + ------------------------------------------------------------------------------------- + TOTAL 21.47458375 2066.88415444 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 348364 +BPFP 0.3306 bits/point +EBPFP 0.3306 equivalent bits/point +MSE 2066.884154 +---------------------- -------------------------------------------------------- +Time: 1.655s Load: 0.070s, Pack+Encode: 0.588s, Decode+Unpack: 0.997s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2066.8842 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03452741-0.002771_chain _ chain_0.9575044.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03452741-0.002771_chain _ chain_0.9575044.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03483316-0.004974_lighter _ lighter_0.27796906.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03483316-0.004974_lighter _ lighter_0.27796906.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 137,328B, BPFP=0.2607 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 210,924B, BPFP=0.4004 +⌛️ [2/4] FRONTEND: Frontend time: 0.498s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.021s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12993333 13.37712490 + layer.39.0 87.07173986 3044.82167153 + ------------------------------------------------------------------------------------- + TOTAL 43.60083660 1529.09939821 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 348252 +BPFP 0.3305 bits/point +EBPFP 0.3305 equivalent bits/point +MSE 1529.099398 +---------------------- -------------------------------------------------------- +Time: 1.570s Load: 0.052s, Pack+Encode: 0.498s, Decode+Unpack: 1.021s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1529.0994 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03483316-0.004974_lighter _ lighter_0.27796906.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03483316-0.004974_lighter _ lighter_0.27796906.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03584829-0.104805_golf cart _ golf cart_0.73568964.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03584829-0.104805_golf cart _ golf cart_0.73568964.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 114,984B, BPFP=0.2182 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 198,736B, BPFP=0.3772 +⌛️ [2/4] FRONTEND: Frontend time: 0.519s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.017s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09917131 12.97830019 + layer.39.0 24.34873246 3528.83284742 + ------------------------------------------------------------------------------------- + TOTAL 12.22395189 1770.90557381 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 313720 +BPFP 0.2977 bits/point +EBPFP 0.2977 equivalent bits/point +MSE 1770.905574 +---------------------- -------------------------------------------------------- +Time: 1.607s Load: 0.071s, Pack+Encode: 0.519s, Decode+Unpack: 1.017s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1770.9056 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03584829-0.104805_golf cart _ golf cart_0.73568964.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03584829-0.104805_golf cart _ golf cart_0.73568964.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03590841-0.001115_rocking chair _ rocking chair_0.2192492.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03590841-0.001115_rocking chair _ rocking chair_0.2192492.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.058s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 115,088B, BPFP=0.2184 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 164,848B, BPFP=0.3129 +⌛️ [2/4] FRONTEND: Frontend time: 0.517s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.979s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11329899 13.18432166 + layer.39.0 19.97532495 3051.45772595 + ------------------------------------------------------------------------------------- + TOTAL 10.04431197 1532.32102381 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 279936 +BPFP 0.2657 bits/point +EBPFP 0.2657 equivalent bits/point +MSE 1532.321024 +---------------------- -------------------------------------------------------- +Time: 1.553s Load: 0.058s, Pack+Encode: 0.517s, Decode+Unpack: 0.979s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1532.3210 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03590841-0.001115_rocking chair _ rocking chair_0.2192492.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03590841-0.001115_rocking chair _ rocking chair_0.2192492.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03617480-0.003238_basketball _ basketball_0.67568874.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03617480-0.003238_basketball _ basketball_0.67568874.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 131,852B, BPFP=0.2503 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 291,076B, BPFP=0.5525 +⌛️ [2/4] FRONTEND: Frontend time: 0.578s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.041s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12967051 37.83105564 + layer.39.0 57.10576865 4014.84475219 + ------------------------------------------------------------------------------------- + TOTAL 28.61771958 2026.33790391 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 422928 +BPFP 0.4014 bits/point +EBPFP 0.4014 equivalent bits/point +MSE 2026.337904 +---------------------- -------------------------------------------------------- +Time: 1.689s Load: 0.070s, Pack+Encode: 0.578s, Decode+Unpack: 1.041s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2026.3379 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03617480-0.003238_basketball _ basketball_0.67568874.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03617480-0.003238_basketball _ basketball_0.67568874.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03666591-0.004622_torch _ torch_0.99906796.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03666591-0.004622_torch _ torch_0.99906796.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 99,008B, BPFP=0.1879 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 179,808B, BPFP=0.3413 +⌛️ [2/4] FRONTEND: Frontend time: 0.578s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.014s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.05477861 13.33327449 + layer.39.0 7.78975672 2630.82750243 + ------------------------------------------------------------------------------------- + TOTAL 7.92226767 1322.08038846 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 278816 +BPFP 0.2646 bits/point +EBPFP 0.2646 equivalent bits/point +MSE 1322.080388 +---------------------- -------------------------------------------------------- +Time: 1.662s Load: 0.070s, Pack+Encode: 0.578s, Decode+Unpack: 1.014s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1322.0804 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03666591-0.004622_torch _ torch_0.99906796.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03666591-0.004622_torch _ torch_0.99906796.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03670208-0.003899_hair dryer _ grand piano_0.7359066.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03670208-0.003899_hair dryer _ grand piano_0.7359066.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 117,992B, BPFP=0.2240 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 240,212B, BPFP=0.4559 +⌛️ [2/4] FRONTEND: Frontend time: 0.541s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.980s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11232473 13.15744674 + layer.39.0 36.60432231 4020.00558795 + ------------------------------------------------------------------------------------- + TOTAL 18.35832352 2016.58151734 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 358204 +BPFP 0.3400 bits/point +EBPFP 0.3400 equivalent bits/point +MSE 2016.581517 +---------------------- -------------------------------------------------------- +Time: 1.570s Load: 0.050s, Pack+Encode: 0.541s, Decode+Unpack: 0.980s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2016.5815 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03670208-0.003899_hair dryer _ grand piano_0.7359066.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03670208-0.003899_hair dryer _ grand piano_0.7359066.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03717622-0.001175_sundial _ sundial_0.9998197.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03717622-0.001175_sundial _ sundial_0.9998197.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 128,288B, BPFP=0.2435 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 244,076B, BPFP=0.4633 +⌛️ [2/4] FRONTEND: Frontend time: 0.573s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.982s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13381931 13.44357860 + layer.39.0 773.52204810 3809.29397473 + ------------------------------------------------------------------------------------- + TOTAL 386.82793371 1911.36877667 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 372364 +BPFP 0.3534 bits/point +EBPFP 0.3534 equivalent bits/point +MSE 1911.368777 +---------------------- -------------------------------------------------------- +Time: 1.626s Load: 0.070s, Pack+Encode: 0.573s, Decode+Unpack: 0.982s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1911.3688 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03717622-0.001175_sundial _ sundial_0.9998197.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03717622-0.001175_sundial _ sundial_0.9998197.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03720891-0.001854_African bush elephant _ African bush elephant_0.722115.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03720891-0.001854_African bush elephant _ African bush elephant_0.722115.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 112,728B, BPFP=0.2140 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 270,276B, BPFP=0.5130 +⌛️ [2/4] FRONTEND: Frontend time: 0.510s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.988s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09642763 12.89564827 + layer.39.0 155.23232507 4154.05830904 + ------------------------------------------------------------------------------------- + TOTAL 77.66437635 2083.47697865 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 383004 +BPFP 0.3635 bits/point +EBPFP 0.3635 equivalent bits/point +MSE 2083.476979 +---------------------- -------------------------------------------------------- +Time: 1.548s Load: 0.051s, Pack+Encode: 0.510s, Decode+Unpack: 0.988s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2083.4770 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03720891-0.001854_African bush elephant _ African bush elephant_0.722115.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03720891-0.001854_African bush elephant _ African bush elephant_0.722115.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03721384-0.003327_chain _ chain_0.5599652.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03721384-0.003327_chain _ chain_0.5599652.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 103,904B, BPFP=0.1972 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 166,076B, BPFP=0.3152 +⌛️ [2/4] FRONTEND: Frontend time: 0.525s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.993s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09561452 13.15263321 + layer.39.0 742.66502672 3560.86103013 + ------------------------------------------------------------------------------------- + TOTAL 371.38032062 1787.00683167 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 269980 +BPFP 0.2562 bits/point +EBPFP 0.2562 equivalent bits/point +MSE 1787.006832 +---------------------- -------------------------------------------------------- +Time: 1.578s Load: 0.061s, Pack+Encode: 0.525s, Decode+Unpack: 0.993s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1787.0068 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03721384-0.003327_chain _ chain_0.5599652.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03721384-0.003327_chain _ chain_0.5599652.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03724870-0.001366_Christmas stocking _ Christmas stocking_0.5709616.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03724870-0.001366_Christmas stocking _ Christmas stocking_0.5709616.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 120,008B, BPFP=0.2278 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 211,464B, BPFP=0.4014 +⌛️ [2/4] FRONTEND: Frontend time: 0.559s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.018s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10329660 12.94764714 + layer.39.0 513.92243683 3484.57458698 + ------------------------------------------------------------------------------------- + TOTAL 257.01286671 1748.76111706 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 331472 +BPFP 0.3146 bits/point +EBPFP 0.3146 equivalent bits/point +MSE 1748.761117 +---------------------- -------------------------------------------------------- +Time: 1.627s Load: 0.050s, Pack+Encode: 0.559s, Decode+Unpack: 1.018s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1748.7611 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03724870-0.001366_Christmas stocking _ Christmas stocking_0.5709616.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03724870-0.001366_Christmas stocking _ Christmas stocking_0.5709616.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03775071-0.000145_Christmas stocking _ Christmas stocking_0.9986047.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03775071-0.000145_Christmas stocking _ Christmas stocking_0.9986047.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 113,744B, BPFP=0.2159 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 235,132B, BPFP=0.4463 +⌛️ [2/4] FRONTEND: Frontend time: 0.588s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.003s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09700392 13.08120464 + layer.39.0 284.92189018 4012.23906706 + ------------------------------------------------------------------------------------- + TOTAL 142.50944705 2012.66013585 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 348876 +BPFP 0.3311 bits/point +EBPFP 0.3311 equivalent bits/point +MSE 2012.660136 +---------------------- -------------------------------------------------------- +Time: 1.661s Load: 0.069s, Pack+Encode: 0.588s, Decode+Unpack: 1.003s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2012.6601 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03775071-0.000145_Christmas stocking _ Christmas stocking_0.9986047.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03775071-0.000145_Christmas stocking _ Christmas stocking_0.9986047.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03788195-0.005975_steam locomotive _ steam locomotive_0.42419377.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03788195-0.005975_steam locomotive _ steam locomotive_0.42419377.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 121,540B, BPFP=0.2307 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 185,116B, BPFP=0.3514 +⌛️ [2/4] FRONTEND: Frontend time: 0.547s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.976s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10790903 13.07062758 + layer.39.0 10.34781284 3121.12123421 + ------------------------------------------------------------------------------------- + TOTAL 5.22786094 1567.09593089 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 306656 +BPFP 0.2910 bits/point +EBPFP 0.2910 equivalent bits/point +MSE 1567.095931 +---------------------- -------------------------------------------------------- +Time: 1.573s Load: 0.050s, Pack+Encode: 0.547s, Decode+Unpack: 0.976s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1567.0959 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03788195-0.005975_steam locomotive _ steam locomotive_0.42419377.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03788195-0.005975_steam locomotive _ steam locomotive_0.42419377.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03837869-0.002023_lighthouse _ lighthouse_0.5898798.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03837869-0.002023_lighthouse _ lighthouse_0.5898798.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 108,740B, BPFP=0.2064 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 173,280B, BPFP=0.3289 +⌛️ [2/4] FRONTEND: Frontend time: 0.552s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.984s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12703056 13.13266065 + layer.39.0 141.21340500 3293.95408163 + ------------------------------------------------------------------------------------- + TOTAL 70.67021778 1653.54337114 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 282020 +BPFP 0.2676 bits/point +EBPFP 0.2676 equivalent bits/point +MSE 1653.543371 +---------------------- -------------------------------------------------------- +Time: 1.607s Load: 0.070s, Pack+Encode: 0.552s, Decode+Unpack: 0.984s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1653.5434 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03837869-0.002023_lighthouse _ lighthouse_0.5898798.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03837869-0.002023_lighthouse _ lighthouse_0.5898798.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03840681-0.002188_ladybug _ ladybug_0.9972365.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03840681-0.002188_ladybug _ ladybug_0.9972365.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.079s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 102,512B, BPFP=0.1946 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 167,036B, BPFP=0.3170 +⌛️ [2/4] FRONTEND: Frontend time: 0.572s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.001s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09487485 13.15597668 + layer.39.0 29.40353574 2629.41788144 + ------------------------------------------------------------------------------------- + TOTAL 14.74920530 1321.28692906 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 269548 +BPFP 0.2558 bits/point +EBPFP 0.2558 equivalent bits/point +MSE 1321.286929 +---------------------- -------------------------------------------------------- +Time: 1.653s Load: 0.079s, Pack+Encode: 0.572s, Decode+Unpack: 1.001s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1321.2869 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03840681-0.002188_ladybug _ ladybug_0.9972365.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03840681-0.002188_ladybug _ ladybug_0.9972365.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03888257-0.004083_rugby ball _ snail_0.41588444.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03888257-0.004083_rugby ball _ snail_0.41588444.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 99,376B, BPFP=0.1886 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 174,784B, BPFP=0.3318 +⌛️ [2/4] FRONTEND: Frontend time: 0.571s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.017s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10005040 13.19449727 + layer.39.0 7.47115060 2411.80442177 + ------------------------------------------------------------------------------------- + TOTAL 3.78560050 1212.49945952 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 274160 +BPFP 0.2602 bits/point +EBPFP 0.2602 equivalent bits/point +MSE 1212.499460 +---------------------- -------------------------------------------------------- +Time: 1.638s Load: 0.050s, Pack+Encode: 0.571s, Decode+Unpack: 1.017s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1212.4995 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03888257-0.004083_rugby ball _ snail_0.41588444.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03888257-0.004083_rugby ball _ snail_0.41588444.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03891332-0.003727_syringe _ syringe_0.93799996.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03891332-0.003727_syringe _ syringe_0.93799996.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 108,092B, BPFP=0.2052 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 207,424B, BPFP=0.3937 +⌛️ [2/4] FRONTEND: Frontend time: 0.512s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.979s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09617506 13.08309797 + layer.39.0 18.45312310 4162.27502430 + ------------------------------------------------------------------------------------- + TOTAL 9.27464908 2087.67906113 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 315516 +BPFP 0.2994 bits/point +EBPFP 0.2994 equivalent bits/point +MSE 2087.679061 +---------------------- -------------------------------------------------------- +Time: 1.561s Load: 0.070s, Pack+Encode: 0.512s, Decode+Unpack: 0.979s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2087.6791 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03891332-0.003727_syringe _ syringe_0.93799996.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03891332-0.003727_syringe _ syringe_0.93799996.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03982430-0.005102_couch _ couch_0.9976859.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03982430-0.005102_couch _ couch_0.9976859.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 103,668B, BPFP=0.1968 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 188,820B, BPFP=0.3584 +⌛️ [2/4] FRONTEND: Frontend time: 0.559s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.984s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09691652 13.27072989 + layer.39.0 169.89398081 3105.67711370 + ------------------------------------------------------------------------------------- + TOTAL 84.99544866 1559.47392180 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 292488 +BPFP 0.2776 bits/point +EBPFP 0.2776 equivalent bits/point +MSE 1559.473922 +---------------------- -------------------------------------------------------- +Time: 1.612s Load: 0.070s, Pack+Encode: 0.559s, Decode+Unpack: 0.984s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1559.4739 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03982430-0.005102_couch _ couch_0.9976859.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03982430-0.005102_couch _ couch_0.9976859.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04033901-0.007476_envelope _ envelope_0.9990971.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04033901-0.007476_envelope _ envelope_0.9990971.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 107,284B, BPFP=0.2036 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 172,932B, BPFP=0.3282 +⌛️ [2/4] FRONTEND: Frontend time: 0.554s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.001s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10364226 13.28740548 + layer.39.0 7.34252906 2811.60762877 + ------------------------------------------------------------------------------------- + TOTAL 3.72308566 1412.44751712 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 280216 +BPFP 0.2659 bits/point +EBPFP 0.2659 equivalent bits/point +MSE 1412.447517 +---------------------- -------------------------------------------------------- +Time: 1.625s Load: 0.070s, Pack+Encode: 0.554s, Decode+Unpack: 1.001s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1412.4475 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04033901-0.007476_envelope _ envelope_0.9990971.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04033901-0.007476_envelope _ envelope_0.9990971.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04039381-0.000054_hair dryer _ hair dryer_0.5667548.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04039381-0.000054_hair dryer _ hair dryer_0.5667548.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.068s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 108,416B, BPFP=0.2058 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 257,492B, BPFP=0.4887 +⌛️ [2/4] FRONTEND: Frontend time: 0.557s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.010s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09588603 13.26292688 + layer.39.0 26.21653304 3627.04008746 + ------------------------------------------------------------------------------------- + TOTAL 13.15620954 1820.15150717 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 365908 +BPFP 0.3473 bits/point +EBPFP 0.3473 equivalent bits/point +MSE 1820.151507 +---------------------- -------------------------------------------------------- +Time: 1.635s Load: 0.068s, Pack+Encode: 0.557s, Decode+Unpack: 1.010s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1820.1515 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04039381-0.000054_hair dryer _ hair dryer_0.5667548.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04039381-0.000054_hair dryer _ hair dryer_0.5667548.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04118538-0.005804_cowboy boot _ cowboy boot_0.21208441.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04118538-0.005804_cowboy boot _ cowboy boot_0.21208441.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 116,980B, BPFP=0.2220 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 204,784B, BPFP=0.3887 +⌛️ [2/4] FRONTEND: Frontend time: 0.536s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.996s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09664223 12.81513169 + layer.39.0 8.64007266 3012.93343052 + ------------------------------------------------------------------------------------- + TOTAL 4.36835744 1512.87428110 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 321764 +BPFP 0.3054 bits/point +EBPFP 0.3054 equivalent bits/point +MSE 1512.874281 +---------------------- -------------------------------------------------------- +Time: 1.602s Load: 0.070s, Pack+Encode: 0.536s, Decode+Unpack: 0.996s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1512.8743 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04118538-0.005804_cowboy boot _ cowboy boot_0.21208441.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04118538-0.005804_cowboy boot _ cowboy boot_0.21208441.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04146614-0.008793_marimba _ marimba_0.54555196.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04146614-0.008793_marimba _ marimba_0.54555196.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 114,820B, BPFP=0.2179 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 192,444B, BPFP=0.3653 +⌛️ [2/4] FRONTEND: Frontend time: 0.533s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.001s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09774729 12.95034052 + layer.39.0 155.07908163 3703.24271137 + ------------------------------------------------------------------------------------- + TOTAL 77.58841446 1858.09652594 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 307264 +BPFP 0.2916 bits/point +EBPFP 0.2916 equivalent bits/point +MSE 1858.096526 +---------------------- -------------------------------------------------------- +Time: 1.605s Load: 0.070s, Pack+Encode: 0.533s, Decode+Unpack: 1.001s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1858.0965 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04146614-0.008793_marimba _ marimba_0.54555196.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04146614-0.008793_marimba _ marimba_0.54555196.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04179913-0.006024_guacamole _ custard apple_0.5550306.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04179913-0.006024_guacamole _ custard apple_0.5550306.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 124,656B, BPFP=0.2366 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 233,848B, BPFP=0.4439 +⌛️ [2/4] FRONTEND: Frontend time: 0.544s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.004s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11409367 13.06228552 + layer.39.0 68.43204871 3801.56438290 + ------------------------------------------------------------------------------------- + TOTAL 34.27307119 1907.31333421 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 358504 +BPFP 0.3402 bits/point +EBPFP 0.3402 equivalent bits/point +MSE 1907.313334 +---------------------- -------------------------------------------------------- +Time: 1.618s Load: 0.070s, Pack+Encode: 0.544s, Decode+Unpack: 1.004s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1907.3133 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04179913-0.006024_guacamole _ custard apple_0.5550306.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04179913-0.006024_guacamole _ custard apple_0.5550306.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04208210-0.007213_doormat _ doormat_0.81736016.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04208210-0.007213_doormat _ doormat_0.81736016.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 128,260B, BPFP=0.2434 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 199,844B, BPFP=0.3793 +⌛️ [2/4] FRONTEND: Frontend time: 0.576s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.002s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10601767 12.87325851 + layer.39.0 349.44518343 3479.16885326 + ------------------------------------------------------------------------------------- + TOTAL 174.77560055 1746.02105588 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 328104 +BPFP 0.3114 bits/point +EBPFP 0.3114 equivalent bits/point +MSE 1746.021056 +---------------------- -------------------------------------------------------- +Time: 1.648s Load: 0.070s, Pack+Encode: 0.576s, Decode+Unpack: 1.002s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1746.0211 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04208210-0.007213_doormat _ doormat_0.81736016.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04208210-0.007213_doormat _ doormat_0.81736016.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04270147-0.000435_rotary dial telephone _ rotary dial telephone_0.9268941.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04270147-0.000435_rotary dial telephone _ rotary dial telephone_0.9268941.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 104,956B, BPFP=0.1992 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 274,168B, BPFP=0.5204 +⌛️ [2/4] FRONTEND: Frontend time: 0.584s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.048s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09464848 13.06957510 + layer.39.0 229.78908528 3835.86054422 + ------------------------------------------------------------------------------------- + TOTAL 114.94186688 1924.46505966 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 379124 +BPFP 0.3598 bits/point +EBPFP 0.3598 equivalent bits/point +MSE 1924.465060 +---------------------- -------------------------------------------------------- +Time: 1.702s Load: 0.070s, Pack+Encode: 0.584s, Decode+Unpack: 1.048s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1924.4651 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04270147-0.000435_rotary dial telephone _ rotary dial telephone_0.9268941.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04270147-0.000435_rotary dial telephone _ rotary dial telephone_0.9268941.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04317175-0.006894_bow tie _ bow tie_0.95877784.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04317175-0.006894_bow tie _ bow tie_0.95877784.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 111,576B, BPFP=0.2118 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 229,588B, BPFP=0.4358 +⌛️ [2/4] FRONTEND: Frontend time: 0.559s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.996s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09706025 13.22415953 + layer.39.0 10.87108806 3497.53644315 + ------------------------------------------------------------------------------------- + TOTAL 5.48407415 1755.38030134 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 341164 +BPFP 0.3238 bits/point +EBPFP 0.3238 equivalent bits/point +MSE 1755.380301 +---------------------- -------------------------------------------------------- +Time: 1.625s Load: 0.070s, Pack+Encode: 0.559s, Decode+Unpack: 0.996s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1755.3803 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04317175-0.006894_bow tie _ bow tie_0.95877784.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04317175-0.006894_bow tie _ bow tie_0.95877784.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04347754-0.001405_viaduct _ viaduct_0.8445672.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04347754-0.001405_viaduct _ viaduct_0.8445672.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 105,424B, BPFP=0.2001 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 208,748B, BPFP=0.3962 +⌛️ [2/4] FRONTEND: Frontend time: 0.503s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.969s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09586499 13.30099953 + layer.39.0 267.55718537 3528.33284742 + ------------------------------------------------------------------------------------- + TOTAL 133.82652518 1770.81692348 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 314172 +BPFP 0.2982 bits/point +EBPFP 0.2982 equivalent bits/point +MSE 1770.816923 +---------------------- -------------------------------------------------------- +Time: 1.523s Load: 0.051s, Pack+Encode: 0.503s, Decode+Unpack: 0.969s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1770.8169 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04347754-0.001405_viaduct _ viaduct_0.8445672.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04347754-0.001405_viaduct _ viaduct_0.8445672.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04355338-0.000791_envelope _ envelope_0.9969598.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04355338-0.000791_envelope _ envelope_0.9969598.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 111,308B, BPFP=0.2113 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 190,952B, BPFP=0.3624 +⌛️ [2/4] FRONTEND: Frontend time: 0.578s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.041s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10273007 13.06630186 + layer.39.0 331.89978134 3426.81754130 + ------------------------------------------------------------------------------------- + TOTAL 166.00125571 1719.94192158 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 302260 +BPFP 0.2869 bits/point +EBPFP 0.2869 equivalent bits/point +MSE 1719.941922 +---------------------- -------------------------------------------------------- +Time: 1.688s Load: 0.069s, Pack+Encode: 0.578s, Decode+Unpack: 1.041s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1719.9419 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04355338-0.000791_envelope _ envelope_0.9969598.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04355338-0.000791_envelope _ envelope_0.9969598.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04366367-0.002021_parachute _ parachute_0.9226023.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04366367-0.002021_parachute _ parachute_0.9226023.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 98,484B, BPFP=0.1869 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 153,156B, BPFP=0.2907 +⌛️ [2/4] FRONTEND: Frontend time: 0.535s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.062s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09577132 13.15391156 + layer.39.0 47.60657343 2681.40816327 + ------------------------------------------------------------------------------------- + TOTAL 23.85117238 1347.28103741 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 251640 +BPFP 0.2388 bits/point +EBPFP 0.2388 equivalent bits/point +MSE 1347.281037 +---------------------- -------------------------------------------------------- +Time: 1.648s Load: 0.051s, Pack+Encode: 0.535s, Decode+Unpack: 1.062s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1347.2810 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04366367-0.002021_parachute _ parachute_0.9226023.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04366367-0.002021_parachute _ parachute_0.9226023.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04376876-0.004615_lighter _ lighter_0.69176143.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04376876-0.004615_lighter _ lighter_0.69176143.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 108,308B, BPFP=0.2056 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 237,868B, BPFP=0.4515 +⌛️ [2/4] FRONTEND: Frontend time: 0.522s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.989s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09912059 13.24141308 + layer.39.0 173.01079628 3573.27162293 + ------------------------------------------------------------------------------------- + TOTAL 86.55495844 1793.25651801 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 346176 +BPFP 0.3285 bits/point +EBPFP 0.3285 equivalent bits/point +MSE 1793.256518 +---------------------- -------------------------------------------------------- +Time: 1.583s Load: 0.071s, Pack+Encode: 0.522s, Decode+Unpack: 0.989s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1793.2565 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04376876-0.004615_lighter _ lighter_0.69176143.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04376876-0.004615_lighter _ lighter_0.69176143.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04442312-0.001690_washing machine _ washing machine_0.8494714.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04442312-0.001690_washing machine _ washing machine_0.8494714.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 98,840B, BPFP=0.1876 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 186,556B, BPFP=0.3541 +⌛️ [2/4] FRONTEND: Frontend time: 0.570s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.032s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.08302300 13.36723685 + layer.39.0 28.24609944 2850.36686103 + ------------------------------------------------------------------------------------- + TOTAL 18.16456122 1431.86704894 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 285396 +BPFP 0.2709 bits/point +EBPFP 0.2709 equivalent bits/point +MSE 1431.867049 +---------------------- -------------------------------------------------------- +Time: 1.672s Load: 0.070s, Pack+Encode: 0.570s, Decode+Unpack: 1.032s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1431.8670 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04442312-0.001690_washing machine _ washing machine_0.8494714.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04442312-0.001690_washing machine _ washing machine_0.8494714.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04456115-0.002573_hair dryer _ envelope_0.34823084.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04456115-0.002573_hair dryer _ envelope_0.34823084.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 100,864B, BPFP=0.1914 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 220,444B, BPFP=0.4184 +⌛️ [2/4] FRONTEND: Frontend time: 0.506s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.001s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09444211 13.04314337 + layer.39.0 8.80792942 3293.41253644 + ------------------------------------------------------------------------------------- + TOTAL 4.45118577 1653.22783991 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 321308 +BPFP 0.3049 bits/point +EBPFP 0.3049 equivalent bits/point +MSE 1653.227840 +---------------------- -------------------------------------------------------- +Time: 1.558s Load: 0.051s, Pack+Encode: 0.506s, Decode+Unpack: 1.001s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1653.2278 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04456115-0.002573_hair dryer _ envelope_0.34823084.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04456115-0.002573_hair dryer _ envelope_0.34823084.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04509417-0.000350_sundial _ sundial_0.99936897.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04509417-0.000350_sundial _ sundial_0.99936897.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.049s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 109,592B, BPFP=0.2080 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 175,944B, BPFP=0.3340 +⌛️ [2/4] FRONTEND: Frontend time: 0.504s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.985s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10319057 13.06482230 + layer.39.0 8.14296913 2594.18999028 + ------------------------------------------------------------------------------------- + TOTAL 4.12307985 1303.62740629 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 285536 +BPFP 0.2710 bits/point +EBPFP 0.2710 equivalent bits/point +MSE 1303.627406 +---------------------- -------------------------------------------------------- +Time: 1.538s Load: 0.049s, Pack+Encode: 0.504s, Decode+Unpack: 0.985s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1303.6274 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04509417-0.000350_sundial _ sundial_0.99936897.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04509417-0.000350_sundial _ sundial_0.99936897.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04532670-0.000670_spider web _ spider web_0.70711935.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04532670-0.000670_spider web _ spider web_0.70711935.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 114,060B, BPFP=0.2165 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 210,032B, BPFP=0.3987 +⌛️ [2/4] FRONTEND: Frontend time: 0.570s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.983s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09618602 12.98195495 + layer.39.0 175.41615039 3124.59718173 + ------------------------------------------------------------------------------------- + TOTAL 87.75616821 1568.78956834 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 324092 +BPFP 0.3076 bits/point +EBPFP 0.3076 equivalent bits/point +MSE 1568.789568 +---------------------- -------------------------------------------------------- +Time: 1.604s Load: 0.051s, Pack+Encode: 0.570s, Decode+Unpack: 0.983s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1568.7896 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04532670-0.000670_spider web _ spider web_0.70711935.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04532670-0.000670_spider web _ spider web_0.70711935.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04540053-0.000734_balance beam _ balance beam_0.99765223.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04540053-0.000734_balance beam _ balance beam_0.99765223.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 99,920B, BPFP=0.1897 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 179,944B, BPFP=0.3415 +⌛️ [2/4] FRONTEND: Frontend time: 0.514s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.972s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09941827 13.32739709 + layer.39.0 8.11341412 3041.51263362 + ------------------------------------------------------------------------------------- + TOTAL 4.10641619 1527.42001536 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 279864 +BPFP 0.2656 bits/point +EBPFP 0.2656 equivalent bits/point +MSE 1527.420015 +---------------------- -------------------------------------------------------- +Time: 1.556s Load: 0.070s, Pack+Encode: 0.514s, Decode+Unpack: 0.972s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1527.4200 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04540053-0.000734_balance beam _ balance beam_0.99765223.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04540053-0.000734_balance beam _ balance beam_0.99765223.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04606251-0.003483_cliff _ cliff_0.9972174.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04606251-0.003483_cliff _ cliff_0.9972174.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 116,032B, BPFP=0.2202 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 161,008B, BPFP=0.3056 +⌛️ [2/4] FRONTEND: Frontend time: 0.513s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.970s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09940710 12.95267705 + layer.39.0 906.86880466 3339.79203110 + ------------------------------------------------------------------------------------- + TOTAL 453.48410588 1676.37235408 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 277040 +BPFP 0.2629 bits/point +EBPFP 0.2629 equivalent bits/point +MSE 1676.372354 +---------------------- -------------------------------------------------------- +Time: 1.552s Load: 0.069s, Pack+Encode: 0.513s, Decode+Unpack: 0.970s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1676.3724 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04606251-0.003483_cliff _ cliff_0.9972174.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04606251-0.003483_cliff _ cliff_0.9972174.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07583066-0.003935_maraca _ maraca_0.5370013.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07583066-0.003935_maraca _ maraca_0.5370013.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 108,532B, BPFP=0.2060 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 184,416B, BPFP=0.3500 +⌛️ [2/4] FRONTEND: Frontend time: 0.511s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.979s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12045678 13.01720705 + layer.39.0 38.29438092 3228.85471331 + ------------------------------------------------------------------------------------- + TOTAL 19.20741885 1620.93596018 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 292948 +BPFP 0.2780 bits/point +EBPFP 0.2780 equivalent bits/point +MSE 1620.935960 +---------------------- -------------------------------------------------------- +Time: 1.559s Load: 0.069s, Pack+Encode: 0.511s, Decode+Unpack: 0.979s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1620.9360 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07583066-0.003935_maraca _ maraca_0.5370013.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n07583066-0.003935_maraca _ maraca_0.5370013.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07695742-0.003226_ant _ ant_0.64413536.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07695742-0.003226_ant _ ant_0.64413536.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 143,488B, BPFP=0.2724 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 200,640B, BPFP=0.3808 +⌛️ [2/4] FRONTEND: Frontend time: 0.527s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.994s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.16263347 12.97665645 + layer.39.0 172.10254191 3473.01336249 + ------------------------------------------------------------------------------------- + TOTAL 86.13258769 1742.99500947 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 344128 +BPFP 0.3266 bits/point +EBPFP 0.3266 equivalent bits/point +MSE 1742.995009 +---------------------- -------------------------------------------------------- +Time: 1.591s Load: 0.071s, Pack+Encode: 0.527s, Decode+Unpack: 0.994s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1742.9950 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07695742-0.003226_ant _ ant_0.64413536.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n07695742-0.003226_ant _ ant_0.64413536.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07718472-0.001330_spatula _ spatula_0.5388231.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07718472-0.001330_spatula _ spatula_0.5388231.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 117,728B, BPFP=0.2235 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 215,940B, BPFP=0.4099 +⌛️ [2/4] FRONTEND: Frontend time: 0.557s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.015s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09672572 13.02827855 + layer.39.0 34.52145211 3816.93391642 + ------------------------------------------------------------------------------------- + TOTAL 17.30908891 1914.98109749 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 333668 +BPFP 0.3167 bits/point +EBPFP 0.3167 equivalent bits/point +MSE 1914.981097 +---------------------- -------------------------------------------------------- +Time: 1.642s Load: 0.070s, Pack+Encode: 0.557s, Decode+Unpack: 1.015s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1914.9811 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07718472-0.001330_spatula _ spatula_0.5388231.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n07718472-0.001330_spatula _ spatula_0.5388231.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07831146-0.002710_guacamole _ guacamole_0.99510396.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07831146-0.002710_guacamole _ guacamole_0.99510396.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 117,492B, BPFP=0.2230 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 215,700B, BPFP=0.4094 +⌛️ [2/4] FRONTEND: Frontend time: 0.497s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.976s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09717902 12.99893992 + layer.39.0 26.55584533 3994.23032070 + ------------------------------------------------------------------------------------- + TOTAL 13.32651218 2003.61463031 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 333192 +BPFP 0.3162 bits/point +EBPFP 0.3162 equivalent bits/point +MSE 2003.614630 +---------------------- -------------------------------------------------------- +Time: 1.523s Load: 0.050s, Pack+Encode: 0.497s, Decode+Unpack: 0.976s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2003.6146 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07831146-0.002710_guacamole _ guacamole_0.99510396.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n07831146-0.002710_guacamole _ guacamole_0.99510396.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n09229709-0.002628_stethoscope _ stethoscope_0.9726458.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n09229709-0.002628_stethoscope _ stethoscope_0.9726458.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 109,772B, BPFP=0.2084 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 176,904B, BPFP=0.3358 +⌛️ [2/4] FRONTEND: Frontend time: 0.509s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.984s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10247729 13.10547539 + layer.39.0 58.71458181 2974.68221574 + ------------------------------------------------------------------------------------- + TOTAL 29.40852955 1493.89384557 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 286676 +BPFP 0.2721 bits/point +EBPFP 0.2721 equivalent bits/point +MSE 1493.893846 +---------------------- -------------------------------------------------------- +Time: 1.553s Load: 0.060s, Pack+Encode: 0.509s, Decode+Unpack: 0.984s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1493.8938 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n09229709-0.002628_stethoscope _ stethoscope_0.9726458.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n09229709-0.002628_stethoscope _ stethoscope_0.9726458.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12057211-0.000404_nail _ newt_0.31321314.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12057211-0.000404_nail _ newt_0.31321314.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 141,940B, BPFP=0.2694 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 162,788B, BPFP=0.3090 +⌛️ [2/4] FRONTEND: Frontend time: 0.522s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.982s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11577855 51.17401603 + layer.39.0 8.72387956 2786.35787172 + ------------------------------------------------------------------------------------- + TOTAL 4.41982905 1418.76594388 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 304728 +BPFP 0.2892 bits/point +EBPFP 0.2892 equivalent bits/point +MSE 1418.765944 +---------------------- -------------------------------------------------------- +Time: 1.554s Load: 0.051s, Pack+Encode: 0.522s, Decode+Unpack: 0.982s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1418.7659 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12057211-0.000404_nail _ newt_0.31321314.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n12057211-0.000404_nail _ newt_0.31321314.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12144580-0.002806_banana _ banana_0.999156.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12144580-0.002806_banana _ banana_0.999156.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 114,140B, BPFP=0.2166 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 239,048B, BPFP=0.4537 +⌛️ [2/4] FRONTEND: Frontend time: 0.511s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.978s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09629347 13.01024299 + layer.39.0 105.38953930 4325.53061224 + ------------------------------------------------------------------------------------- + TOTAL 52.74291638 2169.27042762 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 353188 +BPFP 0.3352 bits/point +EBPFP 0.3352 equivalent bits/point +MSE 2169.270428 +---------------------- -------------------------------------------------------- +Time: 1.560s Load: 0.071s, Pack+Encode: 0.511s, Decode+Unpack: 0.978s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2169.2704 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12144580-0.002806_banana _ banana_0.999156.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n12144580-0.002806_banana _ banana_0.999156.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12267677-0.004617_pomegranate _ pomegranate_0.9159373.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12267677-0.004617_pomegranate _ pomegranate_0.9159373.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 118,168B, BPFP=0.2243 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 211,528B, BPFP=0.4015 +⌛️ [2/4] FRONTEND: Frontend time: 0.532s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.979s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10323383 13.04048986 + layer.39.0 78.12042942 3642.75242954 + ------------------------------------------------------------------------------------- + TOTAL 39.11183162 1827.89645970 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 329696 +BPFP 0.3129 bits/point +EBPFP 0.3129 equivalent bits/point +MSE 1827.896460 +---------------------- -------------------------------------------------------- +Time: 1.561s Load: 0.050s, Pack+Encode: 0.532s, Decode+Unpack: 0.979s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1827.8965 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12267677-0.004617_pomegranate _ pomegranate_0.9159373.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n12267677-0.004617_pomegranate _ pomegranate_0.9159373.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 0.2962 bits/point +Avg EBPFP 0.2962 equivalent bits/point +Avg MSE 1658.228554 +Avg Time 1.615s +------------------------ ---------------------------- diff --git a/lambda0.004/hyperprior-featurecoding-8bit-individual/cls_in1kr/dtufc_hyperprior-featurecoding_dinov3-total_individual.log b/lambda0.004/hyperprior-featurecoding-8bit-individual/cls_in1kr/dtufc_hyperprior-featurecoding_dinov3-total_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..f0ca710cafb9523d57633a242f7fb8038a7ec482 --- /dev/null +++ b/lambda0.004/hyperprior-featurecoding-8bit-individual/cls_in1kr/dtufc_hyperprior-featurecoding_dinov3-total_individual.log @@ -0,0 +1,9344 @@ +Experiment: dtufc_hyperprior-featurecoding_dinov3-total_individual +Log file: output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/dtufc_hyperprior-featurecoding_dinov3-total_individual.log +DTUFCCodecConfig: + arch: hyperprior-featurecoding + handler: dinov3-total + checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.004_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.004_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 559 +Loaded hyperprior-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.9' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.19' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.29' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.39' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json +Loaded per-key mappings: model=dinov3-total + Keys: ['layer.9', 'layer.19', 'layer.29', 'layer.39'] +---------------- ------------------------------------------------------------------------------------------------------------------------------ +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +Checkpoint codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.004_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-DINOv3/imagenet1k-r +Output output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr +---------------- ------------------------------------------------------------------------------------------------------------------------------ +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01443537-graffiti_3.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01443537-graffiti_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.091s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 113,396B, BPFP=0.2152 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 192,152B, BPFP=0.3647 +⌛️ [2/4] FRONTEND: Frontend time: 0.791s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.089s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09690064 13.21310701 + layer.39.0 23.14008974 3204.14261419 + ------------------------------------------------------------------------------------- + TOTAL 11.61849519 1608.67786060 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 305548 +BPFP 0.2900 bits/point +EBPFP 0.2900 equivalent bits/point +MSE 1608.677861 +---------------------- -------------------------------------------------------- +Time: 1.971s Load: 0.091s, Pack+Encode: 0.791s, Decode+Unpack: 1.089s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1608.6779 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01443537-graffiti_3.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01443537-graffiti_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01494475-misc_31.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01494475-misc_31.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 108,780B, BPFP=0.2065 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 211,512B, BPFP=0.4015 +⌛️ [2/4] FRONTEND: Frontend time: 0.595s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.024s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09558801 13.19734629 + layer.39.0 281.54433916 4276.19776482 + ------------------------------------------------------------------------------------- + TOTAL 140.81996359 2144.69755556 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 320292 +BPFP 0.3040 bits/point +EBPFP 0.3040 equivalent bits/point +MSE 2144.697556 +---------------------- -------------------------------------------------------- +Time: 1.689s Load: 0.070s, Pack+Encode: 0.595s, Decode+Unpack: 1.024s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2144.6976 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01494475-misc_31.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01494475-misc_31.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01531178-cartoon_22.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01531178-cartoon_22.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 118,408B, BPFP=0.2247 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 183,028B, BPFP=0.3474 +⌛️ [2/4] FRONTEND: Frontend time: 0.573s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.001s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10319715 13.21300736 + layer.39.0 12.97479918 2758.62172012 + ------------------------------------------------------------------------------------- + TOTAL 6.53899817 1385.91736374 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 301436 +BPFP 0.2861 bits/point +EBPFP 0.2861 equivalent bits/point +MSE 1385.917364 +---------------------- -------------------------------------------------------- +Time: 1.645s Load: 0.071s, Pack+Encode: 0.573s, Decode+Unpack: 1.001s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1385.9174 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01531178-cartoon_22.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01531178-cartoon_22.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01534433-painting_9.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01534433-painting_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 121,576B, BPFP=0.2308 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 169,084B, BPFP=0.3209 +⌛️ [2/4] FRONTEND: Frontend time: 0.570s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.047s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10660143 13.01230241 + layer.39.0 8.42910859 2851.46185617 + ------------------------------------------------------------------------------------- + TOTAL 4.26785501 1432.23707929 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 290660 +BPFP 0.2758 bits/point +EBPFP 0.2758 equivalent bits/point +MSE 1432.237079 +---------------------- -------------------------------------------------------- +Time: 1.669s Load: 0.051s, Pack+Encode: 0.570s, Decode+Unpack: 1.047s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1432.2371 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01534433-painting_9.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01534433-painting_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01632777-toy_21.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01632777-toy_21.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 110,500B, BPFP=0.2097 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 243,544B, BPFP=0.4623 +⌛️ [2/4] FRONTEND: Frontend time: 0.582s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.015s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09516629 12.94423439 + layer.39.0 31.73491595 3875.24198251 + ------------------------------------------------------------------------------------- + TOTAL 15.91504112 1944.09310845 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 354044 +BPFP 0.3360 bits/point +EBPFP 0.3360 equivalent bits/point +MSE 1944.093108 +---------------------- -------------------------------------------------------- +Time: 1.668s Load: 0.071s, Pack+Encode: 0.582s, Decode+Unpack: 1.015s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1944.0931 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01632777-toy_21.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01632777-toy_21.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01748264-misc_18.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01748264-misc_18.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 142,244B, BPFP=0.2700 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 195,852B, BPFP=0.3717 +⌛️ [2/4] FRONTEND: Frontend time: 0.575s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.012s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.16139180 37.46355306 + layer.39.0 362.83485180 4277.47424684 + ------------------------------------------------------------------------------------- + TOTAL 181.49812180 2157.46889995 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 338096 +BPFP 0.3209 bits/point +EBPFP 0.3209 equivalent bits/point +MSE 2157.468900 +---------------------- -------------------------------------------------------- +Time: 1.639s Load: 0.052s, Pack+Encode: 0.575s, Decode+Unpack: 1.012s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2157.4689 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01748264-misc_18.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01748264-misc_18.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01784675-painting_2.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01784675-painting_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 130,076B, BPFP=0.2469 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 202,848B, BPFP=0.3850 +⌛️ [2/4] FRONTEND: Frontend time: 0.525s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.993s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13866578 37.35028319 + layer.39.0 232.10166120 3711.99392614 + ------------------------------------------------------------------------------------- + TOTAL 116.12016349 1874.67210467 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 332924 +BPFP 0.3160 bits/point +EBPFP 0.3160 equivalent bits/point +MSE 1874.672105 +---------------------- -------------------------------------------------------- +Time: 1.568s Load: 0.050s, Pack+Encode: 0.525s, Decode+Unpack: 0.993s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1874.6721 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01784675-painting_2.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01784675-painting_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01820546-painting_29.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01820546-painting_29.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 128,424B, BPFP=0.2438 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 211,808B, BPFP=0.4020 +⌛️ [2/4] FRONTEND: Frontend time: 0.576s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.057s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10398871 13.56975921 + layer.39.0 202.99580904 3758.07434402 + ------------------------------------------------------------------------------------- + TOTAL 101.54989888 1885.82205162 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 340232 +BPFP 0.3229 bits/point +EBPFP 0.3229 equivalent bits/point +MSE 1885.822052 +---------------------- -------------------------------------------------------- +Time: 1.702s Load: 0.070s, Pack+Encode: 0.576s, Decode+Unpack: 1.057s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1885.8221 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01820546-painting_29.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01820546-painting_29.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01833805-painting_23.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01833805-painting_23.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 116,184B, BPFP=0.2205 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 187,464B, BPFP=0.3558 +⌛️ [2/4] FRONTEND: Frontend time: 0.571s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.024s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09675035 12.98180215 + layer.39.0 56.43029868 3238.21282799 + ------------------------------------------------------------------------------------- + TOTAL 28.26352451 1625.59731507 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 303648 +BPFP 0.2882 bits/point +EBPFP 0.2882 equivalent bits/point +MSE 1625.597315 +---------------------- -------------------------------------------------------- +Time: 1.645s Load: 0.051s, Pack+Encode: 0.571s, Decode+Unpack: 1.024s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1625.5973 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01833805-painting_23.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01833805-painting_23.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01860187-painting_2.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01860187-painting_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 113,932B, BPFP=0.2163 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 188,484B, BPFP=0.3578 +⌛️ [2/4] FRONTEND: Frontend time: 0.585s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.013s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09532418 12.99916010 + layer.39.0 11.39113179 2991.29178814 + ------------------------------------------------------------------------------------- + TOTAL 5.74322799 1502.14547412 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 302416 +BPFP 0.2870 bits/point +EBPFP 0.2870 equivalent bits/point +MSE 1502.145474 +---------------------- -------------------------------------------------------- +Time: 1.650s Load: 0.051s, Pack+Encode: 0.585s, Decode+Unpack: 1.013s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1502.1455 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01860187-painting_2.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01860187-painting_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01944390-deviantart_6.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01944390-deviantart_6.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 110,964B, BPFP=0.2106 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 179,840B, BPFP=0.3414 +⌛️ [2/4] FRONTEND: Frontend time: 0.585s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.054s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10713051 13.13988665 + layer.39.0 82.30322218 3423.16302235 + ------------------------------------------------------------------------------------- + TOTAL 41.20517635 1718.15145450 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 290804 +BPFP 0.2760 bits/point +EBPFP 0.2760 equivalent bits/point +MSE 1718.151454 +---------------------- -------------------------------------------------------- +Time: 1.691s Load: 0.052s, Pack+Encode: 0.585s, Decode+Unpack: 1.054s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1718.1515 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01944390-deviantart_6.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01944390-deviantart_6.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01983481-misc_6.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01983481-misc_6.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 123,192B, BPFP=0.2338 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 198,692B, BPFP=0.3771 +⌛️ [2/4] FRONTEND: Frontend time: 0.584s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.063s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10315659 14.18241789 + layer.39.0 236.29731535 3653.24441205 + ------------------------------------------------------------------------------------- + TOTAL 118.20023597 1833.71341497 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 321884 +BPFP 0.3055 bits/point +EBPFP 0.3055 equivalent bits/point +MSE 1833.713415 +---------------------- -------------------------------------------------------- +Time: 1.718s Load: 0.071s, Pack+Encode: 0.584s, Decode+Unpack: 1.063s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1833.7134 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01983481-misc_6.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01983481-misc_6.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02051845-cartoon_2.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02051845-cartoon_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 116,272B, BPFP=0.2207 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 198,576B, BPFP=0.3769 +⌛️ [2/4] FRONTEND: Frontend time: 0.580s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.015s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11657756 13.06785638 + layer.39.0 123.57765428 3517.95262391 + ------------------------------------------------------------------------------------- + TOTAL 61.84711592 1765.51024015 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 314848 +BPFP 0.2988 bits/point +EBPFP 0.2988 equivalent bits/point +MSE 1765.510240 +---------------------- -------------------------------------------------------- +Time: 1.664s Load: 0.070s, Pack+Encode: 0.580s, Decode+Unpack: 1.015s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1765.5102 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02051845-cartoon_2.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02051845-cartoon_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02056570-art_2.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02056570-art_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 116,732B, BPFP=0.2216 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 193,732B, BPFP=0.3677 +⌛️ [2/4] FRONTEND: Frontend time: 0.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.062s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09569211 13.09654682 + layer.39.0 33.39981930 3293.25777454 + ------------------------------------------------------------------------------------- + TOTAL 16.74775571 1653.17716068 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 310464 +BPFP 0.2946 bits/point +EBPFP 0.2946 equivalent bits/point +MSE 1653.177161 +---------------------- -------------------------------------------------------- +Time: 1.694s Load: 0.051s, Pack+Encode: 0.581s, Decode+Unpack: 1.062s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1653.1772 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02056570-art_2.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02056570-art_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02085620-misc_90.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02085620-misc_90.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 114,236B, BPFP=0.2168 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 264,660B, BPFP=0.5023 +⌛️ [2/4] FRONTEND: Frontend time: 0.579s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.045s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09843166 13.01162574 + layer.39.0 72.76188958 3955.83114674 + ------------------------------------------------------------------------------------- + TOTAL 36.43016062 1984.42138624 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 378896 +BPFP 0.3596 bits/point +EBPFP 0.3596 equivalent bits/point +MSE 1984.421386 +---------------------- -------------------------------------------------------- +Time: 1.695s Load: 0.071s, Pack+Encode: 0.579s, Decode+Unpack: 1.045s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1984.4214 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02085620-misc_90.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02085620-misc_90.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088094-misc_39.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088094-misc_39.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 119,616B, BPFP=0.2270 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 201,360B, BPFP=0.3822 +⌛️ [2/4] FRONTEND: Frontend time: 0.579s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.055s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09820385 25.83077662 + layer.39.0 12.32374423 3011.95238095 + ------------------------------------------------------------------------------------- + TOTAL 6.21097404 1518.89157879 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 320976 +BPFP 0.3046 bits/point +EBPFP 0.3046 equivalent bits/point +MSE 1518.891579 +---------------------- -------------------------------------------------------- +Time: 1.704s Load: 0.070s, Pack+Encode: 0.579s, Decode+Unpack: 1.055s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1518.8916 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088094-misc_39.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02088094-misc_39.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088466-sketch_11.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088466-sketch_11.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 109,640B, BPFP=0.2081 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 211,456B, BPFP=0.4014 +⌛️ [2/4] FRONTEND: Frontend time: 0.576s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.043s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09459993 13.05101661 + layer.39.0 16.33682960 3631.50097182 + ------------------------------------------------------------------------------------- + TOTAL 8.21571477 1822.27599421 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 321096 +BPFP 0.3047 bits/point +EBPFP 0.3047 equivalent bits/point +MSE 1822.275994 +---------------------- -------------------------------------------------------- +Time: 1.670s Load: 0.050s, Pack+Encode: 0.576s, Decode+Unpack: 1.043s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1822.2760 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088466-sketch_11.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02088466-sketch_11.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02094433-misc_20.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02094433-misc_20.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 112,908B, BPFP=0.2143 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 211,784B, BPFP=0.4020 +⌛️ [2/4] FRONTEND: Frontend time: 0.570s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.063s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09538842 13.01209267 + layer.39.0 94.83275632 3789.89285714 + ------------------------------------------------------------------------------------- + TOTAL 47.46407237 1901.45247491 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 324692 +BPFP 0.3081 bits/point +EBPFP 0.3081 equivalent bits/point +MSE 1901.452475 +---------------------- -------------------------------------------------------- +Time: 1.685s Load: 0.052s, Pack+Encode: 0.570s, Decode+Unpack: 1.063s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1901.4525 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02094433-misc_20.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02094433-misc_20.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02097298-misc_9.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02097298-misc_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 139,252B, BPFP=0.2643 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 230,872B, BPFP=0.4382 +⌛️ [2/4] FRONTEND: Frontend time: 0.600s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.046s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11199322 73.98385872 + layer.39.0 26.16675018 3725.16302235 + ------------------------------------------------------------------------------------- + TOTAL 13.13937170 1899.57344054 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 370124 +BPFP 0.3513 bits/point +EBPFP 0.3513 equivalent bits/point +MSE 1899.573441 +---------------------- -------------------------------------------------------- +Time: 1.717s Load: 0.070s, Pack+Encode: 0.600s, Decode+Unpack: 1.046s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1899.5734 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02097298-misc_9.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02097298-misc_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02106662-misc_55.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02106662-misc_55.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 119,004B, BPFP=0.2259 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 218,900B, BPFP=0.4155 +⌛️ [2/4] FRONTEND: Frontend time: 0.585s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.050s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09642073 12.92201071 + layer.39.0 14.86428154 3618.36637512 + ------------------------------------------------------------------------------------- + TOTAL 7.48035113 1815.64419292 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 337904 +BPFP 0.3207 bits/point +EBPFP 0.3207 equivalent bits/point +MSE 1815.644193 +---------------------- -------------------------------------------------------- +Time: 1.705s Load: 0.070s, Pack+Encode: 0.585s, Decode+Unpack: 1.050s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1815.6442 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02106662-misc_55.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02106662-misc_55.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02109525-sketch_23.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02109525-sketch_23.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 111,072B, BPFP=0.2108 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 240,640B, BPFP=0.4568 +⌛️ [2/4] FRONTEND: Frontend time: 0.578s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.018s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09568003 13.05655331 + layer.39.0 14.01675815 3410.48785228 + ------------------------------------------------------------------------------------- + TOTAL 7.05621909 1711.77220280 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 351712 +BPFP 0.3338 bits/point +EBPFP 0.3338 equivalent bits/point +MSE 1711.772203 +---------------------- -------------------------------------------------------- +Time: 1.647s Load: 0.051s, Pack+Encode: 0.578s, Decode+Unpack: 1.018s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1711.7722 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02109525-sketch_23.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02109525-sketch_23.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110185-painting_33.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110185-painting_33.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 119,012B, BPFP=0.2259 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 237,224B, BPFP=0.4503 +⌛️ [2/4] FRONTEND: Frontend time: 0.573s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.022s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09599521 12.95421640 + layer.39.0 22.05506522 3818.98493683 + ------------------------------------------------------------------------------------- + TOTAL 11.07553021 1915.96957661 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 356236 +BPFP 0.3381 bits/point +EBPFP 0.3381 equivalent bits/point +MSE 1915.969577 +---------------------- -------------------------------------------------------- +Time: 1.646s Load: 0.051s, Pack+Encode: 0.573s, Decode+Unpack: 1.022s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1915.9696 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110185-painting_33.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02110185-painting_33.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110341-misc_162.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110341-misc_162.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 111,784B, BPFP=0.2122 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 175,496B, BPFP=0.3331 +⌛️ [2/4] FRONTEND: Frontend time: 0.579s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.020s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11124049 13.29927702 + layer.39.0 14.33747210 2883.13848397 + ------------------------------------------------------------------------------------- + TOTAL 7.22435629 1448.21888049 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 287280 +BPFP 0.2726 bits/point +EBPFP 0.2726 equivalent bits/point +MSE 1448.218880 +---------------------- -------------------------------------------------------- +Time: 1.651s Load: 0.052s, Pack+Encode: 0.579s, Decode+Unpack: 1.020s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1448.2189 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110341-misc_162.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02110341-misc_162.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02165456-tattoo_37.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02165456-tattoo_37.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 117,232B, BPFP=0.2225 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 205,328B, BPFP=0.3897 +⌛️ [2/4] FRONTEND: Frontend time: 0.575s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.011s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09780899 12.86094756 + layer.39.0 88.96013271 3609.91496599 + ------------------------------------------------------------------------------------- + TOTAL 44.52897085 1811.38795677 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 322560 +BPFP 0.3061 bits/point +EBPFP 0.3061 equivalent bits/point +MSE 1811.387957 +---------------------- -------------------------------------------------------- +Time: 1.638s Load: 0.052s, Pack+Encode: 0.575s, Decode+Unpack: 1.011s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1811.3880 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02165456-tattoo_37.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02165456-tattoo_37.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02219486-misc_3.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02219486-misc_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 101,744B, BPFP=0.1931 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 165,936B, BPFP=0.3150 +⌛️ [2/4] FRONTEND: Frontend time: 0.568s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.012s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10021695 13.22939159 + layer.39.0 75.73793580 2690.68658892 + ------------------------------------------------------------------------------------- + TOTAL 37.91907638 1351.95799026 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 267680 +BPFP 0.2540 bits/point +EBPFP 0.2540 equivalent bits/point +MSE 1351.957990 +---------------------- -------------------------------------------------------- +Time: 1.631s Load: 0.051s, Pack+Encode: 0.568s, Decode+Unpack: 1.012s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1351.9580 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02219486-misc_3.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02219486-misc_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02226429-tattoo_0.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02226429-tattoo_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 113,992B, BPFP=0.2164 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 196,876B, BPFP=0.3737 +⌛️ [2/4] FRONTEND: Frontend time: 0.579s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.035s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09506506 12.91824682 + layer.39.0 201.13660107 3283.20578231 + ------------------------------------------------------------------------------------- + TOTAL 100.61583306 1648.06201457 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 310868 +BPFP 0.2950 bits/point +EBPFP 0.2950 equivalent bits/point +MSE 1648.062015 +---------------------- -------------------------------------------------------- +Time: 1.674s Load: 0.060s, Pack+Encode: 0.579s, Decode+Unpack: 1.035s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1648.0620 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02226429-tattoo_0.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02226429-tattoo_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02233338-tattoo_5.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02233338-tattoo_5.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 109,980B, BPFP=0.2088 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 245,352B, BPFP=0.4657 +⌛️ [2/4] FRONTEND: Frontend time: 0.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.045s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09502332 12.99843029 + layer.39.0 172.43500972 4151.47862002 + ------------------------------------------------------------------------------------- + TOTAL 86.26501652 2082.23852515 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 355332 +BPFP 0.3372 bits/point +EBPFP 0.3372 equivalent bits/point +MSE 2082.238525 +---------------------- -------------------------------------------------------- +Time: 1.676s Load: 0.051s, Pack+Encode: 0.581s, Decode+Unpack: 1.045s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2082.2385 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02233338-tattoo_5.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02233338-tattoo_5.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02279972-painting_2.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02279972-painting_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 134,388B, BPFP=0.2551 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 229,524B, BPFP=0.4357 +⌛️ [2/4] FRONTEND: Frontend time: 0.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.068s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11337867 12.90402830 + layer.39.0 361.17623299 3900.04713314 + ------------------------------------------------------------------------------------- + TOTAL 180.64480583 1956.47558072 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 363912 +BPFP 0.3454 bits/point +EBPFP 0.3454 equivalent bits/point +MSE 1956.475581 +---------------------- -------------------------------------------------------- +Time: 1.701s Load: 0.052s, Pack+Encode: 0.581s, Decode+Unpack: 1.068s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1956.4756 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02279972-painting_2.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02279972-painting_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02317335-sculpture_0.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02317335-sculpture_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 111,668B, BPFP=0.2120 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 254,200B, BPFP=0.4825 +⌛️ [2/4] FRONTEND: Frontend time: 0.566s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.022s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09546056 13.03875881 + layer.39.0 1163.18707483 4448.25655977 + ------------------------------------------------------------------------------------- + TOTAL 581.64126769 2230.64765929 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 365868 +BPFP 0.3472 bits/point +EBPFP 0.3472 equivalent bits/point +MSE 2230.647659 +---------------------- -------------------------------------------------------- +Time: 1.648s Load: 0.060s, Pack+Encode: 0.566s, Decode+Unpack: 1.022s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2230.6477 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02317335-sculpture_0.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02317335-sculpture_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02346627-painting_9.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02346627-painting_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 131,048B, BPFP=0.2487 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 222,528B, BPFP=0.4224 +⌛️ [2/4] FRONTEND: Frontend time: 0.585s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.062s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13205896 37.67167380 + layer.39.0 503.01482021 3839.75655977 + ------------------------------------------------------------------------------------- + TOTAL 251.57343959 1938.71411679 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 353576 +BPFP 0.3356 bits/point +EBPFP 0.3356 equivalent bits/point +MSE 1938.714117 +---------------------- -------------------------------------------------------- +Time: 1.717s Load: 0.070s, Pack+Encode: 0.585s, Decode+Unpack: 1.062s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1938.7141 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02346627-painting_9.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02346627-painting_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02391049-deviantart_0.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02391049-deviantart_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 108,096B, BPFP=0.2052 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 187,860B, BPFP=0.3566 +⌛️ [2/4] FRONTEND: Frontend time: 0.547s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.991s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10116939 13.19803814 + layer.39.0 17.42674737 3027.75728863 + ------------------------------------------------------------------------------------- + TOTAL 8.76395838 1520.47766339 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 295956 +BPFP 0.2809 bits/point +EBPFP 0.2809 equivalent bits/point +MSE 1520.477663 +---------------------- -------------------------------------------------------- +Time: 1.600s Load: 0.061s, Pack+Encode: 0.547s, Decode+Unpack: 0.991s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1520.4777 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02391049-deviantart_0.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02391049-deviantart_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02395406-sculpture_31.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02395406-sculpture_31.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 134,300B, BPFP=0.2549 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 178,928B, BPFP=0.3396 +⌛️ [2/4] FRONTEND: Frontend time: 0.582s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.052s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11469608 61.54458212 + layer.39.0 30.55020044 2882.32021380 + ------------------------------------------------------------------------------------- + TOTAL 15.33244826 1471.93239796 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 313228 +BPFP 0.2973 bits/point +EBPFP 0.2973 equivalent bits/point +MSE 1471.932398 +---------------------- -------------------------------------------------------- +Time: 1.686s Load: 0.052s, Pack+Encode: 0.582s, Decode+Unpack: 1.052s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1471.9324 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02395406-sculpture_31.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02395406-sculpture_31.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02445715-art_3.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02445715-art_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 111,484B, BPFP=0.2116 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 217,260B, BPFP=0.4124 +⌛️ [2/4] FRONTEND: Frontend time: 0.584s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.046s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09587883 13.19574431 + layer.39.0 77.63827138 3495.28644315 + ------------------------------------------------------------------------------------- + TOTAL 38.86707511 1754.24109373 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 328744 +BPFP 0.3120 bits/point +EBPFP 0.3120 equivalent bits/point +MSE 1754.241094 +---------------------- -------------------------------------------------------- +Time: 1.701s Load: 0.070s, Pack+Encode: 0.584s, Decode+Unpack: 1.046s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1754.2411 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02445715-art_3.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02445715-art_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02672831-sculpture_2.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02672831-sculpture_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 124,032B, BPFP=0.2354 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 237,128B, BPFP=0.4501 +⌛️ [2/4] FRONTEND: Frontend time: 0.588s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.036s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11638676 13.46965538 + layer.39.0 42.74346681 4539.56268222 + ------------------------------------------------------------------------------------- + TOTAL 21.42992678 2276.51616880 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 361160 +BPFP 0.3428 bits/point +EBPFP 0.3428 equivalent bits/point +MSE 2276.516169 +---------------------- -------------------------------------------------------- +Time: 1.693s Load: 0.070s, Pack+Encode: 0.588s, Decode+Unpack: 1.036s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2276.5162 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02672831-sculpture_2.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02672831-sculpture_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02701002-videogame_1.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02701002-videogame_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 116,752B, BPFP=0.2216 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 234,220B, BPFP=0.4446 +⌛️ [2/4] FRONTEND: Frontend time: 0.575s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.003s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10320827 12.88827233 + layer.39.0 160.61054422 4009.61758989 + ------------------------------------------------------------------------------------- + TOTAL 80.35687624 2011.25293111 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 350972 +BPFP 0.3331 bits/point +EBPFP 0.3331 equivalent bits/point +MSE 2011.252931 +---------------------- -------------------------------------------------------- +Time: 1.629s Load: 0.051s, Pack+Encode: 0.575s, Decode+Unpack: 1.003s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2011.2529 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02701002-videogame_1.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02701002-videogame_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02749479-misc_35.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02749479-misc_35.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 108,464B, BPFP=0.2059 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 204,780B, BPFP=0.3887 +⌛️ [2/4] FRONTEND: Frontend time: 0.577s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.004s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09764870 13.11342171 + layer.39.0 172.65676628 3557.86030126 + ------------------------------------------------------------------------------------- + TOTAL 86.37720749 1785.48686149 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 313244 +BPFP 0.2973 bits/point +EBPFP 0.2973 equivalent bits/point +MSE 1785.486861 +---------------------- -------------------------------------------------------- +Time: 1.633s Load: 0.052s, Pack+Encode: 0.577s, Decode+Unpack: 1.004s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1785.4869 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02749479-misc_35.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02749479-misc_35.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02769748-cartoon_11.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02769748-cartoon_11.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 109,800B, BPFP=0.2084 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 212,636B, BPFP=0.4036 +⌛️ [2/4] FRONTEND: Frontend time: 0.582s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.003s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12263774 13.10209301 + layer.39.0 11.02823964 3414.53182702 + ------------------------------------------------------------------------------------- + TOTAL 5.57543869 1713.81696002 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 322436 +BPFP 0.3060 bits/point +EBPFP 0.3060 equivalent bits/point +MSE 1713.816960 +---------------------- -------------------------------------------------------- +Time: 1.636s Load: 0.052s, Pack+Encode: 0.582s, Decode+Unpack: 1.003s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1713.8170 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02769748-cartoon_11.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02769748-cartoon_11.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02793495-sketch_11.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02793495-sketch_11.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 106,248B, BPFP=0.2017 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 174,816B, BPFP=0.3318 +⌛️ [2/4] FRONTEND: Frontend time: 0.575s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.037s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09793751 13.03748614 + layer.39.0 182.75789602 2884.97060253 + ------------------------------------------------------------------------------------- + TOTAL 91.42791676 1449.00404434 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 281064 +BPFP 0.2667 bits/point +EBPFP 0.2667 equivalent bits/point +MSE 1449.004044 +---------------------- -------------------------------------------------------- +Time: 1.663s Load: 0.052s, Pack+Encode: 0.575s, Decode+Unpack: 1.037s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1449.0040 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02793495-sketch_11.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02793495-sketch_11.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02797295-misc_13.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02797295-misc_13.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 147,480B, BPFP=0.2799 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 217,084B, BPFP=0.4120 +⌛️ [2/4] FRONTEND: Frontend time: 0.589s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.982s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.17140635 62.34885204 + layer.39.0 172.50999150 3893.85787172 + ------------------------------------------------------------------------------------- + TOTAL 86.34069892 1978.10336188 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 364564 +BPFP 0.3460 bits/point +EBPFP 0.3460 equivalent bits/point +MSE 1978.103362 +---------------------- -------------------------------------------------------- +Time: 1.623s Load: 0.052s, Pack+Encode: 0.589s, Decode+Unpack: 0.982s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1978.1034 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02797295-misc_13.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02797295-misc_13.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02802426-art_1.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02802426-art_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.056s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 141,132B, BPFP=0.2679 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 233,840B, BPFP=0.4438 +⌛️ [2/4] FRONTEND: Frontend time: 0.600s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.019s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.16523854 14.02428499 + layer.39.0 477.65184645 4209.60592809 + ------------------------------------------------------------------------------------- + TOTAL 238.90854250 2111.81510654 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 374972 +BPFP 0.3559 bits/point +EBPFP 0.3559 equivalent bits/point +MSE 2111.815107 +---------------------- -------------------------------------------------------- +Time: 1.675s Load: 0.056s, Pack+Encode: 0.600s, Decode+Unpack: 1.019s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2111.8151 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02802426-art_1.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02802426-art_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02814860-sticker_8.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02814860-sticker_8.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 119,984B, BPFP=0.2277 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 196,144B, BPFP=0.3723 +⌛️ [2/4] FRONTEND: Frontend time: 0.584s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.031s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12757226 13.11190135 + layer.39.0 19.27598852 2822.94825073 + ------------------------------------------------------------------------------------- + TOTAL 9.70178039 1418.03007604 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 316128 +BPFP 0.3000 bits/point +EBPFP 0.3000 equivalent bits/point +MSE 1418.030076 +---------------------- -------------------------------------------------------- +Time: 1.685s Load: 0.070s, Pack+Encode: 0.584s, Decode+Unpack: 1.031s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1418.0301 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02814860-sticker_8.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02814860-sticker_8.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02841315-graphic_4.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02841315-graphic_4.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 113,992B, BPFP=0.2164 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 212,436B, BPFP=0.4032 +⌛️ [2/4] FRONTEND: Frontend time: 0.574s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.054s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11826141 13.04467797 + layer.39.0 55.46440340 3735.56681244 + ------------------------------------------------------------------------------------- + TOTAL 27.79133240 1874.30574521 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 326428 +BPFP 0.3098 bits/point +EBPFP 0.3098 equivalent bits/point +MSE 1874.305745 +---------------------- -------------------------------------------------------- +Time: 1.698s Load: 0.071s, Pack+Encode: 0.574s, Decode+Unpack: 1.054s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1874.3057 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02841315-graphic_4.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02841315-graphic_4.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02843684-cartoon_9.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02843684-cartoon_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 129,480B, BPFP=0.2458 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 211,040B, BPFP=0.4006 +⌛️ [2/4] FRONTEND: Frontend time: 0.521s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.973s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12386809 13.06722812 + layer.39.0 312.00962707 3224.08211856 + ------------------------------------------------------------------------------------- + TOTAL 156.06674758 1618.57467334 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 340520 +BPFP 0.3232 bits/point +EBPFP 0.3232 equivalent bits/point +MSE 1618.574673 +---------------------- -------------------------------------------------------- +Time: 1.554s Load: 0.060s, Pack+Encode: 0.521s, Decode+Unpack: 0.973s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1618.5747 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02843684-cartoon_9.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02843684-cartoon_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02883205-sketch_15.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02883205-sketch_15.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 114,332B, BPFP=0.2170 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 210,300B, BPFP=0.3992 +⌛️ [2/4] FRONTEND: Frontend time: 0.516s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.969s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09796664 14.02730199 + layer.39.0 103.64267493 3654.21379981 + ------------------------------------------------------------------------------------- + TOTAL 51.87032078 1834.12055090 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 324632 +BPFP 0.3081 bits/point +EBPFP 0.3081 equivalent bits/point +MSE 1834.120551 +---------------------- -------------------------------------------------------- +Time: 1.546s Load: 0.061s, Pack+Encode: 0.516s, Decode+Unpack: 0.969s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1834.1206 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02883205-sketch_15.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02883205-sketch_15.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02906734-graffiti_3.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02906734-graffiti_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 166,292B, BPFP=0.3156 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 266,404B, BPFP=0.5057 +⌛️ [2/4] FRONTEND: Frontend time: 0.562s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.015s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.17339475 162.20708819 + layer.39.0 166.12656402 3885.73129252 + ------------------------------------------------------------------------------------- + TOTAL 83.14997939 2023.96919035 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 432696 +BPFP 0.4106 bits/point +EBPFP 0.4106 equivalent bits/point +MSE 2023.969190 +---------------------- -------------------------------------------------------- +Time: 1.628s Load: 0.050s, Pack+Encode: 0.562s, Decode+Unpack: 1.015s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2023.9692 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02906734-graffiti_3.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02906734-graffiti_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02909870-sketch_0.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02909870-sketch_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 124,136B, BPFP=0.2356 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 194,336B, BPFP=0.3689 +⌛️ [2/4] FRONTEND: Frontend time: 0.593s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.043s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.15317524 13.42124768 + layer.39.0 167.75886783 3241.19825073 + ------------------------------------------------------------------------------------- + TOTAL 83.95602154 1627.30974921 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 318472 +BPFP 0.3022 bits/point +EBPFP 0.3022 equivalent bits/point +MSE 1627.309749 +---------------------- -------------------------------------------------------- +Time: 1.706s Load: 0.070s, Pack+Encode: 0.593s, Decode+Unpack: 1.043s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1627.3097 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02909870-sketch_0.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02909870-sketch_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02939185-painting_0.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02939185-painting_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 104,540B, BPFP=0.1984 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 206,612B, BPFP=0.3922 +⌛️ [2/4] FRONTEND: Frontend time: 0.577s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.055s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09512242 13.07093792 + layer.39.0 131.28711127 3430.67128280 + ------------------------------------------------------------------------------------- + TOTAL 65.69111684 1721.87111036 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 311152 +BPFP 0.2953 bits/point +EBPFP 0.2953 equivalent bits/point +MSE 1721.871110 +---------------------- -------------------------------------------------------- +Time: 1.702s Load: 0.071s, Pack+Encode: 0.577s, Decode+Unpack: 1.055s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1721.8711 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02939185-painting_0.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02939185-painting_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02948072-misc_10.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02948072-misc_10.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 114,280B, BPFP=0.2169 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 269,856B, BPFP=0.5122 +⌛️ [2/4] FRONTEND: Frontend time: 0.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.021s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09566823 13.15280783 + layer.39.0 102.81622783 3692.05272109 + ------------------------------------------------------------------------------------- + TOTAL 51.45594803 1852.60276446 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 384136 +BPFP 0.3646 bits/point +EBPFP 0.3646 equivalent bits/point +MSE 1852.602764 +---------------------- -------------------------------------------------------- +Time: 1.652s Load: 0.050s, Pack+Encode: 0.581s, Decode+Unpack: 1.021s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1852.6028 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02948072-misc_10.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02948072-misc_10.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02950826-art_1.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02950826-art_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 112,168B, BPFP=0.2129 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 261,692B, BPFP=0.4967 +⌛️ [2/4] FRONTEND: Frontend time: 0.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.002s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09506074 13.08993865 + layer.39.0 1071.96149174 4803.88192420 + ------------------------------------------------------------------------------------- + TOTAL 536.02827624 2408.48593143 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 373860 +BPFP 0.3548 bits/point +EBPFP 0.3548 equivalent bits/point +MSE 2408.485931 +---------------------- -------------------------------------------------------- +Time: 1.634s Load: 0.051s, Pack+Encode: 0.581s, Decode+Unpack: 1.002s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2408.4859 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02950826-art_1.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02950826-art_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02951358-misc_1.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02951358-misc_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 104,556B, BPFP=0.1985 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 163,940B, BPFP=0.3112 +⌛️ [2/4] FRONTEND: Frontend time: 0.579s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.051s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09568294 13.14563309 + layer.39.0 598.97078474 3227.12269193 + ------------------------------------------------------------------------------------- + TOTAL 299.53323384 1620.13416251 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 268496 +BPFP 0.2548 bits/point +EBPFP 0.2548 equivalent bits/point +MSE 1620.134163 +---------------------- -------------------------------------------------------- +Time: 1.700s Load: 0.069s, Pack+Encode: 0.579s, Decode+Unpack: 1.051s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1620.1342 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02951358-misc_1.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02951358-misc_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02966193-cartoon_22.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02966193-cartoon_22.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 138,164B, BPFP=0.2622 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 210,244B, BPFP=0.3991 +⌛️ [2/4] FRONTEND: Frontend time: 0.579s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.034s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10376222 13.71202985 + layer.39.0 767.85532070 4190.96598639 + ------------------------------------------------------------------------------------- + TOTAL 383.97954146 2102.33900812 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 348408 +BPFP 0.3307 bits/point +EBPFP 0.3307 equivalent bits/point +MSE 2102.339008 +---------------------- -------------------------------------------------------- +Time: 1.683s Load: 0.071s, Pack+Encode: 0.579s, Decode+Unpack: 1.034s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2102.3390 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02966193-cartoon_22.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02966193-cartoon_22.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02980441-graphic_3.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02980441-graphic_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 106,316B, BPFP=0.2018 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 155,752B, BPFP=0.2956 +⌛️ [2/4] FRONTEND: Frontend time: 0.580s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.046s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09509088 13.09654303 + layer.39.0 13.13791359 2343.41763848 + ------------------------------------------------------------------------------------- + TOTAL 6.61650224 1178.25709075 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 262068 +BPFP 0.2487 bits/point +EBPFP 0.2487 equivalent bits/point +MSE 1178.257091 +---------------------- -------------------------------------------------------- +Time: 1.697s Load: 0.071s, Pack+Encode: 0.580s, Decode+Unpack: 1.046s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1178.2571 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02980441-graphic_3.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02980441-graphic_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03124170-painting_15.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03124170-painting_15.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 120,200B, BPFP=0.2281 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 273,628B, BPFP=0.5194 +⌛️ [2/4] FRONTEND: Frontend time: 0.579s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.020s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10783903 13.10289400 + layer.39.0 326.57091229 5589.99173955 + ------------------------------------------------------------------------------------- + TOTAL 163.33937566 2801.54731678 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 393828 +BPFP 0.3738 bits/point +EBPFP 0.3738 equivalent bits/point +MSE 2801.547317 +---------------------- -------------------------------------------------------- +Time: 1.669s Load: 0.070s, Pack+Encode: 0.579s, Decode+Unpack: 1.020s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2801.5473 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03124170-painting_15.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03124170-painting_15.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03345487-toy_17.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03345487-toy_17.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 115,396B, BPFP=0.2190 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 191,996B, BPFP=0.3644 +⌛️ [2/4] FRONTEND: Frontend time: 0.578s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.009s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10662318 13.02634726 + layer.39.0 198.63900024 3672.59936832 + ------------------------------------------------------------------------------------- + TOTAL 99.37281171 1842.81285779 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 307392 +BPFP 0.2917 bits/point +EBPFP 0.2917 equivalent bits/point +MSE 1842.812858 +---------------------- -------------------------------------------------------- +Time: 1.658s Load: 0.070s, Pack+Encode: 0.578s, Decode+Unpack: 1.009s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1842.8129 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03345487-toy_17.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03345487-toy_17.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03372029-sketch_14.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03372029-sketch_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 123,604B, BPFP=0.2346 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 221,256B, BPFP=0.4200 +⌛️ [2/4] FRONTEND: Frontend time: 0.584s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.004s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12162214 12.89756723 + layer.39.0 228.06095117 3948.72133139 + ------------------------------------------------------------------------------------- + TOTAL 114.09128665 1980.80944931 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 344860 +BPFP 0.3273 bits/point +EBPFP 0.3273 equivalent bits/point +MSE 1980.809449 +---------------------- -------------------------------------------------------- +Time: 1.659s Load: 0.070s, Pack+Encode: 0.584s, Decode+Unpack: 1.004s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1980.8094 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03372029-sketch_14.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03372029-sketch_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03424325-misc_4.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03424325-misc_4.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 121,912B, BPFP=0.2314 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 275,948B, BPFP=0.5238 +⌛️ [2/4] FRONTEND: Frontend time: 0.590s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.033s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10761499 13.03942503 + layer.39.0 21.03287666 3579.84888241 + ------------------------------------------------------------------------------------- + TOTAL 10.57024582 1796.44415372 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 397860 +BPFP 0.3776 bits/point +EBPFP 0.3776 equivalent bits/point +MSE 1796.444154 +---------------------- -------------------------------------------------------- +Time: 1.693s Load: 0.070s, Pack+Encode: 0.590s, Decode+Unpack: 1.033s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1796.4442 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03424325-misc_4.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03424325-misc_4.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03467068-sketch_17.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03467068-sketch_17.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 107,240B, BPFP=0.2036 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 233,076B, BPFP=0.4424 +⌛️ [2/4] FRONTEND: Frontend time: 0.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.015s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09564773 13.03282256 + layer.39.0 208.14688107 3486.28571429 + ------------------------------------------------------------------------------------- + TOTAL 104.12126440 1749.65926842 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 340316 +BPFP 0.3230 bits/point +EBPFP 0.3230 equivalent bits/point +MSE 1749.659268 +---------------------- -------------------------------------------------------- +Time: 1.666s Load: 0.070s, Pack+Encode: 0.581s, Decode+Unpack: 1.015s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1749.6593 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03467068-sketch_17.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03467068-sketch_17.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03481172-sketch_9.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03481172-sketch_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 115,072B, BPFP=0.2184 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 212,508B, BPFP=0.4034 +⌛️ [2/4] FRONTEND: Frontend time: 0.589s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.007s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14641065 13.03931969 + layer.39.0 516.28267736 3705.21768707 + ------------------------------------------------------------------------------------- + TOTAL 258.21454400 1859.12850338 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 327580 +BPFP 0.3109 bits/point +EBPFP 0.3109 equivalent bits/point +MSE 1859.128503 +---------------------- -------------------------------------------------------- +Time: 1.665s Load: 0.070s, Pack+Encode: 0.589s, Decode+Unpack: 1.007s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1859.1285 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03481172-sketch_9.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03481172-sketch_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03494278-deviantart_3.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03494278-deviantart_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 105,896B, BPFP=0.2010 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 205,560B, BPFP=0.3902 +⌛️ [2/4] FRONTEND: Frontend time: 0.591s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.981s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09714438 13.15777036 + layer.39.0 11.38600982 3237.23979592 + ------------------------------------------------------------------------------------- + TOTAL 5.74157710 1625.19878314 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 311456 +BPFP 0.2956 bits/point +EBPFP 0.2956 equivalent bits/point +MSE 1625.198783 +---------------------- -------------------------------------------------------- +Time: 1.642s Load: 0.070s, Pack+Encode: 0.591s, Decode+Unpack: 0.981s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1625.1988 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03494278-deviantart_3.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03494278-deviantart_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03495258-painting_3.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03495258-painting_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 120,992B, BPFP=0.2297 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 226,104B, BPFP=0.4292 +⌛️ [2/4] FRONTEND: Frontend time: 0.514s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.980s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10398556 13.03429547 + layer.39.0 359.17207240 3592.39212828 + ------------------------------------------------------------------------------------- + TOTAL 179.63802898 1802.71321188 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 347096 +BPFP 0.3294 bits/point +EBPFP 0.3294 equivalent bits/point +MSE 1802.713212 +---------------------- -------------------------------------------------------- +Time: 1.544s Load: 0.050s, Pack+Encode: 0.514s, Decode+Unpack: 0.980s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1802.7132 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03495258-painting_3.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03495258-painting_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03498962-sketch_8.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03498962-sketch_8.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 144,008B, BPFP=0.2733 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 205,316B, BPFP=0.3897 +⌛️ [2/4] FRONTEND: Frontend time: 0.574s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.035s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.16074808 39.69427994 + layer.39.0 476.99061589 3552.93488824 + ------------------------------------------------------------------------------------- + TOTAL 238.57568198 1796.31458409 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 349324 +BPFP 0.3315 bits/point +EBPFP 0.3315 equivalent bits/point +MSE 1796.314584 +---------------------- -------------------------------------------------------- +Time: 1.680s Load: 0.071s, Pack+Encode: 0.574s, Decode+Unpack: 1.035s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1796.3146 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03498962-sketch_8.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03498962-sketch_8.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03602883-misc_12.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03602883-misc_12.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 96,052B, BPFP=0.1823 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 181,036B, BPFP=0.3436 +⌛️ [2/4] FRONTEND: Frontend time: 0.595s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.041s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.09080038 13.35898684 + layer.39.0 100.93773536 2828.81171040 + ------------------------------------------------------------------------------------- + TOTAL 54.51426787 1421.08534862 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 277088 +BPFP 0.2630 bits/point +EBPFP 0.2630 equivalent bits/point +MSE 1421.085349 +---------------------- -------------------------------------------------------- +Time: 1.706s Load: 0.070s, Pack+Encode: 0.595s, Decode+Unpack: 1.041s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1421.0853 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03602883-misc_12.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03602883-misc_12.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03630383-toy_1.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03630383-toy_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 107,124B, BPFP=0.2033 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 195,492B, BPFP=0.3711 +⌛️ [2/4] FRONTEND: Frontend time: 0.573s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.073s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09574974 13.25990134 + layer.39.0 14.66923857 3163.51020408 + ------------------------------------------------------------------------------------- + TOTAL 7.38249415 1588.38505271 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 302616 +BPFP 0.2872 bits/point +EBPFP 0.2872 equivalent bits/point +MSE 1588.385053 +---------------------- -------------------------------------------------------- +Time: 1.717s Load: 0.071s, Pack+Encode: 0.573s, Decode+Unpack: 1.073s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1588.3851 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03630383-toy_1.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03630383-toy_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03649909-toy_22.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03649909-toy_22.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 101,684B, BPFP=0.1930 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 148,464B, BPFP=0.2818 +⌛️ [2/4] FRONTEND: Frontend time: 0.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.025s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09878858 13.03825297 + layer.39.0 29.68475348 2710.05733722 + ------------------------------------------------------------------------------------- + TOTAL 14.89177103 1361.54779509 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 250148 +BPFP 0.2374 bits/point +EBPFP 0.2374 equivalent bits/point +MSE 1361.547795 +---------------------- -------------------------------------------------------- +Time: 1.676s Load: 0.070s, Pack+Encode: 0.581s, Decode+Unpack: 1.025s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1361.5478 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03649909-toy_22.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03649909-toy_22.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03676483-sculpture_3.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03676483-sculpture_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 106,936B, BPFP=0.2030 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 274,664B, BPFP=0.5213 +⌛️ [2/4] FRONTEND: Frontend time: 0.594s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.007s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09491264 13.10583413 + layer.39.0 32.22669916 4161.35762877 + ------------------------------------------------------------------------------------- + TOTAL 16.16080590 2087.23173145 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 381600 +BPFP 0.3622 bits/point +EBPFP 0.3622 equivalent bits/point +MSE 2087.231731 +---------------------- -------------------------------------------------------- +Time: 1.671s Load: 0.070s, Pack+Encode: 0.594s, Decode+Unpack: 1.007s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2087.2317 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03676483-sculpture_3.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03676483-sculpture_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03710193-sketch_14.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03710193-sketch_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 118,484B, BPFP=0.2249 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 210,952B, BPFP=0.4004 +⌛️ [2/4] FRONTEND: Frontend time: 0.590s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.007s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.47394152 25.58349087 + layer.39.0 335.99814747 3584.35617104 + ------------------------------------------------------------------------------------- + TOTAL 168.23604450 1804.96983096 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 329436 +BPFP 0.3126 bits/point +EBPFP 0.3126 equivalent bits/point +MSE 1804.969831 +---------------------- -------------------------------------------------------- +Time: 1.666s Load: 0.070s, Pack+Encode: 0.590s, Decode+Unpack: 1.007s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1804.9698 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03710193-sketch_14.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03710193-sketch_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03773504-graphic_4.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03773504-graphic_4.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 105,744B, BPFP=0.2007 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 165,372B, BPFP=0.3139 +⌛️ [2/4] FRONTEND: Frontend time: 0.586s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.039s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09681199 13.37872119 + layer.39.0 18.83313593 2929.73809524 + ------------------------------------------------------------------------------------- + TOTAL 9.46497396 1471.55840821 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 271116 +BPFP 0.2573 bits/point +EBPFP 0.2573 equivalent bits/point +MSE 1471.558408 +---------------------- -------------------------------------------------------- +Time: 1.694s Load: 0.070s, Pack+Encode: 0.586s, Decode+Unpack: 1.039s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1471.5584 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03773504-graphic_4.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03773504-graphic_4.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03775071-painting_1.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03775071-painting_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 113,612B, BPFP=0.2156 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 200,572B, BPFP=0.3807 +⌛️ [2/4] FRONTEND: Frontend time: 0.587s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.056s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11048905 13.17241405 + layer.39.0 386.73560496 3422.97521866 + ------------------------------------------------------------------------------------- + TOTAL 193.42304701 1718.07381636 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 314184 +BPFP 0.2982 bits/point +EBPFP 0.2982 equivalent bits/point +MSE 1718.073816 +---------------------- -------------------------------------------------------- +Time: 1.712s Load: 0.070s, Pack+Encode: 0.587s, Decode+Unpack: 1.056s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1718.0738 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03775071-painting_1.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03775071-painting_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03888257-cartoon_30.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03888257-cartoon_30.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 120,164B, BPFP=0.2281 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 227,976B, BPFP=0.4327 +⌛️ [2/4] FRONTEND: Frontend time: 0.578s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.024s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13203045 13.19043159 + layer.39.0 375.96832483 3735.64407191 + ------------------------------------------------------------------------------------- + TOTAL 188.05017764 1874.41725175 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 348140 +BPFP 0.3304 bits/point +EBPFP 0.3304 equivalent bits/point +MSE 1874.417252 +---------------------- -------------------------------------------------------- +Time: 1.673s Load: 0.070s, Pack+Encode: 0.578s, Decode+Unpack: 1.024s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1874.4173 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03888257-cartoon_30.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03888257-cartoon_30.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03930630-toy_2.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03930630-toy_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.068s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 100,596B, BPFP=0.1909 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 197,944B, BPFP=0.3757 +⌛️ [2/4] FRONTEND: Frontend time: 0.583s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.009s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09699417 13.23103817 + layer.39.0 46.17573949 3504.97376093 + ------------------------------------------------------------------------------------- + TOTAL 23.13636683 1759.10239955 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 298540 +BPFP 0.2833 bits/point +EBPFP 0.2833 equivalent bits/point +MSE 1759.102400 +---------------------- -------------------------------------------------------- +Time: 1.661s Load: 0.068s, Pack+Encode: 0.583s, Decode+Unpack: 1.009s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1759.1024 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03930630-toy_2.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03930630-toy_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04086273-sticker_5.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04086273-sticker_5.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 107,384B, BPFP=0.2038 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 177,332B, BPFP=0.3366 +⌛️ [2/4] FRONTEND: Frontend time: 0.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.014s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10161624 13.04380296 + layer.39.0 24.98063198 2927.99829932 + ------------------------------------------------------------------------------------- + TOTAL 12.54112411 1470.52105114 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 284716 +BPFP 0.2702 bits/point +EBPFP 0.2702 equivalent bits/point +MSE 1470.521051 +---------------------- -------------------------------------------------------- +Time: 1.664s Load: 0.069s, Pack+Encode: 0.581s, Decode+Unpack: 1.014s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1470.5211 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04086273-sticker_5.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04086273-sticker_5.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04118538-sculpture_0.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04118538-sculpture_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 115,740B, BPFP=0.2197 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 260,792B, BPFP=0.4950 +⌛️ [2/4] FRONTEND: Frontend time: 0.590s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.042s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09846411 12.96396209 + layer.39.0 11.87055944 3511.15087464 + ------------------------------------------------------------------------------------- + TOTAL 5.98451177 1762.05741836 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 376532 +BPFP 0.3573 bits/point +EBPFP 0.3573 equivalent bits/point +MSE 1762.057418 +---------------------- -------------------------------------------------------- +Time: 1.701s Load: 0.069s, Pack+Encode: 0.590s, Decode+Unpack: 1.042s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1762.0574 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04118538-sculpture_0.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04118538-sculpture_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04133789-cartoon_7.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04133789-cartoon_7.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.068s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 140,544B, BPFP=0.2668 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 251,572B, BPFP=0.4775 +⌛️ [2/4] FRONTEND: Frontend time: 0.584s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.044s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13739287 37.30199982 + layer.39.0 370.52532799 4683.08454810 + ------------------------------------------------------------------------------------- + TOTAL 185.33136043 2360.19327396 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 392116 +BPFP 0.3721 bits/point +EBPFP 0.3721 equivalent bits/point +MSE 2360.193274 +---------------------- -------------------------------------------------------- +Time: 1.696s Load: 0.068s, Pack+Encode: 0.584s, Decode+Unpack: 1.044s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2360.1933 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04133789-cartoon_7.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04133789-cartoon_7.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04141076-cartoon_14.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04141076-cartoon_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 114,944B, BPFP=0.2182 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 222,084B, BPFP=0.4215 +⌛️ [2/4] FRONTEND: Frontend time: 0.577s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.060s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11960477 13.13645302 + layer.39.0 53.25505649 3533.97303207 + ------------------------------------------------------------------------------------- + TOTAL 26.68733063 1773.55474254 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 337028 +BPFP 0.3199 bits/point +EBPFP 0.3199 equivalent bits/point +MSE 1773.554743 +---------------------- -------------------------------------------------------- +Time: 1.710s Load: 0.072s, Pack+Encode: 0.577s, Decode+Unpack: 1.060s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1773.5547 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04141076-cartoon_14.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04141076-cartoon_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04146614-misc_4.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04146614-misc_4.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 115,236B, BPFP=0.2187 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 210,844B, BPFP=0.4002 +⌛️ [2/4] FRONTEND: Frontend time: 0.576s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.054s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10047569 13.10249351 + layer.39.0 167.29959305 3392.41180758 + ------------------------------------------------------------------------------------- + TOTAL 83.70003437 1702.75715054 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 326080 +BPFP 0.3095 bits/point +EBPFP 0.3095 equivalent bits/point +MSE 1702.757151 +---------------------- -------------------------------------------------------- +Time: 1.699s Load: 0.069s, Pack+Encode: 0.576s, Decode+Unpack: 1.054s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1702.7572 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04146614-misc_4.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04146614-misc_4.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04147183-art_9.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04147183-art_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 115,692B, BPFP=0.2196 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 182,076B, BPFP=0.3456 +⌛️ [2/4] FRONTEND: Frontend time: 0.593s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.026s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11332939 13.52204431 + layer.39.0 22.95352360 3534.19144801 + ------------------------------------------------------------------------------------- + TOTAL 11.53342649 1773.85674616 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 297768 +BPFP 0.2826 bits/point +EBPFP 0.2826 equivalent bits/point +MSE 1773.856746 +---------------------- -------------------------------------------------------- +Time: 1.689s Load: 0.070s, Pack+Encode: 0.593s, Decode+Unpack: 1.026s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1773.8567 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04147183-art_9.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04147183-art_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04192698-videogame_16.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04192698-videogame_16.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.079s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 120,252B, BPFP=0.2282 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 234,200B, BPFP=0.4445 +⌛️ [2/4] FRONTEND: Frontend time: 0.593s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.024s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09706018 12.86094851 + layer.39.0 404.66927843 3650.78595724 + ------------------------------------------------------------------------------------- + TOTAL 202.38316930 1831.82345287 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 354452 +BPFP 0.3364 bits/point +EBPFP 0.3364 equivalent bits/point +MSE 1831.823453 +---------------------- -------------------------------------------------------- +Time: 1.696s Load: 0.079s, Pack+Encode: 0.593s, Decode+Unpack: 1.024s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1831.8235 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04192698-videogame_16.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04192698-videogame_16.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04254680-deviantart_17.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04254680-deviantart_17.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 119,152B, BPFP=0.2262 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 216,576B, BPFP=0.4111 +⌛️ [2/4] FRONTEND: Frontend time: 0.604s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.022s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10685510 13.00907567 + layer.39.0 151.81593173 3321.94314869 + ------------------------------------------------------------------------------------- + TOTAL 75.96139341 1667.47611218 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 335728 +BPFP 0.3186 bits/point +EBPFP 0.3186 equivalent bits/point +MSE 1667.476112 +---------------------- -------------------------------------------------------- +Time: 1.696s Load: 0.070s, Pack+Encode: 0.604s, Decode+Unpack: 1.022s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1667.4761 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04254680-deviantart_17.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04254680-deviantart_17.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04266014-painting_13.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04266014-painting_13.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 112,816B, BPFP=0.2141 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 181,268B, BPFP=0.3441 +⌛️ [2/4] FRONTEND: Frontend time: 0.615s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.019s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09568562 13.16059471 + layer.39.0 29.62437363 3055.62439261 + ------------------------------------------------------------------------------------- + TOTAL 14.86002963 1534.39249366 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 294084 +BPFP 0.2791 bits/point +EBPFP 0.2791 equivalent bits/point +MSE 1534.392494 +---------------------- -------------------------------------------------------- +Time: 1.705s Load: 0.070s, Pack+Encode: 0.615s, Decode+Unpack: 1.019s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1534.3925 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04266014-painting_13.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04266014-painting_13.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04310018-art_3.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04310018-art_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 126,536B, BPFP=0.2402 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 200,620B, BPFP=0.3808 +⌛️ [2/4] FRONTEND: Frontend time: 0.606s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.003s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13375617 14.11119336 + layer.39.0 75.24515610 3429.68537415 + ------------------------------------------------------------------------------------- + TOTAL 37.68945614 1721.89828376 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 327156 +BPFP 0.3105 bits/point +EBPFP 0.3105 equivalent bits/point +MSE 1721.898284 +---------------------- -------------------------------------------------------- +Time: 1.679s Load: 0.070s, Pack+Encode: 0.606s, Decode+Unpack: 1.003s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1721.8983 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04310018-art_3.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04310018-art_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04347754-sculpture_0.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04347754-sculpture_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 135,164B, BPFP=0.2566 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 181,648B, BPFP=0.3448 +⌛️ [2/4] FRONTEND: Frontend time: 0.585s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.022s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14257451 25.32148931 + layer.39.0 394.23636419 3110.10106900 + ------------------------------------------------------------------------------------- + TOTAL 197.18946935 1567.71127915 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 316812 +BPFP 0.3007 bits/point +EBPFP 0.3007 equivalent bits/point +MSE 1567.711279 +---------------------- -------------------------------------------------------- +Time: 1.677s Load: 0.069s, Pack+Encode: 0.585s, Decode+Unpack: 1.022s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1567.7113 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04347754-sculpture_0.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04347754-sculpture_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04409515-deviantart_1.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04409515-deviantart_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 110,460B, BPFP=0.2097 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 184,792B, BPFP=0.3508 +⌛️ [2/4] FRONTEND: Frontend time: 0.586s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.079s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09627266 13.15101320 + layer.39.0 9.33068077 2908.18197279 + ------------------------------------------------------------------------------------- + TOTAL 4.71347671 1460.66649299 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 295252 +BPFP 0.2802 bits/point +EBPFP 0.2802 equivalent bits/point +MSE 1460.666493 +---------------------- -------------------------------------------------------- +Time: 1.734s Load: 0.069s, Pack+Encode: 0.586s, Decode+Unpack: 1.079s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1460.6665 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04409515-deviantart_1.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04409515-deviantart_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04487394-deviantart_8.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04487394-deviantart_8.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 115,080B, BPFP=0.2184 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 198,164B, BPFP=0.3761 +⌛️ [2/4] FRONTEND: Frontend time: 0.589s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.060s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09911632 13.15087464 + layer.39.0 99.63155977 3407.13508260 + ------------------------------------------------------------------------------------- + TOTAL 49.86533804 1710.14297862 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 313244 +BPFP 0.2973 bits/point +EBPFP 0.2973 equivalent bits/point +MSE 1710.142979 +---------------------- -------------------------------------------------------- +Time: 1.719s Load: 0.070s, Pack+Encode: 0.589s, Decode+Unpack: 1.060s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1710.1430 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04487394-deviantart_8.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04487394-deviantart_8.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04522168-painting_32.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04522168-painting_32.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.068s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 109,940B, BPFP=0.2087 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 174,500B, BPFP=0.3312 +⌛️ [2/4] FRONTEND: Frontend time: 0.583s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.041s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11740584 13.25755436 + layer.39.0 10.95138066 3223.99975705 + ------------------------------------------------------------------------------------- + TOTAL 5.53439325 1618.62865570 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 284440 +BPFP 0.2699 bits/point +EBPFP 0.2699 equivalent bits/point +MSE 1618.628656 +---------------------- -------------------------------------------------------- +Time: 1.692s Load: 0.068s, Pack+Encode: 0.583s, Decode+Unpack: 1.041s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1618.6287 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04522168-painting_32.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04522168-painting_32.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04591713-painting_14.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04591713-painting_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 120,164B, BPFP=0.2281 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 207,952B, BPFP=0.3947 +⌛️ [2/4] FRONTEND: Frontend time: 0.580s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.030s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11212821 13.21296940 + layer.39.0 165.22564383 3084.84110787 + ------------------------------------------------------------------------------------- + TOTAL 82.66888602 1549.02703863 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 328116 +BPFP 0.3114 bits/point +EBPFP 0.3114 equivalent bits/point +MSE 1549.027039 +---------------------- -------------------------------------------------------- +Time: 1.679s Load: 0.070s, Pack+Encode: 0.580s, Decode+Unpack: 1.030s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1549.0270 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04591713-painting_14.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04591713-painting_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07693725-sketch_15.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07693725-sketch_15.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 125,820B, BPFP=0.2388 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 193,932B, BPFP=0.3681 +⌛️ [2/4] FRONTEND: Frontend time: 0.577s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.044s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10569874 12.89484918 + layer.39.0 214.96065658 3609.75534500 + ------------------------------------------------------------------------------------- + TOTAL 107.53317766 1811.32509709 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 319752 +BPFP 0.3035 bits/point +EBPFP 0.3035 equivalent bits/point +MSE 1811.325097 +---------------------- -------------------------------------------------------- +Time: 1.690s Load: 0.069s, Pack+Encode: 0.577s, Decode+Unpack: 1.044s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1811.3251 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07693725-sketch_15.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07693725-sketch_15.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07695742-videogame_1.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07695742-videogame_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 132,668B, BPFP=0.2518 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 207,692B, BPFP=0.3942 +⌛️ [2/4] FRONTEND: Frontend time: 0.595s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.984s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12460778 12.96794237 + layer.39.0 438.29433916 3778.07069971 + ------------------------------------------------------------------------------------- + TOTAL 219.20947347 1895.51932104 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 340360 +BPFP 0.3230 bits/point +EBPFP 0.3230 equivalent bits/point +MSE 1895.519321 +---------------------- -------------------------------------------------------- +Time: 1.649s Load: 0.070s, Pack+Encode: 0.595s, Decode+Unpack: 0.984s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1895.5193 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07695742-videogame_1.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07695742-videogame_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697313-deviantart_7.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697313-deviantart_7.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 112,888B, BPFP=0.2143 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 180,444B, BPFP=0.3425 +⌛️ [2/4] FRONTEND: Frontend time: 0.512s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.974s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09520741 13.40609626 + layer.39.0 14.69109212 3408.45772595 + ------------------------------------------------------------------------------------- + TOTAL 7.39314977 1710.93191110 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 293332 +BPFP 0.2784 bits/point +EBPFP 0.2784 equivalent bits/point +MSE 1710.931911 +---------------------- -------------------------------------------------------- +Time: 1.536s Load: 0.051s, Pack+Encode: 0.512s, Decode+Unpack: 0.974s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1710.9319 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697313-deviantart_7.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07697313-deviantart_7.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697537-deviantart_16.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697537-deviantart_16.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 115,992B, BPFP=0.2202 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 222,148B, BPFP=0.4217 +⌛️ [2/4] FRONTEND: Frontend time: 0.522s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.990s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09755328 12.80359041 + layer.39.0 90.32537658 3630.16666667 + ------------------------------------------------------------------------------------- + TOTAL 45.21146493 1821.48512854 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 338140 +BPFP 0.3209 bits/point +EBPFP 0.3209 equivalent bits/point +MSE 1821.485129 +---------------------- -------------------------------------------------------- +Time: 1.582s Load: 0.070s, Pack+Encode: 0.522s, Decode+Unpack: 0.990s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1821.4851 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697537-deviantart_16.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07697537-deviantart_16.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714571-deviantart_0.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714571-deviantart_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 111,604B, BPFP=0.2118 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 217,100B, BPFP=0.4121 +⌛️ [2/4] FRONTEND: Frontend time: 0.591s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.985s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09528512 12.89623478 + layer.39.0 45.81401467 3868.17492711 + ------------------------------------------------------------------------------------- + TOTAL 22.95464989 1940.53558095 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 328704 +BPFP 0.3120 bits/point +EBPFP 0.3120 equivalent bits/point +MSE 1940.535581 +---------------------- -------------------------------------------------------- +Time: 1.646s Load: 0.070s, Pack+Encode: 0.591s, Decode+Unpack: 0.985s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1940.5356 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714571-deviantart_0.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07714571-deviantart_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714990-toy_14.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714990-toy_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 113,144B, BPFP=0.2148 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 253,100B, BPFP=0.4804 +⌛️ [2/4] FRONTEND: Frontend time: 0.584s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.015s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09793257 12.99111603 + layer.39.0 322.50334062 3992.87900875 + ------------------------------------------------------------------------------------- + TOTAL 161.30063660 2002.93506239 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 366244 +BPFP 0.3476 bits/point +EBPFP 0.3476 equivalent bits/point +MSE 2002.935062 +---------------------- -------------------------------------------------------- +Time: 1.669s Load: 0.070s, Pack+Encode: 0.584s, Decode+Unpack: 1.015s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2002.9351 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714990-toy_14.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07714990-toy_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07718472-cartoon_1.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07718472-cartoon_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 103,840B, BPFP=0.1971 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 163,264B, BPFP=0.3099 +⌛️ [2/4] FRONTEND: Frontend time: 0.580s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.049s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11235230 13.19827540 + layer.39.0 14.49942963 2935.56073858 + ------------------------------------------------------------------------------------- + TOTAL 7.30589096 1474.37950699 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 267104 +BPFP 0.2535 bits/point +EBPFP 0.2535 equivalent bits/point +MSE 1474.379507 +---------------------- -------------------------------------------------------- +Time: 1.698s Load: 0.070s, Pack+Encode: 0.580s, Decode+Unpack: 1.049s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1474.3795 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07718472-cartoon_1.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07718472-cartoon_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07742313-deviantart_8.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07742313-deviantart_8.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 107,152B, BPFP=0.2034 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 175,088B, BPFP=0.3323 +⌛️ [2/4] FRONTEND: Frontend time: 0.584s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.030s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09669835 13.37872119 + layer.39.0 8.77690150 2567.17201166 + ------------------------------------------------------------------------------------- + TOTAL 4.43679992 1290.27536642 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 282240 +BPFP 0.2679 bits/point +EBPFP 0.2679 equivalent bits/point +MSE 1290.275366 +---------------------- -------------------------------------------------------- +Time: 1.684s Load: 0.069s, Pack+Encode: 0.584s, Decode+Unpack: 1.030s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1290.2754 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07742313-deviantart_8.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07742313-deviantart_8.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07749582-sticker_0.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07749582-sticker_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 118,356B, BPFP=0.2246 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 214,876B, BPFP=0.4079 +⌛️ [2/4] FRONTEND: Frontend time: 0.589s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.988s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09550123 12.93112530 + layer.39.0 34.64631545 3357.68513120 + ------------------------------------------------------------------------------------- + TOTAL 17.37090834 1685.30812825 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 333232 +BPFP 0.3163 bits/point +EBPFP 0.3163 equivalent bits/point +MSE 1685.308128 +---------------------- -------------------------------------------------------- +Time: 1.646s Load: 0.069s, Pack+Encode: 0.589s, Decode+Unpack: 0.988s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1685.3081 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07749582-sticker_0.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07749582-sticker_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07753275-painting_2.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07753275-painting_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 142,708B, BPFP=0.2709 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 227,804B, BPFP=0.4324 +⌛️ [2/4] FRONTEND: Frontend time: 0.523s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.985s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10429548 12.94280134 + layer.39.0 540.43106171 4315.77745384 + ------------------------------------------------------------------------------------- + TOTAL 270.26767859 2164.36012759 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 370512 +BPFP 0.3516 bits/point +EBPFP 0.3516 equivalent bits/point +MSE 2164.360128 +---------------------- -------------------------------------------------------- +Time: 1.557s Load: 0.050s, Pack+Encode: 0.523s, Decode+Unpack: 0.985s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2164.3601 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07753275-painting_2.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07753275-painting_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07768694-painting_12.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07768694-painting_12.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 120,652B, BPFP=0.2290 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 209,572B, BPFP=0.3978 +⌛️ [2/4] FRONTEND: Frontend time: 0.547s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.052s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09821300 13.09233878 + layer.39.0 635.68343052 4027.99659864 + ------------------------------------------------------------------------------------- + TOTAL 317.89082176 2020.54446871 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 330224 +BPFP 0.3134 bits/point +EBPFP 0.3134 equivalent bits/point +MSE 2020.544469 +---------------------- -------------------------------------------------------- +Time: 1.649s Load: 0.050s, Pack+Encode: 0.547s, Decode+Unpack: 1.052s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2020.5445 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07768694-painting_12.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07768694-painting_12.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07920052-painting_1.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07920052-painting_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 116,388B, BPFP=0.2209 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 231,620B, BPFP=0.4396 +⌛️ [2/4] FRONTEND: Frontend time: 0.586s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.990s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09582097 12.93651395 + layer.39.0 9.59182155 3167.53960155 + ------------------------------------------------------------------------------------- + TOTAL 4.84382126 1590.23805775 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 348008 +BPFP 0.3303 bits/point +EBPFP 0.3303 equivalent bits/point +MSE 1590.238058 +---------------------- -------------------------------------------------------- +Time: 1.646s Load: 0.070s, Pack+Encode: 0.586s, Decode+Unpack: 0.990s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1590.2381 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07920052-painting_1.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07920052-painting_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09472597-painting_15.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09472597-painting_15.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 101,152B, BPFP=0.1920 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 175,104B, BPFP=0.3324 +⌛️ [2/4] FRONTEND: Frontend time: 0.583s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.014s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09164813 13.39860833 + layer.39.0 9.11265014 2674.85592809 + ------------------------------------------------------------------------------------- + TOTAL 4.60214913 1344.12726821 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 276256 +BPFP 0.2622 bits/point +EBPFP 0.2622 equivalent bits/point +MSE 1344.127268 +---------------------- -------------------------------------------------------- +Time: 1.666s Load: 0.070s, Pack+Encode: 0.583s, Decode+Unpack: 1.014s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1344.1273 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09472597-painting_15.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n09472597-painting_15.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09835506-videogame_3.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09835506-videogame_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 111,008B, BPFP=0.2107 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 211,204B, BPFP=0.4009 +⌛️ [2/4] FRONTEND: Frontend time: 0.583s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.006s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09585661 13.10245080 + layer.39.0 12.34450164 3429.67832847 + ------------------------------------------------------------------------------------- + TOTAL 6.22017912 1721.39038964 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 322212 +BPFP 0.3058 bits/point +EBPFP 0.3058 equivalent bits/point +MSE 1721.390390 +---------------------- -------------------------------------------------------- +Time: 1.659s Load: 0.070s, Pack+Encode: 0.583s, Decode+Unpack: 1.006s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1721.3904 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09835506-videogame_3.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n09835506-videogame_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n12267677-misc_105.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n12267677-misc_105.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 103,124B, BPFP=0.1957 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 233,000B, BPFP=0.4423 +⌛️ [2/4] FRONTEND: Frontend time: 0.593s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.034s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10166193 13.03536789 + layer.39.0 219.41089650 3941.69922255 + ------------------------------------------------------------------------------------- + TOTAL 109.75627921 1977.36729522 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 336124 +BPFP 0.3190 bits/point +EBPFP 0.3190 equivalent bits/point +MSE 1977.367295 +---------------------- -------------------------------------------------------- +Time: 1.697s Load: 0.070s, Pack+Encode: 0.593s, Decode+Unpack: 1.034s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1977.3673 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n12267677-misc_105.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n12267677-misc_105.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 0.3107 bits/point +Avg EBPFP 0.3107 equivalent bits/point +Avg MSE 1772.186609 +Avg Time 1.670s +------------------------ ---------------------------- diff --git a/lambda0.004/hyperprior-featurecoding-8bit-individual/cls_in1kval/dtufc_hyperprior-featurecoding_dinov3-total_individual.log b/lambda0.004/hyperprior-featurecoding-8bit-individual/cls_in1kval/dtufc_hyperprior-featurecoding_dinov3-total_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..254f22ec0b643962a62e405eba5f344fa3294abd --- /dev/null +++ b/lambda0.004/hyperprior-featurecoding-8bit-individual/cls_in1kval/dtufc_hyperprior-featurecoding_dinov3-total_individual.log @@ -0,0 +1,9344 @@ +Experiment: dtufc_hyperprior-featurecoding_dinov3-total_individual +Log file: output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/dtufc_hyperprior-featurecoding_dinov3-total_individual.log +DTUFCCodecConfig: + arch: hyperprior-featurecoding + handler: dinov3-total + checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.004_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.004_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 559 +Loaded hyperprior-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.9' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.19' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.29' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.39' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json +Loaded per-key mappings: model=dinov3-total + Keys: ['layer.9', 'layer.19', 'layer.29', 'layer.39'] +---------------- ------------------------------------------------------------------------------------------------------------------------------ +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +Checkpoint codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.004_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-DINOv3/imagenet1k-val +Output output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval +---------------- ------------------------------------------------------------------------------------------------------------------------------ +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02825657-ILSVRC2012_val_00001103.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02825657-ILSVRC2012_val_00001103.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.090s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 105,020B, BPFP=0.1993 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 174,656B, BPFP=0.3315 +⌛️ [2/4] FRONTEND: Frontend time: 0.837s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.067s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10264289 13.06834799 + layer.39.0 9.47367932 3201.65427600 + ------------------------------------------------------------------------------------- + TOTAL 4.78816110 1607.36131199 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 279676 +BPFP 0.2654 bits/point +EBPFP 0.2654 equivalent bits/point +MSE 1607.361312 +---------------------- -------------------------------------------------------- +Time: 1.994s Load: 0.090s, Pack+Encode: 0.837s, Decode+Unpack: 1.067s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1607.3613 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02825657-ILSVRC2012_val_00001103.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02825657-ILSVRC2012_val_00001103.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02834397-ILSVRC2012_val_00001252.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02834397-ILSVRC2012_val_00001252.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 134,108B, BPFP=0.2545 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 216,500B, BPFP=0.4109 +⌛️ [2/4] FRONTEND: Frontend time: 0.590s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.074s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14789204 13.14241204 + layer.39.0 415.43227648 3629.49514091 + ------------------------------------------------------------------------------------- + TOTAL 207.79008426 1821.31877648 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 350608 +BPFP 0.3327 bits/point +EBPFP 0.3327 equivalent bits/point +MSE 1821.318776 +---------------------- -------------------------------------------------------- +Time: 1.735s Load: 0.070s, Pack+Encode: 0.590s, Decode+Unpack: 1.074s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1821.3188 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02834397-ILSVRC2012_val_00001252.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02834397-ILSVRC2012_val_00001252.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02840245-ILSVRC2012_val_00003446.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02840245-ILSVRC2012_val_00003446.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 113,260B, BPFP=0.2150 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 185,200B, BPFP=0.3515 +⌛️ [2/4] FRONTEND: Frontend time: 0.558s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.988s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10761288 13.26430678 + layer.39.0 28.71820525 3044.25097182 + ------------------------------------------------------------------------------------- + TOTAL 14.41290906 1528.75763930 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 298460 +BPFP 0.2833 bits/point +EBPFP 0.2833 equivalent bits/point +MSE 1528.757639 +---------------------- -------------------------------------------------------- +Time: 1.596s Load: 0.051s, Pack+Encode: 0.558s, Decode+Unpack: 0.988s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1528.7576 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02840245-ILSVRC2012_val_00003446.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02840245-ILSVRC2012_val_00003446.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02843684-ILSVRC2012_val_00000514.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02843684-ILSVRC2012_val_00000514.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 114,956B, BPFP=0.2182 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 178,772B, BPFP=0.3393 +⌛️ [2/4] FRONTEND: Frontend time: 0.578s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.002s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11482661 13.03123387 + layer.39.0 84.54469600 3363.80952381 + ------------------------------------------------------------------------------------- + TOTAL 42.32976130 1688.42037884 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 293728 +BPFP 0.2788 bits/point +EBPFP 0.2788 equivalent bits/point +MSE 1688.420379 +---------------------- -------------------------------------------------------- +Time: 1.632s Load: 0.052s, Pack+Encode: 0.578s, Decode+Unpack: 1.002s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1688.4204 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02843684-ILSVRC2012_val_00000514.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02843684-ILSVRC2012_val_00000514.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02859443-ILSVRC2012_val_00000193.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02859443-ILSVRC2012_val_00000193.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 99,364B, BPFP=0.1886 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 137,788B, BPFP=0.2615 +⌛️ [2/4] FRONTEND: Frontend time: 0.548s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.008s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11417333 13.39546985 + layer.39.0 9.67809406 2633.73275024 + ------------------------------------------------------------------------------------- + TOTAL 4.89613370 1323.56411005 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 237152 +BPFP 0.2251 bits/point +EBPFP 0.2251 equivalent bits/point +MSE 1323.564110 +---------------------- -------------------------------------------------------- +Time: 1.606s Load: 0.051s, Pack+Encode: 0.548s, Decode+Unpack: 1.008s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1323.5641 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02859443-ILSVRC2012_val_00000193.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02859443-ILSVRC2012_val_00000193.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02860847-ILSVRC2012_val_00000601.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02860847-ILSVRC2012_val_00000601.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 121,708B, BPFP=0.2310 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 176,840B, BPFP=0.3357 +⌛️ [2/4] FRONTEND: Frontend time: 0.559s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.988s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12653054 88.07366071 + layer.39.0 266.35249636 3324.23104956 + ------------------------------------------------------------------------------------- + TOTAL 133.23951345 1706.15235514 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 298548 +BPFP 0.2833 bits/point +EBPFP 0.2833 equivalent bits/point +MSE 1706.152355 +---------------------- -------------------------------------------------------- +Time: 1.598s Load: 0.051s, Pack+Encode: 0.559s, Decode+Unpack: 0.988s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1706.1524 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02860847-ILSVRC2012_val_00000601.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02860847-ILSVRC2012_val_00000601.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02865351-ILSVRC2012_val_00000763.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02865351-ILSVRC2012_val_00000763.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 99,908B, BPFP=0.1896 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 232,588B, BPFP=0.4415 +⌛️ [2/4] FRONTEND: Frontend time: 0.571s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.037s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09467571 13.17491762 + layer.39.0 15.47581086 3835.88119534 + ------------------------------------------------------------------------------------- + TOTAL 7.78524328 1924.52805648 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 332496 +BPFP 0.3156 bits/point +EBPFP 0.3156 equivalent bits/point +MSE 1924.528056 +---------------------- -------------------------------------------------------- +Time: 1.660s Load: 0.051s, Pack+Encode: 0.571s, Decode+Unpack: 1.037s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1924.5281 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02865351-ILSVRC2012_val_00000763.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02865351-ILSVRC2012_val_00000763.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02869837-ILSVRC2012_val_00000906.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02869837-ILSVRC2012_val_00000906.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 122,100B, BPFP=0.2318 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 266,440B, BPFP=0.5057 +⌛️ [2/4] FRONTEND: Frontend time: 0.538s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.001s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09659988 13.06888419 + layer.39.0 16.39405483 3759.37998056 + ------------------------------------------------------------------------------------- + TOTAL 8.24532736 1886.22443238 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 388540 +BPFP 0.3687 bits/point +EBPFP 0.3687 equivalent bits/point +MSE 1886.224432 +---------------------- -------------------------------------------------------- +Time: 1.590s Load: 0.051s, Pack+Encode: 0.538s, Decode+Unpack: 1.001s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1886.2244 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02869837-ILSVRC2012_val_00000906.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02869837-ILSVRC2012_val_00000906.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02870880-ILSVRC2012_val_00003274.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02870880-ILSVRC2012_val_00003274.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 121,420B, BPFP=0.2305 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 171,764B, BPFP=0.3260 +⌛️ [2/4] FRONTEND: Frontend time: 0.584s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.035s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10254154 13.47017071 + layer.39.0 9.36513093 3256.87973761 + ------------------------------------------------------------------------------------- + TOTAL 4.73383623 1635.17495416 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 293184 +BPFP 0.2782 bits/point +EBPFP 0.2782 equivalent bits/point +MSE 1635.174954 +---------------------- -------------------------------------------------------- +Time: 1.671s Load: 0.052s, Pack+Encode: 0.584s, Decode+Unpack: 1.035s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1635.1750 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02870880-ILSVRC2012_val_00003274.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02870880-ILSVRC2012_val_00003274.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02871525-ILSVRC2012_val_00000879.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02871525-ILSVRC2012_val_00000879.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 143,040B, BPFP=0.2715 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 207,020B, BPFP=0.3929 +⌛️ [2/4] FRONTEND: Frontend time: 0.519s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.995s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.17072899 49.93028350 + layer.39.0 20.29403547 3403.95578231 + ------------------------------------------------------------------------------------- + TOTAL 10.23238223 1726.94303291 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 350060 +BPFP 0.3322 bits/point +EBPFP 0.3322 equivalent bits/point +MSE 1726.943033 +---------------------- -------------------------------------------------------- +Time: 1.564s Load: 0.050s, Pack+Encode: 0.519s, Decode+Unpack: 0.995s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1726.9430 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02871525-ILSVRC2012_val_00000879.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02871525-ILSVRC2012_val_00000879.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02877765-ILSVRC2012_val_00000634.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02877765-ILSVRC2012_val_00000634.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 116,296B, BPFP=0.2207 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 241,388B, BPFP=0.4582 +⌛️ [2/4] FRONTEND: Frontend time: 0.580s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.055s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10908128 12.94684045 + layer.39.0 364.97770894 4057.71428571 + ------------------------------------------------------------------------------------- + TOTAL 182.54339511 2035.33056308 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 357684 +BPFP 0.3395 bits/point +EBPFP 0.3395 equivalent bits/point +MSE 2035.330563 +---------------------- -------------------------------------------------------- +Time: 1.685s Load: 0.051s, Pack+Encode: 0.580s, Decode+Unpack: 1.055s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2035.3306 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02877765-ILSVRC2012_val_00000634.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02877765-ILSVRC2012_val_00000634.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02879718-ILSVRC2012_val_00001354.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02879718-ILSVRC2012_val_00001354.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 120,304B, BPFP=0.2283 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 203,672B, BPFP=0.3866 +⌛️ [2/4] FRONTEND: Frontend time: 0.580s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.039s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10948122 13.42192340 + layer.39.0 55.92460444 3755.46987366 + ------------------------------------------------------------------------------------- + TOTAL 28.01704283 1884.44589853 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 323976 +BPFP 0.3075 bits/point +EBPFP 0.3075 equivalent bits/point +MSE 1884.445899 +---------------------- -------------------------------------------------------- +Time: 1.671s Load: 0.052s, Pack+Encode: 0.580s, Decode+Unpack: 1.039s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1884.4459 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02879718-ILSVRC2012_val_00001354.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02879718-ILSVRC2012_val_00001354.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02883205-ILSVRC2012_val_00000126.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02883205-ILSVRC2012_val_00000126.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 101,012B, BPFP=0.1917 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 196,136B, BPFP=0.3723 +⌛️ [2/4] FRONTEND: Frontend time: 0.569s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.003s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.06711708 13.54395575 + layer.39.0 7.82069686 3038.51287658 + ------------------------------------------------------------------------------------- + TOTAL 7.94390697 1526.02841617 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 297148 +BPFP 0.2820 bits/point +EBPFP 0.2820 equivalent bits/point +MSE 1526.028416 +---------------------- -------------------------------------------------------- +Time: 1.623s Load: 0.051s, Pack+Encode: 0.569s, Decode+Unpack: 1.003s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1526.0284 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02883205-ILSVRC2012_val_00000126.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02883205-ILSVRC2012_val_00000126.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892201-ILSVRC2012_val_00001145.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892201-ILSVRC2012_val_00001145.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 130,004B, BPFP=0.2468 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 208,888B, BPFP=0.3965 +⌛️ [2/4] FRONTEND: Frontend time: 0.566s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.055s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11297333 25.89171450 + layer.39.0 15.09638643 2808.90694849 + ------------------------------------------------------------------------------------- + TOTAL 7.60467988 1417.39933150 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 338892 +BPFP 0.3216 bits/point +EBPFP 0.3216 equivalent bits/point +MSE 1417.399331 +---------------------- -------------------------------------------------------- +Time: 1.672s Load: 0.052s, Pack+Encode: 0.566s, Decode+Unpack: 1.055s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1417.3993 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892201-ILSVRC2012_val_00001145.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02892201-ILSVRC2012_val_00001145.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892767-ILSVRC2012_val_00000808.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892767-ILSVRC2012_val_00000808.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 115,232B, BPFP=0.2187 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 273,096B, BPFP=0.5184 +⌛️ [2/4] FRONTEND: Frontend time: 0.562s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.042s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09598007 13.04629324 + layer.39.0 31.15013059 3895.04470360 + ------------------------------------------------------------------------------------- + TOTAL 15.62305533 1954.04549842 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 388328 +BPFP 0.3685 bits/point +EBPFP 0.3685 equivalent bits/point +MSE 1954.045498 +---------------------- -------------------------------------------------------- +Time: 1.655s Load: 0.051s, Pack+Encode: 0.562s, Decode+Unpack: 1.042s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1954.0455 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892767-ILSVRC2012_val_00000808.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02892767-ILSVRC2012_val_00000808.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02895154-ILSVRC2012_val_00000080.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02895154-ILSVRC2012_val_00000080.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.053s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 118,228B, BPFP=0.2244 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 242,304B, BPFP=0.4599 +⌛️ [2/4] FRONTEND: Frontend time: 0.521s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.985s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09530723 13.02426791 + layer.39.0 971.40427600 4022.79324587 + ------------------------------------------------------------------------------------- + TOTAL 485.74979162 2017.90875689 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 360532 +BPFP 0.3422 bits/point +EBPFP 0.3422 equivalent bits/point +MSE 2017.908757 +---------------------- -------------------------------------------------------- +Time: 1.559s Load: 0.053s, Pack+Encode: 0.521s, Decode+Unpack: 0.985s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2017.9088 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02895154-ILSVRC2012_val_00000080.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02895154-ILSVRC2012_val_00000080.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02906734-ILSVRC2012_val_00002937.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02906734-ILSVRC2012_val_00002937.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 112,992B, BPFP=0.2145 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 184,720B, BPFP=0.3506 +⌛️ [2/4] FRONTEND: Frontend time: 0.555s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.010s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09767962 12.97700380 + layer.39.0 32.09536716 2816.83430515 + ------------------------------------------------------------------------------------- + TOTAL 16.09652339 1414.90565448 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 297712 +BPFP 0.2825 bits/point +EBPFP 0.2825 equivalent bits/point +MSE 1414.905654 +---------------------- -------------------------------------------------------- +Time: 1.616s Load: 0.051s, Pack+Encode: 0.555s, Decode+Unpack: 1.010s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1414.9057 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02906734-ILSVRC2012_val_00002937.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02906734-ILSVRC2012_val_00002937.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02910353-ILSVRC2012_val_00000558.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02910353-ILSVRC2012_val_00000558.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 115,540B, BPFP=0.2193 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 192,136B, BPFP=0.3647 +⌛️ [2/4] FRONTEND: Frontend time: 0.562s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.044s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11017090 13.02724885 + layer.39.0 483.40066205 3587.80587949 + ------------------------------------------------------------------------------------- + TOTAL 241.75541648 1800.41656417 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 307676 +BPFP 0.2920 bits/point +EBPFP 0.2920 equivalent bits/point +MSE 1800.416564 +---------------------- -------------------------------------------------------- +Time: 1.657s Load: 0.050s, Pack+Encode: 0.562s, Decode+Unpack: 1.044s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1800.4166 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02910353-ILSVRC2012_val_00000558.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02910353-ILSVRC2012_val_00000558.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02916936-ILSVRC2012_val_00000366.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02916936-ILSVRC2012_val_00000366.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 108,440B, BPFP=0.2058 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 216,548B, BPFP=0.4110 +⌛️ [2/4] FRONTEND: Frontend time: 0.575s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.015s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10657579 13.13764501 + layer.39.0 435.18944363 3736.81899903 + ------------------------------------------------------------------------------------- + TOTAL 217.64800971 1874.97832202 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 324988 +BPFP 0.3084 bits/point +EBPFP 0.3084 equivalent bits/point +MSE 1874.978322 +---------------------- -------------------------------------------------------- +Time: 1.640s Load: 0.050s, Pack+Encode: 0.575s, Decode+Unpack: 1.015s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1874.9783 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02916936-ILSVRC2012_val_00000366.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02916936-ILSVRC2012_val_00000366.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02917067-ILSVRC2012_val_00000562.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02917067-ILSVRC2012_val_00000562.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.058s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 127,000B, BPFP=0.2411 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 204,840B, BPFP=0.3888 +⌛️ [2/4] FRONTEND: Frontend time: 0.529s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.009s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10760244 13.76856608 + layer.39.0 37.55795979 3879.14528669 + ------------------------------------------------------------------------------------- + TOTAL 18.83278111 1946.45692638 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 331840 +BPFP 0.3149 bits/point +EBPFP 0.3149 equivalent bits/point +MSE 1946.456926 +---------------------- -------------------------------------------------------- +Time: 1.597s Load: 0.058s, Pack+Encode: 0.529s, Decode+Unpack: 1.009s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1946.4569 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02917067-ILSVRC2012_val_00000562.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02917067-ILSVRC2012_val_00000562.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02930766-ILSVRC2012_val_00000056.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02930766-ILSVRC2012_val_00000056.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 123,160B, BPFP=0.2338 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 228,684B, BPFP=0.4341 +⌛️ [2/4] FRONTEND: Frontend time: 0.549s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.007s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10591127 61.54727739 + layer.39.0 18.32421875 3868.13265306 + ------------------------------------------------------------------------------------- + TOTAL 9.21506501 1964.83996523 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 351844 +BPFP 0.3339 bits/point +EBPFP 0.3339 equivalent bits/point +MSE 1964.839965 +---------------------- -------------------------------------------------------- +Time: 1.606s Load: 0.050s, Pack+Encode: 0.549s, Decode+Unpack: 1.007s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1964.8400 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02930766-ILSVRC2012_val_00000056.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02930766-ILSVRC2012_val_00000056.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02939185-ILSVRC2012_val_00000302.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02939185-ILSVRC2012_val_00000302.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 116,332B, BPFP=0.2208 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 196,400B, BPFP=0.3728 +⌛️ [2/4] FRONTEND: Frontend time: 0.579s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.038s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09694758 13.01007881 + layer.39.0 25.52453269 3346.38994169 + ------------------------------------------------------------------------------------- + TOTAL 12.81074014 1679.70001025 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 312732 +BPFP 0.2968 bits/point +EBPFP 0.2968 equivalent bits/point +MSE 1679.700010 +---------------------- -------------------------------------------------------- +Time: 1.667s Load: 0.051s, Pack+Encode: 0.579s, Decode+Unpack: 1.038s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1679.7000 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02939185-ILSVRC2012_val_00000302.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02939185-ILSVRC2012_val_00000302.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02950826-ILSVRC2012_val_00000392.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02950826-ILSVRC2012_val_00000392.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 118,772B, BPFP=0.2254 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 191,800B, BPFP=0.3641 +⌛️ [2/4] FRONTEND: Frontend time: 0.549s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.997s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10873010 13.40036975 + layer.39.0 707.96944849 4072.65306122 + ------------------------------------------------------------------------------------- + TOTAL 354.03908930 2043.02671549 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 310572 +BPFP 0.2947 bits/point +EBPFP 0.2947 equivalent bits/point +MSE 2043.026715 +---------------------- -------------------------------------------------------- +Time: 1.598s Load: 0.051s, Pack+Encode: 0.549s, Decode+Unpack: 0.997s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2043.0267 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02950826-ILSVRC2012_val_00000392.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02950826-ILSVRC2012_val_00000392.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951358-ILSVRC2012_val_00000347.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951358-ILSVRC2012_val_00000347.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 115,552B, BPFP=0.2193 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 171,928B, BPFP=0.3263 +⌛️ [2/4] FRONTEND: Frontend time: 0.520s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.009s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12200860 12.92399800 + layer.39.0 237.66299198 3189.67128280 + ------------------------------------------------------------------------------------- + TOTAL 118.89250029 1601.29764040 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 287480 +BPFP 0.2728 bits/point +EBPFP 0.2728 equivalent bits/point +MSE 1601.297640 +---------------------- -------------------------------------------------------- +Time: 1.598s Load: 0.070s, Pack+Encode: 0.520s, Decode+Unpack: 1.009s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1601.2976 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951358-ILSVRC2012_val_00000347.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02951358-ILSVRC2012_val_00000347.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951585-ILSVRC2012_val_00000101.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951585-ILSVRC2012_val_00000101.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 98,780B, BPFP=0.1875 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 195,224B, BPFP=0.3706 +⌛️ [2/4] FRONTEND: Frontend time: 0.540s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.009s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.07385432 13.35234831 + layer.39.0 181.90962099 3681.28984451 + ------------------------------------------------------------------------------------- + TOTAL 94.99173765 1847.32109641 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 294004 +BPFP 0.2790 bits/point +EBPFP 0.2790 equivalent bits/point +MSE 1847.321096 +---------------------- -------------------------------------------------------- +Time: 1.598s Load: 0.050s, Pack+Encode: 0.540s, Decode+Unpack: 1.009s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1847.3211 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951585-ILSVRC2012_val_00000101.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02951585-ILSVRC2012_val_00000101.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02963159-ILSVRC2012_val_00000061.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02963159-ILSVRC2012_val_00000061.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 118,120B, BPFP=0.2242 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 234,876B, BPFP=0.4458 +⌛️ [2/4] FRONTEND: Frontend time: 0.578s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.001s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11232698 12.99748789 + layer.39.0 24.77479842 3501.72813411 + ------------------------------------------------------------------------------------- + TOTAL 12.44356270 1757.36281100 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 352996 +BPFP 0.3350 bits/point +EBPFP 0.3350 equivalent bits/point +MSE 1757.362811 +---------------------- -------------------------------------------------------- +Time: 1.629s Load: 0.050s, Pack+Encode: 0.578s, Decode+Unpack: 1.001s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1757.3628 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02963159-ILSVRC2012_val_00000061.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02963159-ILSVRC2012_val_00000061.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02965783-ILSVRC2012_val_00000213.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02965783-ILSVRC2012_val_00000213.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 111,352B, BPFP=0.2114 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 237,244B, BPFP=0.4503 +⌛️ [2/4] FRONTEND: Frontend time: 0.575s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.037s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09516161 13.02690055 + layer.39.0 223.32294704 3397.40913508 + ------------------------------------------------------------------------------------- + TOTAL 111.70905432 1705.21801782 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 348596 +BPFP 0.3308 bits/point +EBPFP 0.3308 equivalent bits/point +MSE 1705.218018 +---------------------- -------------------------------------------------------- +Time: 1.664s Load: 0.052s, Pack+Encode: 0.575s, Decode+Unpack: 1.037s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1705.2180 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02965783-ILSVRC2012_val_00000213.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02965783-ILSVRC2012_val_00000213.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966193-ILSVRC2012_val_00000074.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966193-ILSVRC2012_val_00000074.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 133,252B, BPFP=0.2529 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 297,712B, BPFP=0.5651 +⌛️ [2/4] FRONTEND: Frontend time: 0.565s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.025s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12190965 37.30780415 + layer.39.0 378.75431244 4882.86977648 + ------------------------------------------------------------------------------------- + TOTAL 189.43811104 2460.08879032 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 430964 +BPFP 0.4090 bits/point +EBPFP 0.4090 equivalent bits/point +MSE 2460.088790 +---------------------- -------------------------------------------------------- +Time: 1.642s Load: 0.052s, Pack+Encode: 0.565s, Decode+Unpack: 1.025s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2460.0888 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966193-ILSVRC2012_val_00000074.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02966193-ILSVRC2012_val_00000074.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966687-ILSVRC2012_val_00001041.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966687-ILSVRC2012_val_00001041.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 120,992B, BPFP=0.2297 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 290,312B, BPFP=0.5510 +⌛️ [2/4] FRONTEND: Frontend time: 0.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.044s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12487827 13.02286333 + layer.39.0 254.07423773 4096.36929057 + ------------------------------------------------------------------------------------- + TOTAL 127.09955800 2054.69607695 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 411304 +BPFP 0.3903 bits/point +EBPFP 0.3903 equivalent bits/point +MSE 2054.696077 +---------------------- -------------------------------------------------------- +Time: 1.696s Load: 0.071s, Pack+Encode: 0.581s, Decode+Unpack: 1.044s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2054.6961 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966687-ILSVRC2012_val_00001041.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02966687-ILSVRC2012_val_00001041.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02971356-ILSVRC2012_val_00000019.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02971356-ILSVRC2012_val_00000019.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 109,868B, BPFP=0.2085 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 176,072B, BPFP=0.3342 +⌛️ [2/4] FRONTEND: Frontend time: 0.534s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.991s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09754465 13.14569572 + layer.39.0 24.51746044 2598.75655977 + ------------------------------------------------------------------------------------- + TOTAL 12.30750255 1305.95112774 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 285940 +BPFP 0.2714 bits/point +EBPFP 0.2714 equivalent bits/point +MSE 1305.951128 +---------------------- -------------------------------------------------------- +Time: 1.577s Load: 0.051s, Pack+Encode: 0.534s, Decode+Unpack: 0.991s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1305.9511 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02971356-ILSVRC2012_val_00000019.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02971356-ILSVRC2012_val_00000019.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02978881-ILSVRC2012_val_00000353.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02978881-ILSVRC2012_val_00000353.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 118,540B, BPFP=0.2250 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 220,136B, BPFP=0.4178 +⌛️ [2/4] FRONTEND: Frontend time: 0.578s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.035s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09975241 12.77758481 + layer.39.0 226.62124939 4122.32604470 + ------------------------------------------------------------------------------------- + TOTAL 113.36050090 2067.55181475 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 338676 +BPFP 0.3214 bits/point +EBPFP 0.3214 equivalent bits/point +MSE 2067.551815 +---------------------- -------------------------------------------------------- +Time: 1.663s Load: 0.050s, Pack+Encode: 0.578s, Decode+Unpack: 1.035s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2067.5518 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02978881-ILSVRC2012_val_00000353.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02978881-ILSVRC2012_val_00000353.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02980441-ILSVRC2012_val_00000122.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02980441-ILSVRC2012_val_00000122.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 113,532B, BPFP=0.2155 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 174,520B, BPFP=0.3313 +⌛️ [2/4] FRONTEND: Frontend time: 0.567s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.003s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10186533 13.14932106 + layer.39.0 8.25151846 2943.77016521 + ------------------------------------------------------------------------------------- + TOTAL 4.17669190 1478.45974313 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 288052 +BPFP 0.2734 bits/point +EBPFP 0.2734 equivalent bits/point +MSE 1478.459743 +---------------------- -------------------------------------------------------- +Time: 1.621s Load: 0.052s, Pack+Encode: 0.567s, Decode+Unpack: 1.003s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1478.4597 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02980441-ILSVRC2012_val_00000122.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02980441-ILSVRC2012_val_00000122.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02988304-ILSVRC2012_val_00003491.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02988304-ILSVRC2012_val_00003491.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 115,996B, BPFP=0.2202 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 190,240B, BPFP=0.3611 +⌛️ [2/4] FRONTEND: Frontend time: 0.537s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.993s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10176498 74.11170584 + layer.39.0 516.16180758 3701.31827017 + ------------------------------------------------------------------------------------- + TOTAL 258.13178628 1887.71498800 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 306236 +BPFP 0.2906 bits/point +EBPFP 0.2906 equivalent bits/point +MSE 1887.714988 +---------------------- -------------------------------------------------------- +Time: 1.581s Load: 0.051s, Pack+Encode: 0.537s, Decode+Unpack: 0.993s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1887.7150 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02988304-ILSVRC2012_val_00003491.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02988304-ILSVRC2012_val_00003491.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992211-ILSVRC2012_val_00000108.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992211-ILSVRC2012_val_00000108.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 119,644B, BPFP=0.2271 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 210,524B, BPFP=0.3996 +⌛️ [2/4] FRONTEND: Frontend time: 0.582s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.036s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10107529 13.15740403 + layer.39.0 89.13089923 3920.84936832 + ------------------------------------------------------------------------------------- + TOTAL 44.61598726 1967.00338618 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 330168 +BPFP 0.3133 bits/point +EBPFP 0.3133 equivalent bits/point +MSE 1967.003386 +---------------------- -------------------------------------------------------- +Time: 1.689s Load: 0.071s, Pack+Encode: 0.582s, Decode+Unpack: 1.036s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1967.0034 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992211-ILSVRC2012_val_00000108.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02992211-ILSVRC2012_val_00000108.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992529-ILSVRC2012_val_00000089.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992529-ILSVRC2012_val_00000089.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 119,948B, BPFP=0.2277 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 221,000B, BPFP=0.4195 +⌛️ [2/4] FRONTEND: Frontend time: 0.580s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.028s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11197385 13.07375657 + layer.39.0 964.25631681 4635.69144801 + ------------------------------------------------------------------------------------- + TOTAL 482.18414533 2324.38260229 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 340948 +BPFP 0.3236 bits/point +EBPFP 0.3236 equivalent bits/point +MSE 2324.382602 +---------------------- -------------------------------------------------------- +Time: 1.679s Load: 0.071s, Pack+Encode: 0.580s, Decode+Unpack: 1.028s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2324.3826 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992529-ILSVRC2012_val_00000089.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02992529-ILSVRC2012_val_00000089.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02999410-ILSVRC2012_val_00000376.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02999410-ILSVRC2012_val_00000376.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 120,680B, BPFP=0.2291 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 211,792B, BPFP=0.4020 +⌛️ [2/4] FRONTEND: Frontend time: 0.513s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.989s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10398186 13.81745114 + layer.39.0 145.78410471 3566.47084548 + ------------------------------------------------------------------------------------- + TOTAL 72.94404329 1790.14414831 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 332472 +BPFP 0.3155 bits/point +EBPFP 0.3155 equivalent bits/point +MSE 1790.144148 +---------------------- -------------------------------------------------------- +Time: 1.552s Load: 0.050s, Pack+Encode: 0.513s, Decode+Unpack: 0.989s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1790.1441 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02999410-ILSVRC2012_val_00000376.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02999410-ILSVRC2012_val_00000376.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000134-ILSVRC2012_val_00001094.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000134-ILSVRC2012_val_00001094.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 110,512B, BPFP=0.2098 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 234,488B, BPFP=0.4451 +⌛️ [2/4] FRONTEND: Frontend time: 0.512s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.970s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09696872 13.13365335 + layer.39.0 22.81329530 3830.45092323 + ------------------------------------------------------------------------------------- + TOTAL 11.45513201 1921.79228829 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 345000 +BPFP 0.3274 bits/point +EBPFP 0.3274 equivalent bits/point +MSE 1921.792288 +---------------------- -------------------------------------------------------- +Time: 1.531s Load: 0.050s, Pack+Encode: 0.512s, Decode+Unpack: 0.970s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1921.7923 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000134-ILSVRC2012_val_00001094.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03000134-ILSVRC2012_val_00001094.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000247-ILSVRC2012_val_00002280.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000247-ILSVRC2012_val_00002280.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 204,096B, BPFP=0.3874 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 189,932B, BPFP=0.3605 +⌛️ [2/4] FRONTEND: Frontend time: 0.563s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.045s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.29135144 112.84923166 + layer.39.0 428.26293732 3422.18172983 + ------------------------------------------------------------------------------------- + TOTAL 214.27714438 1767.51548075 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 394028 +BPFP 0.3739 bits/point +EBPFP 0.3739 equivalent bits/point +MSE 1767.515481 +---------------------- -------------------------------------------------------- +Time: 1.659s Load: 0.051s, Pack+Encode: 0.563s, Decode+Unpack: 1.045s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1767.5155 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000247-ILSVRC2012_val_00002280.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03000247-ILSVRC2012_val_00002280.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000684-ILSVRC2012_val_00000537.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000684-ILSVRC2012_val_00000537.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 130,160B, BPFP=0.2471 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 259,584B, BPFP=0.4927 +⌛️ [2/4] FRONTEND: Frontend time: 0.585s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.992s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13150742 98.85898779 + layer.39.0 55.24585459 3680.55247813 + ------------------------------------------------------------------------------------- + TOTAL 27.68868101 1889.70573296 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 389744 +BPFP 0.3699 bits/point +EBPFP 0.3699 equivalent bits/point +MSE 1889.705733 +---------------------- -------------------------------------------------------- +Time: 1.627s Load: 0.050s, Pack+Encode: 0.585s, Decode+Unpack: 0.992s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1889.7057 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000684-ILSVRC2012_val_00000537.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03000684-ILSVRC2012_val_00000537.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03014705-ILSVRC2012_val_00001168.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03014705-ILSVRC2012_val_00001168.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 99,408B, BPFP=0.1887 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 184,448B, BPFP=0.3501 +⌛️ [2/4] FRONTEND: Frontend time: 0.514s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.973s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09787338 13.02998208 + layer.39.0 322.89622813 3709.04154519 + ------------------------------------------------------------------------------------- + TOTAL 161.49705076 1861.03576364 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 283856 +BPFP 0.2694 bits/point +EBPFP 0.2694 equivalent bits/point +MSE 1861.035764 +---------------------- -------------------------------------------------------- +Time: 1.538s Load: 0.050s, Pack+Encode: 0.514s, Decode+Unpack: 0.973s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1861.0358 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03014705-ILSVRC2012_val_00001168.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03014705-ILSVRC2012_val_00001168.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03017168-ILSVRC2012_val_00001601.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03017168-ILSVRC2012_val_00001601.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 108,412B, BPFP=0.2058 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 202,380B, BPFP=0.3841 +⌛️ [2/4] FRONTEND: Frontend time: 0.526s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.039s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10213913 12.89595956 + layer.39.0 475.40952988 3990.05102041 + ------------------------------------------------------------------------------------- + TOTAL 237.75583451 2001.47348998 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 310792 +BPFP 0.2950 bits/point +EBPFP 0.2950 equivalent bits/point +MSE 2001.473490 +---------------------- -------------------------------------------------------- +Time: 1.635s Load: 0.070s, Pack+Encode: 0.526s, Decode+Unpack: 1.039s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2001.4735 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03017168-ILSVRC2012_val_00001601.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03017168-ILSVRC2012_val_00001601.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03018349-ILSVRC2012_val_00000346.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03018349-ILSVRC2012_val_00000346.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 113,524B, BPFP=0.2155 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 290,728B, BPFP=0.5518 +⌛️ [2/4] FRONTEND: Frontend time: 0.515s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.989s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09959339 12.89648248 + layer.39.0 56.59841169 3972.09135083 + ------------------------------------------------------------------------------------- + TOTAL 28.34900254 1992.49391665 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 404252 +BPFP 0.3837 bits/point +EBPFP 0.3837 equivalent bits/point +MSE 1992.493917 +---------------------- -------------------------------------------------------- +Time: 1.563s Load: 0.059s, Pack+Encode: 0.515s, Decode+Unpack: 0.989s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1992.4939 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03018349-ILSVRC2012_val_00000346.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03018349-ILSVRC2012_val_00000346.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03026506-ILSVRC2012_val_00001908.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03026506-ILSVRC2012_val_00001908.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.074s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 115,636B, BPFP=0.2195 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 245,760B, BPFP=0.4665 +⌛️ [2/4] FRONTEND: Frontend time: 0.573s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.025s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10977067 12.89654606 + layer.39.0 668.54063411 4291.63945578 + ------------------------------------------------------------------------------------- + TOTAL 334.32520239 2152.26800092 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 361396 +BPFP 0.3430 bits/point +EBPFP 0.3430 equivalent bits/point +MSE 2152.268001 +---------------------- -------------------------------------------------------- +Time: 1.673s Load: 0.074s, Pack+Encode: 0.573s, Decode+Unpack: 1.025s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2152.2680 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03026506-ILSVRC2012_val_00001908.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03026506-ILSVRC2012_val_00001908.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03028079-ILSVRC2012_val_00003351.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03028079-ILSVRC2012_val_00003351.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 116,208B, BPFP=0.2206 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 188,904B, BPFP=0.3586 +⌛️ [2/4] FRONTEND: Frontend time: 0.578s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.032s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10934904 13.29003527 + layer.39.0 15.31112010 3241.65621963 + ------------------------------------------------------------------------------------- + TOTAL 7.71023457 1627.47312745 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 305112 +BPFP 0.2896 bits/point +EBPFP 0.2896 equivalent bits/point +MSE 1627.473127 +---------------------- -------------------------------------------------------- +Time: 1.662s Load: 0.052s, Pack+Encode: 0.578s, Decode+Unpack: 1.032s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1627.4731 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03028079-ILSVRC2012_val_00003351.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03028079-ILSVRC2012_val_00003351.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03032252-ILSVRC2012_val_00000086.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03032252-ILSVRC2012_val_00000086.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 131,976B, BPFP=0.2505 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 214,104B, BPFP=0.4064 +⌛️ [2/4] FRONTEND: Frontend time: 0.590s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.012s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13507480 26.36100545 + layer.39.0 103.55165816 3512.66277940 + ------------------------------------------------------------------------------------- + TOTAL 51.84336648 1769.51189242 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 346080 +BPFP 0.3284 bits/point +EBPFP 0.3284 equivalent bits/point +MSE 1769.511892 +---------------------- -------------------------------------------------------- +Time: 1.653s Load: 0.051s, Pack+Encode: 0.590s, Decode+Unpack: 1.012s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1769.5119 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03032252-ILSVRC2012_val_00000086.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03032252-ILSVRC2012_val_00000086.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03041632-ILSVRC2012_val_00000564.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03041632-ILSVRC2012_val_00000564.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 107,504B, BPFP=0.2041 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 176,936B, BPFP=0.3358 +⌛️ [2/4] FRONTEND: Frontend time: 0.517s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.984s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10123130 13.11758800 + layer.39.0 371.34277818 3956.66034985 + ------------------------------------------------------------------------------------- + TOTAL 185.72200474 1984.88896892 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 284440 +BPFP 0.2699 bits/point +EBPFP 0.2699 equivalent bits/point +MSE 1984.888969 +---------------------- -------------------------------------------------------- +Time: 1.552s Load: 0.051s, Pack+Encode: 0.517s, Decode+Unpack: 0.984s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1984.8890 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03041632-ILSVRC2012_val_00000564.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03041632-ILSVRC2012_val_00000564.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03042490-ILSVRC2012_val_00001426.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03042490-ILSVRC2012_val_00001426.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 117,804B, BPFP=0.2236 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 188,756B, BPFP=0.3583 +⌛️ [2/4] FRONTEND: Frontend time: 0.586s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.046s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10706725 12.91656702 + layer.39.0 141.71039845 3405.03109815 + ------------------------------------------------------------------------------------- + TOTAL 70.90873285 1708.97383259 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 306560 +BPFP 0.2909 bits/point +EBPFP 0.2909 equivalent bits/point +MSE 1708.973833 +---------------------- -------------------------------------------------------- +Time: 1.683s Load: 0.051s, Pack+Encode: 0.586s, Decode+Unpack: 1.046s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1708.9738 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03042490-ILSVRC2012_val_00001426.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03042490-ILSVRC2012_val_00001426.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03047690-ILSVRC2012_val_00001500.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03047690-ILSVRC2012_val_00001500.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 115,092B, BPFP=0.2185 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 288,840B, BPFP=0.5482 +⌛️ [2/4] FRONTEND: Frontend time: 0.586s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.991s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09570478 12.94880117 + layer.39.0 226.76483540 4548.67978620 + ------------------------------------------------------------------------------------- + TOTAL 113.43027009 2280.81429369 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 403932 +BPFP 0.3833 bits/point +EBPFP 0.3833 equivalent bits/point +MSE 2280.814294 +---------------------- -------------------------------------------------------- +Time: 1.628s Load: 0.051s, Pack+Encode: 0.586s, Decode+Unpack: 0.991s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2280.8143 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03047690-ILSVRC2012_val_00001500.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03047690-ILSVRC2012_val_00001500.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03062245-ILSVRC2012_val_00000344.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03062245-ILSVRC2012_val_00000344.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 108,748B, BPFP=0.2064 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 193,276B, BPFP=0.3669 +⌛️ [2/4] FRONTEND: Frontend time: 0.532s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.988s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09619164 13.18104273 + layer.39.0 46.71096787 3234.02356657 + ------------------------------------------------------------------------------------- + TOTAL 23.40357976 1623.60230465 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 302024 +BPFP 0.2866 bits/point +EBPFP 0.2866 equivalent bits/point +MSE 1623.602305 +---------------------- -------------------------------------------------------- +Time: 1.572s Load: 0.052s, Pack+Encode: 0.532s, Decode+Unpack: 0.988s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1623.6023 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03062245-ILSVRC2012_val_00000344.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03062245-ILSVRC2012_val_00000344.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063599-ILSVRC2012_val_00000164.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063599-ILSVRC2012_val_00000164.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 117,460B, BPFP=0.2229 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 286,544B, BPFP=0.5439 +⌛️ [2/4] FRONTEND: Frontend time: 0.539s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.002s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10111790 12.87323858 + layer.39.0 9.80528160 3521.04227405 + ------------------------------------------------------------------------------------- + TOTAL 4.95319975 1766.95775632 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 404004 +BPFP 0.3834 bits/point +EBPFP 0.3834 equivalent bits/point +MSE 1766.957756 +---------------------- -------------------------------------------------------- +Time: 1.591s Load: 0.050s, Pack+Encode: 0.539s, Decode+Unpack: 1.002s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1766.9578 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063599-ILSVRC2012_val_00000164.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03063599-ILSVRC2012_val_00000164.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063689-ILSVRC2012_val_00001940.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063689-ILSVRC2012_val_00001940.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 116,220B, BPFP=0.2206 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 244,916B, BPFP=0.4649 +⌛️ [2/4] FRONTEND: Frontend time: 0.541s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.993s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09645106 13.00523680 + layer.39.0 18.48014797 4029.08989310 + ------------------------------------------------------------------------------------- + TOTAL 9.28829952 2021.04756495 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 361136 +BPFP 0.3427 bits/point +EBPFP 0.3427 equivalent bits/point +MSE 2021.047565 +---------------------- -------------------------------------------------------- +Time: 1.594s Load: 0.060s, Pack+Encode: 0.541s, Decode+Unpack: 0.993s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2021.0476 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063689-ILSVRC2012_val_00001940.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03063689-ILSVRC2012_val_00001940.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03065424-ILSVRC2012_val_00000915.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03065424-ILSVRC2012_val_00000915.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 134,464B, BPFP=0.2552 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 234,596B, BPFP=0.4453 +⌛️ [2/4] FRONTEND: Frontend time: 0.533s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.986s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12384982 13.31967190 + layer.39.0 2154.15986395 4717.54033042 + ------------------------------------------------------------------------------------- + TOTAL 1077.14185688 2365.43000116 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 369060 +BPFP 0.3503 bits/point +EBPFP 0.3503 equivalent bits/point +MSE 2365.430001 +---------------------- -------------------------------------------------------- +Time: 1.571s Load: 0.052s, Pack+Encode: 0.533s, Decode+Unpack: 0.986s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2365.4300 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03065424-ILSVRC2012_val_00000915.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03065424-ILSVRC2012_val_00000915.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03075370-ILSVRC2012_val_00004971.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03075370-ILSVRC2012_val_00004971.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 104,576B, BPFP=0.1985 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 203,624B, BPFP=0.3865 +⌛️ [2/4] FRONTEND: Frontend time: 0.578s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.011s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10672879 13.28786766 + layer.39.0 301.29020894 3348.84620991 + ------------------------------------------------------------------------------------- + TOTAL 150.69846886 1681.06703879 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 308200 +BPFP 0.2925 bits/point +EBPFP 0.2925 equivalent bits/point +MSE 1681.067039 +---------------------- -------------------------------------------------------- +Time: 1.640s Load: 0.051s, Pack+Encode: 0.578s, Decode+Unpack: 1.011s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1681.0670 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03075370-ILSVRC2012_val_00004971.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03075370-ILSVRC2012_val_00004971.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03089624-ILSVRC2012_val_00001190.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03089624-ILSVRC2012_val_00001190.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 115,404B, BPFP=0.2190 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 223,068B, BPFP=0.4234 +⌛️ [2/4] FRONTEND: Frontend time: 0.544s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.974s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10385029 37.19028259 + layer.39.0 606.38896987 3963.30344995 + ------------------------------------------------------------------------------------- + TOTAL 303.24641008 2000.24686627 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 338472 +BPFP 0.3212 bits/point +EBPFP 0.3212 equivalent bits/point +MSE 2000.246866 +---------------------- -------------------------------------------------------- +Time: 1.568s Load: 0.050s, Pack+Encode: 0.544s, Decode+Unpack: 0.974s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2000.2469 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03089624-ILSVRC2012_val_00001190.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03089624-ILSVRC2012_val_00001190.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03095699-ILSVRC2012_val_00000403.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03095699-ILSVRC2012_val_00000403.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 129,016B, BPFP=0.2449 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 203,784B, BPFP=0.3868 +⌛️ [2/4] FRONTEND: Frontend time: 0.529s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.031s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12139760 13.07531204 + layer.39.0 62.59250486 4088.45675413 + ------------------------------------------------------------------------------------- + TOTAL 31.35695123 2050.76603309 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 332800 +BPFP 0.3158 bits/point +EBPFP 0.3158 equivalent bits/point +MSE 2050.766033 +---------------------- -------------------------------------------------------- +Time: 1.611s Load: 0.051s, Pack+Encode: 0.529s, Decode+Unpack: 1.031s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2050.7660 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03095699-ILSVRC2012_val_00000403.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03095699-ILSVRC2012_val_00000403.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03100240-ILSVRC2012_val_00001201.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03100240-ILSVRC2012_val_00001201.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 113,172B, BPFP=0.2148 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 175,672B, BPFP=0.3334 +⌛️ [2/4] FRONTEND: Frontend time: 0.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.002s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10258218 12.88264832 + layer.39.0 42.98202138 3110.03449951 + ------------------------------------------------------------------------------------- + TOTAL 21.54230178 1561.45857392 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 288844 +BPFP 0.2741 bits/point +EBPFP 0.2741 equivalent bits/point +MSE 1561.458574 +---------------------- -------------------------------------------------------- +Time: 1.634s Load: 0.051s, Pack+Encode: 0.581s, Decode+Unpack: 1.002s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1561.4586 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03100240-ILSVRC2012_val_00001201.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03100240-ILSVRC2012_val_00001201.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03109150-ILSVRC2012_val_00000678.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03109150-ILSVRC2012_val_00000678.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 121,284B, BPFP=0.2302 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 216,116B, BPFP=0.4102 +⌛️ [2/4] FRONTEND: Frontend time: 0.568s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.993s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09720685 12.91749898 + layer.39.0 496.21158285 3980.26482021 + ------------------------------------------------------------------------------------- + TOTAL 248.15439485 1996.59115959 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 337400 +BPFP 0.3202 bits/point +EBPFP 0.3202 equivalent bits/point +MSE 1996.591160 +---------------------- -------------------------------------------------------- +Time: 1.612s Load: 0.050s, Pack+Encode: 0.568s, Decode+Unpack: 0.993s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1996.5912 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03109150-ILSVRC2012_val_00000678.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03109150-ILSVRC2012_val_00000678.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03110669-ILSVRC2012_val_00002171.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03110669-ILSVRC2012_val_00002171.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 139,036B, BPFP=0.2639 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 301,620B, BPFP=0.5725 +⌛️ [2/4] FRONTEND: Frontend time: 0.524s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.997s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.15128201 100.07555879 + layer.39.0 15.00769387 4001.94679300 + ------------------------------------------------------------------------------------- + TOTAL 7.57948794 2051.01117590 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 440656 +BPFP 0.4182 bits/point +EBPFP 0.4182 equivalent bits/point +MSE 2051.011176 +---------------------- -------------------------------------------------------- +Time: 1.571s Load: 0.051s, Pack+Encode: 0.524s, Decode+Unpack: 0.997s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2051.0112 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03110669-ILSVRC2012_val_00002171.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03110669-ILSVRC2012_val_00002171.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124043-ILSVRC2012_val_00000766.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124043-ILSVRC2012_val_00000766.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 117,848B, BPFP=0.2237 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 235,256B, BPFP=0.4465 +⌛️ [2/4] FRONTEND: Frontend time: 0.517s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.999s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11473456 13.37942063 + layer.39.0 54.83309418 4680.31827017 + ------------------------------------------------------------------------------------- + TOTAL 27.47391437 2346.84884540 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 353104 +BPFP 0.3351 bits/point +EBPFP 0.3351 equivalent bits/point +MSE 2346.848845 +---------------------- -------------------------------------------------------- +Time: 1.576s Load: 0.060s, Pack+Encode: 0.517s, Decode+Unpack: 0.999s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2346.8488 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124043-ILSVRC2012_val_00000766.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03124043-ILSVRC2012_val_00000766.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124170-ILSVRC2012_val_00001875.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124170-ILSVRC2012_val_00001875.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 113,516B, BPFP=0.2155 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 162,076B, BPFP=0.3076 +⌛️ [2/4] FRONTEND: Frontend time: 0.586s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.038s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11393612 13.01924939 + layer.39.0 9.06747107 3028.10325559 + ------------------------------------------------------------------------------------- + TOTAL 4.59070360 1520.56125249 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 275592 +BPFP 0.2615 bits/point +EBPFP 0.2615 equivalent bits/point +MSE 1520.561252 +---------------------- -------------------------------------------------------- +Time: 1.675s Load: 0.051s, Pack+Encode: 0.586s, Decode+Unpack: 1.038s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1520.5613 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124170-ILSVRC2012_val_00001875.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03124170-ILSVRC2012_val_00001875.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03126707-ILSVRC2012_val_00000020.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03126707-ILSVRC2012_val_00000020.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 122,172B, BPFP=0.2319 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 156,724B, BPFP=0.2975 +⌛️ [2/4] FRONTEND: Frontend time: 0.550s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.044s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.15273996 38.18792517 + layer.39.0 1033.15269679 3582.45869776 + ------------------------------------------------------------------------------------- + TOTAL 516.65271838 1810.32331147 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 278896 +BPFP 0.2647 bits/point +EBPFP 0.2647 equivalent bits/point +MSE 1810.323311 +---------------------- -------------------------------------------------------- +Time: 1.644s Load: 0.050s, Pack+Encode: 0.550s, Decode+Unpack: 1.044s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1810.3233 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03126707-ILSVRC2012_val_00000020.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03126707-ILSVRC2012_val_00000020.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03127747-ILSVRC2012_val_00001689.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03127747-ILSVRC2012_val_00001689.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 109,344B, BPFP=0.2075 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 201,944B, BPFP=0.3833 +⌛️ [2/4] FRONTEND: Frontend time: 0.505s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.994s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10152024 13.02899698 + layer.39.0 322.92343902 3497.44096210 + ------------------------------------------------------------------------------------- + TOTAL 161.51247963 1755.23497954 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 311288 +BPFP 0.2954 bits/point +EBPFP 0.2954 equivalent bits/point +MSE 1755.234980 +---------------------- -------------------------------------------------------- +Time: 1.548s Load: 0.050s, Pack+Encode: 0.505s, Decode+Unpack: 0.994s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1755.2350 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03127747-ILSVRC2012_val_00001689.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03127747-ILSVRC2012_val_00001689.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03131574-ILSVRC2012_val_00003036.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03131574-ILSVRC2012_val_00003036.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 109,440B, BPFP=0.2077 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 206,140B, BPFP=0.3913 +⌛️ [2/4] FRONTEND: Frontend time: 0.498s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.978s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09568423 12.99339278 + layer.39.0 163.24681122 4013.72400389 + ------------------------------------------------------------------------------------- + TOTAL 81.67124773 2013.35869833 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 315580 +BPFP 0.2995 bits/point +EBPFP 0.2995 equivalent bits/point +MSE 2013.358698 +---------------------- -------------------------------------------------------- +Time: 1.527s Load: 0.051s, Pack+Encode: 0.498s, Decode+Unpack: 0.978s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2013.3587 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03131574-ILSVRC2012_val_00003036.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03131574-ILSVRC2012_val_00003036.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03133878-ILSVRC2012_val_00000534.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03133878-ILSVRC2012_val_00000534.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.049s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 130,896B, BPFP=0.2485 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 252,852B, BPFP=0.4799 +⌛️ [2/4] FRONTEND: Frontend time: 0.522s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.015s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11186348 13.27994697 + layer.39.0 28.46096218 4211.13216715 + ------------------------------------------------------------------------------------- + TOTAL 14.28641283 2112.20605706 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 383748 +BPFP 0.3642 bits/point +EBPFP 0.3642 equivalent bits/point +MSE 2112.206057 +---------------------- -------------------------------------------------------- +Time: 1.587s Load: 0.049s, Pack+Encode: 0.522s, Decode+Unpack: 1.015s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2112.2061 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03133878-ILSVRC2012_val_00000534.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03133878-ILSVRC2012_val_00000534.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03134739-ILSVRC2012_val_00000249.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03134739-ILSVRC2012_val_00000249.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 112,288B, BPFP=0.2131 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 257,356B, BPFP=0.4885 +⌛️ [2/4] FRONTEND: Frontend time: 0.515s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.012s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09967384 13.10994158 + layer.39.0 372.24465500 3922.77721088 + ------------------------------------------------------------------------------------- + TOTAL 186.17216442 1967.94357623 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 369644 +BPFP 0.3508 bits/point +EBPFP 0.3508 equivalent bits/point +MSE 1967.943576 +---------------------- -------------------------------------------------------- +Time: 1.596s Load: 0.070s, Pack+Encode: 0.515s, Decode+Unpack: 1.012s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1967.9436 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03134739-ILSVRC2012_val_00000249.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03134739-ILSVRC2012_val_00000249.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03141823-ILSVRC2012_val_00001337.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03141823-ILSVRC2012_val_00001337.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 119,900B, BPFP=0.2276 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 349,332B, BPFP=0.6631 +⌛️ [2/4] FRONTEND: Frontend time: 0.587s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.045s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10422104 13.02379149 + layer.39.0 29.45558301 4130.52672498 + ------------------------------------------------------------------------------------- + TOTAL 14.77990203 2071.77525823 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 469232 +BPFP 0.4453 bits/point +EBPFP 0.4453 equivalent bits/point +MSE 2071.775258 +---------------------- -------------------------------------------------------- +Time: 1.703s Load: 0.070s, Pack+Encode: 0.587s, Decode+Unpack: 1.045s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2071.7753 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03141823-ILSVRC2012_val_00001337.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03141823-ILSVRC2012_val_00001337.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03160309-ILSVRC2012_val_00000330.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03160309-ILSVRC2012_val_00000330.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 104,704B, BPFP=0.1987 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 149,480B, BPFP=0.2837 +⌛️ [2/4] FRONTEND: Frontend time: 0.530s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.989s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09980877 13.06162783 + layer.39.0 30.04123011 2455.17541302 + ------------------------------------------------------------------------------------- + TOTAL 15.07051944 1234.11852043 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 254184 +BPFP 0.2412 bits/point +EBPFP 0.2412 equivalent bits/point +MSE 1234.118520 +---------------------- -------------------------------------------------------- +Time: 1.571s Load: 0.052s, Pack+Encode: 0.530s, Decode+Unpack: 0.989s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1234.1185 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03160309-ILSVRC2012_val_00000330.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03160309-ILSVRC2012_val_00000330.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03187595-ILSVRC2012_val_00000137.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03187595-ILSVRC2012_val_00000137.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 116,832B, BPFP=0.2218 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 235,948B, BPFP=0.4478 +⌛️ [2/4] FRONTEND: Frontend time: 0.560s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.987s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10716813 13.02910422 + layer.39.0 12.39187394 3915.74927114 + ------------------------------------------------------------------------------------- + TOTAL 6.24952103 1964.38918768 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 352780 +BPFP 0.3348 bits/point +EBPFP 0.3348 equivalent bits/point +MSE 1964.389188 +---------------------- -------------------------------------------------------- +Time: 1.599s Load: 0.052s, Pack+Encode: 0.560s, Decode+Unpack: 0.987s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1964.3892 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03187595-ILSVRC2012_val_00000137.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03187595-ILSVRC2012_val_00000137.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03188531-ILSVRC2012_val_00000493.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03188531-ILSVRC2012_val_00000493.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 111,332B, BPFP=0.2113 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 247,024B, BPFP=0.4689 +⌛️ [2/4] FRONTEND: Frontend time: 0.521s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.998s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09509044 13.19024178 + layer.39.0 10.77256154 3293.30077745 + ------------------------------------------------------------------------------------- + TOTAL 5.43382599 1653.24550962 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 358356 +BPFP 0.3401 bits/point +EBPFP 0.3401 equivalent bits/point +MSE 1653.245510 +---------------------- -------------------------------------------------------- +Time: 1.580s Load: 0.062s, Pack+Encode: 0.521s, Decode+Unpack: 0.998s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1653.2455 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03188531-ILSVRC2012_val_00000493.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03188531-ILSVRC2012_val_00000493.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03196217-ILSVRC2012_val_00003643.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03196217-ILSVRC2012_val_00003643.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 106,108B, BPFP=0.2014 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 232,840B, BPFP=0.4419 +⌛️ [2/4] FRONTEND: Frontend time: 0.524s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.984s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09478207 13.17524314 + layer.39.0 65.57403274 3546.78668610 + ------------------------------------------------------------------------------------- + TOTAL 32.83440740 1779.98096462 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 338948 +BPFP 0.3217 bits/point +EBPFP 0.3217 equivalent bits/point +MSE 1779.980965 +---------------------- -------------------------------------------------------- +Time: 1.558s Load: 0.051s, Pack+Encode: 0.524s, Decode+Unpack: 0.984s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1779.9810 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03196217-ILSVRC2012_val_00003643.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03196217-ILSVRC2012_val_00003643.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03201208-ILSVRC2012_val_00000241.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03201208-ILSVRC2012_val_00000241.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 118,220B, BPFP=0.2244 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 195,308B, BPFP=0.3707 +⌛️ [2/4] FRONTEND: Frontend time: 0.517s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.971s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10331685 25.39592824 + layer.39.0 136.59314261 3416.14601555 + ------------------------------------------------------------------------------------- + TOTAL 68.34822973 1720.77097189 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 313528 +BPFP 0.2976 bits/point +EBPFP 0.2976 equivalent bits/point +MSE 1720.770972 +---------------------- -------------------------------------------------------- +Time: 1.540s Load: 0.051s, Pack+Encode: 0.517s, Decode+Unpack: 0.971s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1720.7710 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03201208-ILSVRC2012_val_00000241.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03201208-ILSVRC2012_val_00000241.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03207743-ILSVRC2012_val_00000256.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03207743-ILSVRC2012_val_00000256.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 142,184B, BPFP=0.2699 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 193,656B, BPFP=0.3676 +⌛️ [2/4] FRONTEND: Frontend time: 0.564s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.024s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09674843 13.05902461 + layer.39.0 189.63590258 3459.25534500 + ------------------------------------------------------------------------------------- + TOTAL 94.86632550 1736.15718480 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 335840 +BPFP 0.3187 bits/point +EBPFP 0.3187 equivalent bits/point +MSE 1736.157185 +---------------------- -------------------------------------------------------- +Time: 1.640s Load: 0.051s, Pack+Encode: 0.564s, Decode+Unpack: 1.024s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1736.1572 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03207743-ILSVRC2012_val_00000256.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03207743-ILSVRC2012_val_00000256.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03216828-ILSVRC2012_val_00001729.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03216828-ILSVRC2012_val_00001729.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 117,120B, BPFP=0.2223 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 178,212B, BPFP=0.3383 +⌛️ [2/4] FRONTEND: Frontend time: 0.543s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.991s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10800209 37.65432535 + layer.39.0 31.30713223 3301.14844509 + ------------------------------------------------------------------------------------- + TOTAL 15.70756716 1669.40138522 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 295332 +BPFP 0.2803 bits/point +EBPFP 0.2803 equivalent bits/point +MSE 1669.401385 +---------------------- -------------------------------------------------------- +Time: 1.596s Load: 0.061s, Pack+Encode: 0.543s, Decode+Unpack: 0.991s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1669.4014 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03216828-ILSVRC2012_val_00001729.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03216828-ILSVRC2012_val_00001729.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03218198-ILSVRC2012_val_00002266.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03218198-ILSVRC2012_val_00002266.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 122,560B, BPFP=0.2326 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 282,828B, BPFP=0.5368 +⌛️ [2/4] FRONTEND: Frontend time: 0.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.016s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11617067 13.05940992 + layer.39.0 195.83184524 4188.44509232 + ------------------------------------------------------------------------------------- + TOTAL 97.97400795 2100.75225112 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 405388 +BPFP 0.3847 bits/point +EBPFP 0.3847 equivalent bits/point +MSE 2100.752251 +---------------------- -------------------------------------------------------- +Time: 1.667s Load: 0.070s, Pack+Encode: 0.581s, Decode+Unpack: 1.016s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2100.7523 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03218198-ILSVRC2012_val_00002266.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03218198-ILSVRC2012_val_00002266.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03220513-ILSVRC2012_val_00001868.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03220513-ILSVRC2012_val_00001868.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 196,012B, BPFP=0.3720 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 241,616B, BPFP=0.4586 +⌛️ [2/4] FRONTEND: Frontend time: 0.595s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.995s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.20032125 61.98359679 + layer.39.0 377.00176142 3238.48931001 + ------------------------------------------------------------------------------------- + TOTAL 188.60104134 1650.23645340 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 437628 +BPFP 0.4153 bits/point +EBPFP 0.4153 equivalent bits/point +MSE 1650.236453 +---------------------- -------------------------------------------------------- +Time: 1.641s Load: 0.051s, Pack+Encode: 0.595s, Decode+Unpack: 0.995s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1650.2365 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03220513-ILSVRC2012_val_00001868.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03220513-ILSVRC2012_val_00001868.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03223299-ILSVRC2012_val_00001893.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03223299-ILSVRC2012_val_00001893.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 111,388B, BPFP=0.2114 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 181,428B, BPFP=0.3444 +⌛️ [2/4] FRONTEND: Frontend time: 0.580s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.054s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10735053 13.01031037 + layer.39.0 354.51621720 2737.24003887 + ------------------------------------------------------------------------------------- + TOTAL 177.31178386 1375.12517462 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 292816 +BPFP 0.2779 bits/point +EBPFP 0.2779 equivalent bits/point +MSE 1375.125175 +---------------------- -------------------------------------------------------- +Time: 1.684s Load: 0.051s, Pack+Encode: 0.580s, Decode+Unpack: 1.054s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1375.1252 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03223299-ILSVRC2012_val_00001893.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03223299-ILSVRC2012_val_00001893.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03240683-ILSVRC2012_val_00000504.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03240683-ILSVRC2012_val_00000504.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 110,556B, BPFP=0.2098 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 198,760B, BPFP=0.3773 +⌛️ [2/4] FRONTEND: Frontend time: 0.539s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.006s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10065408 13.04439326 + layer.39.0 443.53838678 3912.56875607 + ------------------------------------------------------------------------------------- + TOTAL 221.81952043 1962.80657467 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 309316 +BPFP 0.2936 bits/point +EBPFP 0.2936 equivalent bits/point +MSE 1962.806575 +---------------------- -------------------------------------------------------- +Time: 1.594s Load: 0.050s, Pack+Encode: 0.539s, Decode+Unpack: 1.006s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1962.8066 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03240683-ILSVRC2012_val_00000504.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03240683-ILSVRC2012_val_00000504.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03250847-ILSVRC2012_val_00000542.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03250847-ILSVRC2012_val_00000542.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 112,848B, BPFP=0.2142 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 270,692B, BPFP=0.5138 +⌛️ [2/4] FRONTEND: Frontend time: 0.517s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.023s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10136319 49.91109770 + layer.39.0 140.24735787 4319.62730807 + ------------------------------------------------------------------------------------- + TOTAL 70.17436053 2184.76920288 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 383540 +BPFP 0.3640 bits/point +EBPFP 0.3640 equivalent bits/point +MSE 2184.769203 +---------------------- -------------------------------------------------------- +Time: 1.592s Load: 0.052s, Pack+Encode: 0.517s, Decode+Unpack: 1.023s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2184.7692 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03250847-ILSVRC2012_val_00000542.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03250847-ILSVRC2012_val_00000542.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03255030-ILSVRC2012_val_00001045.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03255030-ILSVRC2012_val_00001045.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 109,468B, BPFP=0.2078 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 198,588B, BPFP=0.3769 +⌛️ [2/4] FRONTEND: Frontend time: 0.518s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.002s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10050351 13.05948205 + layer.39.0 12.06722622 3078.59450923 + ------------------------------------------------------------------------------------- + TOTAL 6.08386487 1545.82699564 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 308056 +BPFP 0.2924 bits/point +EBPFP 0.2924 equivalent bits/point +MSE 1545.826996 +---------------------- -------------------------------------------------------- +Time: 1.579s Load: 0.059s, Pack+Encode: 0.518s, Decode+Unpack: 1.002s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1545.8270 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03255030-ILSVRC2012_val_00001045.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03255030-ILSVRC2012_val_00001045.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03271574-ILSVRC2012_val_00000942.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03271574-ILSVRC2012_val_00000942.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 116,896B, BPFP=0.2219 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 209,872B, BPFP=0.3984 +⌛️ [2/4] FRONTEND: Frontend time: 0.538s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.017s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10164264 13.00532981 + layer.39.0 660.63544704 3706.52380952 + ------------------------------------------------------------------------------------- + TOTAL 330.36854484 1859.76456967 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 326768 +BPFP 0.3101 bits/point +EBPFP 0.3101 equivalent bits/point +MSE 1859.764570 +---------------------- -------------------------------------------------------- +Time: 1.606s Load: 0.051s, Pack+Encode: 0.538s, Decode+Unpack: 1.017s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1859.7646 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03271574-ILSVRC2012_val_00000942.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03271574-ILSVRC2012_val_00000942.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272010-ILSVRC2012_val_00000374.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272010-ILSVRC2012_val_00000374.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 113,560B, BPFP=0.2155 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 253,144B, BPFP=0.4805 +⌛️ [2/4] FRONTEND: Frontend time: 0.517s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.032s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10420663 13.30983130 + layer.39.0 9.63653369 3677.44047619 + ------------------------------------------------------------------------------------- + TOTAL 4.87037016 1845.37515374 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 366704 +BPFP 0.3480 bits/point +EBPFP 0.3480 equivalent bits/point +MSE 1845.375154 +---------------------- -------------------------------------------------------- +Time: 1.601s Load: 0.052s, Pack+Encode: 0.517s, Decode+Unpack: 1.032s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1845.3752 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272010-ILSVRC2012_val_00000374.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03272010-ILSVRC2012_val_00000374.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272562-ILSVRC2012_val_00001699.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272562-ILSVRC2012_val_00001699.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 123,328B, BPFP=0.2341 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 181,660B, BPFP=0.3448 +⌛️ [2/4] FRONTEND: Frontend time: 0.582s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.057s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11399285 12.95144899 + layer.39.0 12.79457642 3129.40840622 + ------------------------------------------------------------------------------------- + TOTAL 6.45428464 1571.17992761 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 304988 +BPFP 0.2894 bits/point +EBPFP 0.2894 equivalent bits/point +MSE 1571.179928 +---------------------- -------------------------------------------------------- +Time: 1.689s Load: 0.050s, Pack+Encode: 0.582s, Decode+Unpack: 1.057s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1571.1799 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272562-ILSVRC2012_val_00001699.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03272562-ILSVRC2012_val_00001699.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03290653-ILSVRC2012_val_00000199.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03290653-ILSVRC2012_val_00000199.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 113,648B, BPFP=0.2157 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 213,460B, BPFP=0.4052 +⌛️ [2/4] FRONTEND: Frontend time: 0.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.032s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09581849 12.90261423 + layer.39.0 9.30266794 3261.77040816 + ------------------------------------------------------------------------------------- + TOTAL 4.69924322 1637.33651119 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 327108 +BPFP 0.3104 bits/point +EBPFP 0.3104 equivalent bits/point +MSE 1637.336511 +---------------------- -------------------------------------------------------- +Time: 1.664s Load: 0.051s, Pack+Encode: 0.581s, Decode+Unpack: 1.032s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1637.3365 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03290653-ILSVRC2012_val_00000199.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03290653-ILSVRC2012_val_00000199.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03291819-ILSVRC2012_val_00000419.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03291819-ILSVRC2012_val_00000419.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 102,152B, BPFP=0.1939 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 194,712B, BPFP=0.3696 +⌛️ [2/4] FRONTEND: Frontend time: 0.563s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.975s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10621172 13.28873318 + layer.39.0 31.36357166 2909.22740525 + ------------------------------------------------------------------------------------- + TOTAL 15.73489169 1461.25806922 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 296864 +BPFP 0.2817 bits/point +EBPFP 0.2817 equivalent bits/point +MSE 1461.258069 +---------------------- -------------------------------------------------------- +Time: 1.589s Load: 0.051s, Pack+Encode: 0.563s, Decode+Unpack: 0.975s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1461.2581 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03291819-ILSVRC2012_val_00000419.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03291819-ILSVRC2012_val_00000419.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03314780-ILSVRC2012_val_00000624.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03314780-ILSVRC2012_val_00000624.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 116,304B, BPFP=0.2208 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 248,380B, BPFP=0.4714 +⌛️ [2/4] FRONTEND: Frontend time: 0.543s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.996s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10172509 12.89812432 + layer.39.0 35.60390853 4316.89358601 + ------------------------------------------------------------------------------------- + TOTAL 17.85281681 2164.89585516 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 364684 +BPFP 0.3461 bits/point +EBPFP 0.3461 equivalent bits/point +MSE 2164.895855 +---------------------- -------------------------------------------------------- +Time: 1.589s Load: 0.050s, Pack+Encode: 0.543s, Decode+Unpack: 0.996s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2164.8959 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03314780-ILSVRC2012_val_00000624.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03314780-ILSVRC2012_val_00000624.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03325584-ILSVRC2012_val_00001256.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03325584-ILSVRC2012_val_00001256.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 123,744B, BPFP=0.2349 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 254,772B, BPFP=0.4836 +⌛️ [2/4] FRONTEND: Frontend time: 0.515s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.993s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11348933 13.16618645 + layer.39.0 26.85401292 3891.60957240 + ------------------------------------------------------------------------------------- + TOTAL 13.48375113 1952.38787943 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 378516 +BPFP 0.3592 bits/point +EBPFP 0.3592 equivalent bits/point +MSE 1952.387879 +---------------------- -------------------------------------------------------- +Time: 1.560s Load: 0.052s, Pack+Encode: 0.515s, Decode+Unpack: 0.993s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1952.3879 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03325584-ILSVRC2012_val_00001256.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03325584-ILSVRC2012_val_00001256.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03337140-ILSVRC2012_val_00000132.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03337140-ILSVRC2012_val_00000132.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 112,576B, BPFP=0.2137 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 205,656B, BPFP=0.3904 +⌛️ [2/4] FRONTEND: Frontend time: 0.566s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.003s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09852950 13.07473028 + layer.39.0 10.39905343 3561.22764820 + ------------------------------------------------------------------------------------- + TOTAL 5.24879146 1787.15118924 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 318232 +BPFP 0.3020 bits/point +EBPFP 0.3020 equivalent bits/point +MSE 1787.151189 +---------------------- -------------------------------------------------------- +Time: 1.620s Load: 0.051s, Pack+Encode: 0.566s, Decode+Unpack: 1.003s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1787.1512 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03337140-ILSVRC2012_val_00000132.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03337140-ILSVRC2012_val_00000132.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03344393-ILSVRC2012_val_00000288.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03344393-ILSVRC2012_val_00000288.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 106,720B, BPFP=0.2026 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 197,156B, BPFP=0.3742 +⌛️ [2/4] FRONTEND: Frontend time: 0.587s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.997s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09830858 13.15230484 + layer.39.0 109.00505649 3381.99101069 + ------------------------------------------------------------------------------------- + TOTAL 54.55168253 1697.57165776 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 303876 +BPFP 0.2884 bits/point +EBPFP 0.2884 equivalent bits/point +MSE 1697.571658 +---------------------- -------------------------------------------------------- +Time: 1.654s Load: 0.070s, Pack+Encode: 0.587s, Decode+Unpack: 0.997s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1697.5717 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03344393-ILSVRC2012_val_00000288.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03344393-ILSVRC2012_val_00000288.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03345487-ILSVRC2012_val_00000764.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03345487-ILSVRC2012_val_00000764.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 114,808B, BPFP=0.2179 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 209,608B, BPFP=0.3979 +⌛️ [2/4] FRONTEND: Frontend time: 0.575s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.025s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10639974 13.22144148 + layer.39.0 14.55993569 3642.70068027 + ------------------------------------------------------------------------------------- + TOTAL 7.33316771 1827.96106088 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 324416 +BPFP 0.3079 bits/point +EBPFP 0.3079 equivalent bits/point +MSE 1827.961061 +---------------------- -------------------------------------------------------- +Time: 1.652s Load: 0.051s, Pack+Encode: 0.575s, Decode+Unpack: 1.025s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1827.9611 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03345487-ILSVRC2012_val_00000764.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03345487-ILSVRC2012_val_00000764.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03347037-ILSVRC2012_val_00000743.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03347037-ILSVRC2012_val_00000743.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 137,668B, BPFP=0.2613 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 215,500B, BPFP=0.4090 +⌛️ [2/4] FRONTEND: Frontend time: 0.543s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.017s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14351733 13.28838583 + layer.39.0 355.98426871 3531.96258503 + ------------------------------------------------------------------------------------- + TOTAL 178.06389302 1772.62548543 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 353168 +BPFP 0.3352 bits/point +EBPFP 0.3352 equivalent bits/point +MSE 1772.625485 +---------------------- -------------------------------------------------------- +Time: 1.621s Load: 0.061s, Pack+Encode: 0.543s, Decode+Unpack: 1.017s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1772.6255 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03347037-ILSVRC2012_val_00000743.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03347037-ILSVRC2012_val_00000743.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03355925-ILSVRC2012_val_00000445.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03355925-ILSVRC2012_val_00000445.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 103,184B, BPFP=0.1959 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 155,280B, BPFP=0.2947 +⌛️ [2/4] FRONTEND: Frontend time: 0.533s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.994s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09979894 13.24934232 + layer.39.0 9.06502540 3084.97400389 + ------------------------------------------------------------------------------------- + TOTAL 4.58241217 1549.11167310 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 258464 +BPFP 0.2453 bits/point +EBPFP 0.2453 equivalent bits/point +MSE 1549.111673 +---------------------- -------------------------------------------------------- +Time: 1.577s Load: 0.050s, Pack+Encode: 0.533s, Decode+Unpack: 0.994s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1549.1117 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03355925-ILSVRC2012_val_00000445.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03355925-ILSVRC2012_val_00000445.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03376595-ILSVRC2012_val_00001616.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03376595-ILSVRC2012_val_00001616.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 123,060B, BPFP=0.2336 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 217,588B, BPFP=0.4130 +⌛️ [2/4] FRONTEND: Frontend time: 0.514s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.982s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09988844 12.92427892 + layer.39.0 1408.20760447 4545.82701652 + ------------------------------------------------------------------------------------- + TOTAL 704.15374646 2279.37564772 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 340648 +BPFP 0.3233 bits/point +EBPFP 0.3233 equivalent bits/point +MSE 2279.375648 +---------------------- -------------------------------------------------------- +Time: 1.549s Load: 0.052s, Pack+Encode: 0.514s, Decode+Unpack: 0.982s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2279.3756 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03376595-ILSVRC2012_val_00001616.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03376595-ILSVRC2012_val_00001616.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03379051-ILSVRC2012_val_00002562.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03379051-ILSVRC2012_val_00002562.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 121,936B, BPFP=0.2314 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 244,340B, BPFP=0.4638 +⌛️ [2/4] FRONTEND: Frontend time: 0.593s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.046s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10889592 13.04302190 + layer.39.0 102.95462828 4567.45481050 + ------------------------------------------------------------------------------------- + TOTAL 51.53176210 2290.24891620 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 366276 +BPFP 0.3476 bits/point +EBPFP 0.3476 equivalent bits/point +MSE 2290.248916 +---------------------- -------------------------------------------------------- +Time: 1.690s Load: 0.051s, Pack+Encode: 0.593s, Decode+Unpack: 1.046s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2290.2489 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03379051-ILSVRC2012_val_00002562.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03379051-ILSVRC2012_val_00002562.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388043-ILSVRC2012_val_00001018.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388043-ILSVRC2012_val_00001018.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 106,324B, BPFP=0.2018 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 177,824B, BPFP=0.3375 +⌛️ [2/4] FRONTEND: Frontend time: 0.579s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.053s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09747427 13.07635504 + layer.39.0 21.12933142 2913.45772595 + ------------------------------------------------------------------------------------- + TOTAL 10.61340285 1463.26704049 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 284148 +BPFP 0.2697 bits/point +EBPFP 0.2697 equivalent bits/point +MSE 1463.267040 +---------------------- -------------------------------------------------------- +Time: 1.683s Load: 0.051s, Pack+Encode: 0.579s, Decode+Unpack: 1.053s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1463.2670 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388043-ILSVRC2012_val_00001018.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03388043-ILSVRC2012_val_00001018.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388183-ILSVRC2012_val_00002799.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388183-ILSVRC2012_val_00002799.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 116,636B, BPFP=0.2214 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 213,976B, BPFP=0.4061 +⌛️ [2/4] FRONTEND: Frontend time: 0.566s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.053s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10066175 13.06453759 + layer.39.0 786.68810739 4279.62585034 + ------------------------------------------------------------------------------------- + TOTAL 393.39438457 2146.34519396 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 330612 +BPFP 0.3138 bits/point +EBPFP 0.3138 equivalent bits/point +MSE 2146.345194 +---------------------- -------------------------------------------------------- +Time: 1.669s Load: 0.051s, Pack+Encode: 0.566s, Decode+Unpack: 1.053s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2146.3452 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388183-ILSVRC2012_val_00002799.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03388183-ILSVRC2012_val_00002799.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388549-ILSVRC2012_val_00002945.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388549-ILSVRC2012_val_00002945.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 114,116B, BPFP=0.2166 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 208,368B, BPFP=0.3955 +⌛️ [2/4] FRONTEND: Frontend time: 0.598s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.051s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09849939 13.02852056 + layer.39.0 10.79426799 3158.34402332 + ------------------------------------------------------------------------------------- + TOTAL 5.44638369 1585.68627194 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 322484 +BPFP 0.3061 bits/point +EBPFP 0.3061 equivalent bits/point +MSE 1585.686272 +---------------------- -------------------------------------------------------- +Time: 1.720s Load: 0.071s, Pack+Encode: 0.598s, Decode+Unpack: 1.051s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1585.6863 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388549-ILSVRC2012_val_00002945.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03388549-ILSVRC2012_val_00002945.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03393912-ILSVRC2012_val_00000047.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03393912-ILSVRC2012_val_00000047.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 109,912B, BPFP=0.2086 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 185,204B, BPFP=0.3515 +⌛️ [2/4] FRONTEND: Frontend time: 0.564s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.039s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09729456 12.98761407 + layer.39.0 38.26720800 3456.21404276 + ------------------------------------------------------------------------------------- + TOTAL 19.18225128 1734.60082842 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 295116 +BPFP 0.2801 bits/point +EBPFP 0.2801 equivalent bits/point +MSE 1734.600828 +---------------------- -------------------------------------------------------- +Time: 1.654s Load: 0.050s, Pack+Encode: 0.564s, Decode+Unpack: 1.039s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1734.6008 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03393912-ILSVRC2012_val_00000047.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03393912-ILSVRC2012_val_00000047.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03394916-ILSVRC2012_val_00000957.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03394916-ILSVRC2012_val_00000957.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 107,812B, BPFP=0.2046 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 210,064B, BPFP=0.3987 +⌛️ [2/4] FRONTEND: Frontend time: 0.557s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.032s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10421823 13.26333686 + layer.39.0 9.72561820 3662.89091351 + ------------------------------------------------------------------------------------- + TOTAL 4.91491822 1838.07712519 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 317876 +BPFP 0.3017 bits/point +EBPFP 0.3017 equivalent bits/point +MSE 1838.077125 +---------------------- -------------------------------------------------------- +Time: 1.639s Load: 0.051s, Pack+Encode: 0.557s, Decode+Unpack: 1.032s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1838.0771 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03394916-ILSVRC2012_val_00000957.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03394916-ILSVRC2012_val_00000957.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03404251-ILSVRC2012_val_00000641.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03404251-ILSVRC2012_val_00000641.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 113,492B, BPFP=0.2154 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 230,976B, BPFP=0.4384 +⌛️ [2/4] FRONTEND: Frontend time: 0.555s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.046s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10764784 12.99635474 + layer.39.0 585.45553936 4102.22789116 + ------------------------------------------------------------------------------------- + TOTAL 292.78159360 2057.61212295 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 344468 +BPFP 0.3269 bits/point +EBPFP 0.3269 equivalent bits/point +MSE 2057.612123 +---------------------- -------------------------------------------------------- +Time: 1.651s Load: 0.050s, Pack+Encode: 0.555s, Decode+Unpack: 1.046s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2057.6121 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03404251-ILSVRC2012_val_00000641.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03404251-ILSVRC2012_val_00000641.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03417042-ILSVRC2012_val_00001144.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03417042-ILSVRC2012_val_00001144.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 112,376B, BPFP=0.2133 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 201,024B, BPFP=0.3816 +⌛️ [2/4] FRONTEND: Frontend time: 0.587s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.010s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10091509 12.98103628 + layer.39.0 202.93364310 3700.51895044 + ------------------------------------------------------------------------------------- + TOTAL 101.51727910 1856.74999336 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 313400 +BPFP 0.2974 bits/point +EBPFP 0.2974 equivalent bits/point +MSE 1856.749993 +---------------------- -------------------------------------------------------- +Time: 1.647s Load: 0.050s, Pack+Encode: 0.587s, Decode+Unpack: 1.010s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1856.7500 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03417042-ILSVRC2012_val_00001144.zst + to output-fixed/dinov3-total/lambda0.004/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03417042-ILSVRC2012_val_00001144.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 0.3167 bits/point +Avg EBPFP 0.3167 equivalent bits/point +Avg MSE 1832.856597 +Avg Time 1.625s +------------------------ ---------------------------- diff --git a/lambda0.007/elic-featurecoding-8bit-individual/cls_in1ka/dtufc_elic-featurecoding_dinov3-total_individual.log b/lambda0.007/elic-featurecoding-8bit-individual/cls_in1ka/dtufc_elic-featurecoding_dinov3-total_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..088adbc8da495fb30e745d6f001a5eb1f5514c64 --- /dev/null +++ b/lambda0.007/elic-featurecoding-8bit-individual/cls_in1ka/dtufc_elic-featurecoding_dinov3-total_individual.log @@ -0,0 +1,9344 @@ +Experiment: dtufc_elic-featurecoding_dinov3-total_individual +Log file: output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/dtufc_elic-featurecoding_dinov3-total_individual.log +DTUFCCodecConfig: + arch: elic-featurecoding + handler: dinov3-total + checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.007_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.007_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 333 +Loaded elic-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.9' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.19' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.29' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.39' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json +Loaded per-key mappings: model=dinov3-total + Keys: ['layer.9', 'layer.19', 'layer.29', 'layer.39'] +---------------- -------------------------------------------------------------------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +Checkpoint codec_weights/elic_hybrid/elic2022-official_lambda0.007_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-DINOv3/imagenet1k-a +Output output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka +---------------- -------------------------------------------------------------------------------------------------------------------- +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01498041-0.006639_koala _ American bullfrog_0.6658246.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01498041-0.006639_koala _ American bullfrog_0.6658246.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 201,852B, BPFP=0.3831 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 305,048B, BPFP=0.5790 +⌛️ [2/4] FRONTEND: Frontend time: 2.965s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.543s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09594801 12.55126146 + layer.39.0 58.94484178 4041.34086492 + ------------------------------------------------------------------------------------- + TOTAL 29.52039490 2026.94606319 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 506900 +BPFP 0.4811 bits/point +EBPFP 0.4811 equivalent bits/point +MSE 2026.946063 +---------------------- -------------------------------------------------------- +Time: 5.580s Load: 0.072s, Pack+Encode: 2.965s, Decode+Unpack: 2.543s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2026.9461 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01498041-0.006639_koala _ American bullfrog_0.6658246.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n01498041-0.006639_koala _ American bullfrog_0.6658246.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01531178-0.000765_fox squirrel _ ant_0.9486805.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01531178-0.000765_fox squirrel _ ant_0.9486805.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 189,924B, BPFP=0.3605 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 310,084B, BPFP=0.5886 +⌛️ [2/4] FRONTEND: Frontend time: 2.611s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.518s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09773727 12.38377388 + layer.39.0 17.17825445 2535.16982507 + ------------------------------------------------------------------------------------- + TOTAL 8.63799586 1273.77679948 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 500008 +BPFP 0.4745 bits/point +EBPFP 0.4745 equivalent bits/point +MSE 1273.776799 +---------------------- -------------------------------------------------------- +Time: 5.198s Load: 0.069s, Pack+Encode: 2.611s, Decode+Unpack: 2.518s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1273.7768 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01531178-0.000765_fox squirrel _ ant_0.9486805.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n01531178-0.000765_fox squirrel _ ant_0.9486805.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01534433-0.004573_stingray _ stingray_0.97124094.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01534433-0.004573_stingray _ stingray_0.97124094.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 126,720B, BPFP=0.2405 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 235,968B, BPFP=0.4479 +⌛️ [2/4] FRONTEND: Frontend time: 2.622s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.501s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09515371 12.44973882 + layer.39.0 6.87362484 1471.48627308 + ------------------------------------------------------------------------------------- + TOTAL 3.48438928 741.96800595 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 362688 +BPFP 0.3442 bits/point +EBPFP 0.3442 equivalent bits/point +MSE 741.968006 +---------------------- -------------------------------------------------------- +Time: 5.183s Load: 0.061s, Pack+Encode: 2.622s, Decode+Unpack: 2.501s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 741.9680 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01534433-0.004573_stingray _ stingray_0.97124094.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n01534433-0.004573_stingray _ stingray_0.97124094.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01558993-0.000522_bow _ bow_0.9033333.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01558993-0.000522_bow _ bow_0.9033333.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 226,512B, BPFP=0.4299 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 236,836B, BPFP=0.4495 +⌛️ [2/4] FRONTEND: Frontend time: 2.605s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.490s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09874929 48.95881165 + layer.39.0 7.31778236 1364.14334305 + ------------------------------------------------------------------------------------- + TOTAL 3.70826583 706.55107735 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 463348 +BPFP 0.4397 bits/point +EBPFP 0.4397 equivalent bits/point +MSE 706.551077 +---------------------- -------------------------------------------------------- +Time: 5.167s Load: 0.072s, Pack+Encode: 2.605s, Decode+Unpack: 2.490s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 706.5511 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01558993-0.000522_bow _ bow_0.9033333.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n01558993-0.000522_bow _ bow_0.9033333.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01580077-0.004583_African bush elephant _ African bush elephant_0.59939015.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01580077-0.004583_African bush elephant _ African bush elephant_0.59939015.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 204,436B, BPFP=0.3880 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 307,352B, BPFP=0.5834 +⌛️ [2/4] FRONTEND: Frontend time: 2.606s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.500s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10720986 25.81062470 + layer.39.0 24.46209533 2436.01579203 + ------------------------------------------------------------------------------------- + TOTAL 12.28465260 1230.91320836 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 511788 +BPFP 0.4857 bits/point +EBPFP 0.4857 equivalent bits/point +MSE 1230.913208 +---------------------- -------------------------------------------------------- +Time: 5.157s Load: 0.052s, Pack+Encode: 2.606s, Decode+Unpack: 2.500s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1230.9132 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01580077-0.004583_African bush elephant _ African bush elephant_0.59939015.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n01580077-0.004583_African bush elephant _ African bush elephant_0.59939015.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01614925-0.049724_white-headed capuchin _ white-headed capuchin_0.9309422.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01614925-0.049724_white-headed capuchin _ white-headed capuchin_0.9309422.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 230,296B, BPFP=0.4371 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 246,624B, BPFP=0.4681 +⌛️ [2/4] FRONTEND: Frontend time: 2.613s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.492s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09739119 12.86861581 + layer.39.0 8.81423010 1633.88921283 + ------------------------------------------------------------------------------------- + TOTAL 4.45581065 823.37891432 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 476920 +BPFP 0.4526 bits/point +EBPFP 0.4526 equivalent bits/point +MSE 823.378914 +---------------------- -------------------------------------------------------- +Time: 5.156s Load: 0.051s, Pack+Encode: 2.613s, Decode+Unpack: 2.492s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 823.3789 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01614925-0.049724_white-headed capuchin _ white-headed capuchin_0.9309422.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n01614925-0.049724_white-headed capuchin _ white-headed capuchin_0.9309422.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01631663-0.004606_American bullfrog _ American bullfrog_0.8789855.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01631663-0.004606_American bullfrog _ American bullfrog_0.8789855.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 168,028B, BPFP=0.3189 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 290,180B, BPFP=0.5508 +⌛️ [2/4] FRONTEND: Frontend time: 2.624s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.513s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09716670 12.89093059 + layer.39.0 20.45897868 1880.04907677 + ------------------------------------------------------------------------------------- + TOTAL 10.27807269 946.47000368 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 458208 +BPFP 0.4349 bits/point +EBPFP 0.4349 equivalent bits/point +MSE 946.470004 +---------------------- -------------------------------------------------------- +Time: 5.188s Load: 0.051s, Pack+Encode: 2.624s, Decode+Unpack: 2.513s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 946.4700 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01631663-0.004606_American bullfrog _ American bullfrog_0.8789855.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n01631663-0.004606_American bullfrog _ American bullfrog_0.8789855.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01669191-0.029754_sandal _ sandal_0.38198605.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01669191-0.029754_sandal _ sandal_0.38198605.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.056s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 282,676B, BPFP=0.5365 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 246,328B, BPFP=0.4676 +⌛️ [2/4] FRONTEND: Frontend time: 2.621s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.502s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10877632 67.35840318 + layer.39.0 13.16500205 1458.01081147 + ------------------------------------------------------------------------------------- + TOTAL 6.63688918 762.68460733 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 529004 +BPFP 0.5020 bits/point +EBPFP 0.5020 equivalent bits/point +MSE 762.684607 +---------------------- -------------------------------------------------------- +Time: 5.179s Load: 0.056s, Pack+Encode: 2.621s, Decode+Unpack: 2.502s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 762.6846 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01669191-0.029754_sandal _ sandal_0.38198605.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n01669191-0.029754_sandal _ sandal_0.38198605.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770081-0.000571_syringe _ syringe_0.7369336.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770081-0.000571_syringe _ syringe_0.7369336.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 147,280B, BPFP=0.2795 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 322,920B, BPFP=0.6129 +⌛️ [2/4] FRONTEND: Frontend time: 2.612s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.496s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09508557 0.87183187 + layer.39.0 60.03878538 1947.22242468 + ------------------------------------------------------------------------------------- + TOTAL 30.06693547 974.04712828 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 470200 +BPFP 0.4462 bits/point +EBPFP 0.4462 equivalent bits/point +MSE 974.047128 +---------------------- -------------------------------------------------------- +Time: 5.170s Load: 0.061s, Pack+Encode: 2.612s, Decode+Unpack: 2.496s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 974.0471 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770081-0.000571_syringe _ syringe_0.7369336.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n01770081-0.000571_syringe _ syringe_0.7369336.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770393-0.000908_harvestman _ harvestman_0.8782497.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770393-0.000908_harvestman _ harvestman_0.8782497.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 204,428B, BPFP=0.3880 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 308,552B, BPFP=0.5857 +⌛️ [2/4] FRONTEND: Frontend time: 2.615s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.506s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11350316 1.74604499 + layer.39.0 19.73148992 2088.80417881 + ------------------------------------------------------------------------------------- + TOTAL 9.92249654 1045.27511190 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 512980 +BPFP 0.4868 bits/point +EBPFP 0.4868 equivalent bits/point +MSE 1045.275112 +---------------------- -------------------------------------------------------- +Time: 5.173s Load: 0.052s, Pack+Encode: 2.615s, Decode+Unpack: 2.506s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1045.2751 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770393-0.000908_harvestman _ harvestman_0.8782497.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n01770393-0.000908_harvestman _ harvestman_0.8782497.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01784675-0.027853_syringe _ syringe_0.9584382.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01784675-0.027853_syringe _ syringe_0.9584382.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 264,980B, BPFP=0.5030 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 279,596B, BPFP=0.5307 +⌛️ [2/4] FRONTEND: Frontend time: 2.619s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.491s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11002613 53.26167699 + layer.39.0 26.08665877 6487.16375121 + ------------------------------------------------------------------------------------- + TOTAL 13.09834245 3270.21271410 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 544576 +BPFP 0.5168 bits/point +EBPFP 0.5168 equivalent bits/point +MSE 3270.212714 +---------------------- -------------------------------------------------------- +Time: 5.172s Load: 0.062s, Pack+Encode: 2.619s, Decode+Unpack: 2.491s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3270.2127 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01784675-0.027853_syringe _ syringe_0.9584382.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n01784675-0.027853_syringe _ syringe_0.9584382.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01819313-0.053742_koala _ koala_0.98647016.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01819313-0.053742_koala _ koala_0.98647016.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 259,116B, BPFP=0.4918 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 336,264B, BPFP=0.6383 +⌛️ [2/4] FRONTEND: Frontend time: 2.606s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.506s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14565475 78.29546283 + layer.39.0 25.01023445 4128.47716229 + ------------------------------------------------------------------------------------- + TOTAL 12.57794460 2103.38631256 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 595380 +BPFP 0.5650 bits/point +EBPFP 0.5650 equivalent bits/point +MSE 2103.386313 +---------------------- -------------------------------------------------------- +Time: 5.173s Load: 0.061s, Pack+Encode: 2.606s, Decode+Unpack: 2.506s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2103.3863 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01819313-0.053742_koala _ koala_0.98647016.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n01819313-0.053742_koala _ koala_0.98647016.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01820546-0.012522_toucan _ toucan_0.63882655.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01820546-0.012522_toucan _ toucan_0.63882655.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 185,872B, BPFP=0.3528 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 290,608B, BPFP=0.5516 +⌛️ [2/4] FRONTEND: Frontend time: 2.605s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.516s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09696376 25.06373185 + layer.39.0 16.65489097 2709.10544218 + ------------------------------------------------------------------------------------- + TOTAL 8.37592737 1367.08458702 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 476480 +BPFP 0.4522 bits/point +EBPFP 0.4522 equivalent bits/point +MSE 1367.084587 +---------------------- -------------------------------------------------------- +Time: 5.172s Load: 0.052s, Pack+Encode: 2.605s, Decode+Unpack: 2.516s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1367.0846 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01820546-0.012522_toucan _ toucan_0.63882655.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n01820546-0.012522_toucan _ toucan_0.63882655.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01833805-0.013248_toucan _ lorikeet_0.77773976.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01833805-0.013248_toucan _ lorikeet_0.77773976.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 173,956B, BPFP=0.3302 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 293,852B, BPFP=0.5578 +⌛️ [2/4] FRONTEND: Frontend time: 2.617s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.493s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09866240 8.92439470 + layer.39.0 7.67772963 1767.78109815 + ------------------------------------------------------------------------------------- + TOTAL 3.88819601 888.35274643 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 467808 +BPFP 0.4440 bits/point +EBPFP 0.4440 equivalent bits/point +MSE 888.352746 +---------------------- -------------------------------------------------------- +Time: 5.172s Load: 0.061s, Pack+Encode: 2.617s, Decode+Unpack: 2.493s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 888.3527 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01833805-0.013248_toucan _ lorikeet_0.77773976.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n01833805-0.013248_toucan _ lorikeet_0.77773976.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01843383-0.013605_white-headed capuchin _ white-headed capuchin_0.9984768.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01843383-0.013605_white-headed capuchin _ white-headed capuchin_0.9984768.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 238,560B, BPFP=0.4528 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 317,256B, BPFP=0.6022 +⌛️ [2/4] FRONTEND: Frontend time: 2.607s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.502s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11910487 51.91369048 + layer.39.0 9.20068692 5157.14771623 + ------------------------------------------------------------------------------------- + TOTAL 4.65989589 2604.53070335 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 555816 +BPFP 0.5275 bits/point +EBPFP 0.5275 equivalent bits/point +MSE 2604.530703 +---------------------- -------------------------------------------------------- +Time: 5.170s Load: 0.062s, Pack+Encode: 2.607s, Decode+Unpack: 2.502s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2604.5307 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01843383-0.013605_white-headed capuchin _ white-headed capuchin_0.9984768.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n01843383-0.013605_white-headed capuchin _ white-headed capuchin_0.9984768.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01924916-0.000644_jay _ jay_0.82223135.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01924916-0.000644_jay _ jay_0.82223135.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 250,964B, BPFP=0.4763 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 261,748B, BPFP=0.4968 +⌛️ [2/4] FRONTEND: Frontend time: 2.634s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.507s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11488669 111.43195001 + layer.39.0 141.08750911 1590.11321672 + ------------------------------------------------------------------------------------- + TOTAL 70.60119790 850.77258336 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 512712 +BPFP 0.4866 bits/point +EBPFP 0.4866 equivalent bits/point +MSE 850.772583 +---------------------- -------------------------------------------------------- +Time: 5.192s Load: 0.052s, Pack+Encode: 2.634s, Decode+Unpack: 2.507s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 850.7726 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01924916-0.000644_jay _ jay_0.82223135.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n01924916-0.000644_jay _ jay_0.82223135.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01944390-0.002567_American robin _ American robin_0.5629079.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01944390-0.002567_American robin _ American robin_0.5629079.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 206,932B, BPFP=0.3928 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 278,292B, BPFP=0.5282 +⌛️ [2/4] FRONTEND: Frontend time: 2.637s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.495s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10732387 26.08216032 + layer.39.0 16.74672581 1874.11844023 + ------------------------------------------------------------------------------------- + TOTAL 8.42702484 950.10030028 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 485224 +BPFP 0.4605 bits/point +EBPFP 0.4605 equivalent bits/point +MSE 950.100300 +---------------------- -------------------------------------------------------- +Time: 5.184s Load: 0.052s, Pack+Encode: 2.637s, Decode+Unpack: 2.495s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 950.1003 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01944390-0.002567_American robin _ American robin_0.5629079.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n01944390-0.002567_American robin _ American robin_0.5629079.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01985128-0.001579_centipede _ centipede_0.85936093.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01985128-0.001579_centipede _ centipede_0.85936093.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 194,328B, BPFP=0.3689 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 262,316B, BPFP=0.4979 +⌛️ [2/4] FRONTEND: Frontend time: 2.630s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.510s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09645609 0.89122088 + layer.39.0 23.47999613 2032.37001944 + ------------------------------------------------------------------------------------- + TOTAL 11.78822611 1016.63062016 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 456644 +BPFP 0.4334 bits/point +EBPFP 0.4334 equivalent bits/point +MSE 1016.630620 +---------------------- -------------------------------------------------------- +Time: 5.192s Load: 0.052s, Pack+Encode: 2.630s, Decode+Unpack: 2.510s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1016.6306 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01985128-0.001579_centipede _ centipede_0.85936093.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n01985128-0.001579_centipede _ centipede_0.85936093.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02037110-0.003503_snowmobile _ snowmobile_0.5200631.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02037110-0.003503_snowmobile _ snowmobile_0.5200631.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 117,064B, BPFP=0.2222 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 247,016B, BPFP=0.4689 +⌛️ [2/4] FRONTEND: Frontend time: 2.619s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.509s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09471867 8.64838435 + layer.39.0 17.04498261 1769.91326531 + ------------------------------------------------------------------------------------- + TOTAL 8.56985064 889.28082483 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 364080 +BPFP 0.3455 bits/point +EBPFP 0.3455 equivalent bits/point +MSE 889.280825 +---------------------- -------------------------------------------------------- +Time: 5.180s Load: 0.051s, Pack+Encode: 2.619s, Decode+Unpack: 2.509s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 889.2808 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02037110-0.003503_snowmobile _ snowmobile_0.5200631.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n02037110-0.003503_snowmobile _ snowmobile_0.5200631.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02123394-0.015363_marmot _ marmot_0.82052565.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02123394-0.015363_marmot _ marmot_0.82052565.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 169,872B, BPFP=0.3224 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 300,868B, BPFP=0.5711 +⌛️ [2/4] FRONTEND: Frontend time: 2.613s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.497s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10209646 8.93471362 + layer.39.0 11.38238543 1856.18294461 + ------------------------------------------------------------------------------------- + TOTAL 5.74224095 932.55882911 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 470740 +BPFP 0.4468 bits/point +EBPFP 0.4468 equivalent bits/point +MSE 932.558829 +---------------------- -------------------------------------------------------- +Time: 5.161s Load: 0.050s, Pack+Encode: 2.613s, Decode+Unpack: 2.497s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 932.5588 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02123394-0.015363_marmot _ marmot_0.82052565.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n02123394-0.015363_marmot _ marmot_0.82052565.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02165456-0.000157_corn _ corn_0.9868978.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02165456-0.000157_corn _ corn_0.9868978.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 217,428B, BPFP=0.4127 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 300,896B, BPFP=0.5711 +⌛️ [2/4] FRONTEND: Frontend time: 2.618s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.489s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10346756 63.79550079 + layer.39.0 776.17699223 2726.19266278 + ------------------------------------------------------------------------------------- + TOTAL 388.14022989 1394.99408178 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 518324 +BPFP 0.4919 bits/point +EBPFP 0.4919 equivalent bits/point +MSE 1394.994082 +---------------------- -------------------------------------------------------- +Time: 5.158s Load: 0.051s, Pack+Encode: 2.618s, Decode+Unpack: 2.489s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1394.9941 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02165456-0.000157_corn _ corn_0.9868978.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n02165456-0.000157_corn _ corn_0.9868978.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02219486-0.000060_cliff _ cliff_0.99684334.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02219486-0.000060_cliff _ cliff_0.99684334.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 170,304B, BPFP=0.3233 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 245,216B, BPFP=0.4654 +⌛️ [2/4] FRONTEND: Frontend time: 2.617s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.498s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09584527 13.15716013 + layer.39.0 31.94620460 1833.16763848 + ------------------------------------------------------------------------------------- + TOTAL 16.02102494 923.16239931 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 415520 +BPFP 0.3943 bits/point +EBPFP 0.3943 equivalent bits/point +MSE 923.162399 +---------------------- -------------------------------------------------------- +Time: 5.176s Load: 0.062s, Pack+Encode: 2.617s, Decode+Unpack: 2.498s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 923.1624 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02219486-0.000060_cliff _ cliff_0.99684334.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n02219486-0.000060_cliff _ cliff_0.99684334.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02226429-0.000085_stick insect _ stick insect_0.99900275.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02226429-0.000085_stick insect _ stick insect_0.99900275.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 182,096B, BPFP=0.3456 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 333,112B, BPFP=0.6323 +⌛️ [2/4] FRONTEND: Frontend time: 2.630s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.502s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09547379 0.90734715 + layer.39.0 19.16722850 3463.78790087 + ------------------------------------------------------------------------------------- + TOTAL 9.63135114 1732.34762401 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 515208 +BPFP 0.4890 bits/point +EBPFP 0.4890 equivalent bits/point +MSE 1732.347624 +---------------------- -------------------------------------------------------- +Time: 5.193s Load: 0.061s, Pack+Encode: 2.630s, Decode+Unpack: 2.502s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1732.3476 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02226429-0.000085_stick insect _ stick insect_0.99900275.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n02226429-0.000085_stick insect _ stick insect_0.99900275.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02231487-0.000080_manhole cover _ manhole cover_0.7554716.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02231487-0.000080_manhole cover _ manhole cover_0.7554716.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 168,148B, BPFP=0.3192 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 312,552B, BPFP=0.5932 +⌛️ [2/4] FRONTEND: Frontend time: 2.592s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.499s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09512618 12.61463458 + layer.39.0 210.79875790 2373.94825073 + ------------------------------------------------------------------------------------- + TOTAL 105.44694204 1193.28144266 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 480700 +BPFP 0.4562 bits/point +EBPFP 0.4562 equivalent bits/point +MSE 1193.281443 +---------------------- -------------------------------------------------------- +Time: 5.152s Load: 0.061s, Pack+Encode: 2.592s, Decode+Unpack: 2.499s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1193.2814 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02231487-0.000080_manhole cover _ manhole cover_0.7554716.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n02231487-0.000080_manhole cover _ manhole cover_0.7554716.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02233338-0.002901_manhole cover _ manhole cover_0.69458175.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02233338-0.002901_manhole cover _ manhole cover_0.69458175.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 150,084B, BPFP=0.2849 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 288,392B, BPFP=0.5474 +⌛️ [2/4] FRONTEND: Frontend time: 2.635s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.494s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09539769 12.58481953 + layer.39.0 58.97704841 1954.36115160 + ------------------------------------------------------------------------------------- + TOTAL 29.53622305 983.47298557 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 438476 +BPFP 0.4161 bits/point +EBPFP 0.4161 equivalent bits/point +MSE 983.472986 +---------------------- -------------------------------------------------------- +Time: 5.181s Load: 0.052s, Pack+Encode: 2.635s, Decode+Unpack: 2.494s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 983.4730 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02233338-0.002901_manhole cover _ manhole cover_0.69458175.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n02233338-0.002901_manhole cover _ manhole cover_0.69458175.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02236044-0.000522_sundial _ sundial_0.96381366.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02236044-0.000522_sundial _ sundial_0.96381366.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 214,024B, BPFP=0.4062 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 334,492B, BPFP=0.6349 +⌛️ [2/4] FRONTEND: Frontend time: 2.636s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.493s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09795647 2.12385854 + layer.39.0 53.12385356 2193.12730807 + ------------------------------------------------------------------------------------- + TOTAL 26.61090502 1097.62558330 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 548516 +BPFP 0.5206 bits/point +EBPFP 0.5206 equivalent bits/point +MSE 1097.625583 +---------------------- -------------------------------------------------------- +Time: 5.180s Load: 0.052s, Pack+Encode: 2.636s, Decode+Unpack: 2.493s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1097.6256 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02236044-0.000522_sundial _ sundial_0.96381366.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n02236044-0.000522_sundial _ sundial_0.96381366.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02259212-0.000032_chain _ chain_0.6590295.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02259212-0.000032_chain _ chain_0.6590295.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 173,320B, BPFP=0.3290 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 339,296B, BPFP=0.6440 +⌛️ [2/4] FRONTEND: Frontend time: 2.624s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.492s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09523673 0.86947137 + layer.39.0 80.66082058 6135.04081633 + ------------------------------------------------------------------------------------- + TOTAL 40.37802865 3067.95514385 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 512616 +BPFP 0.4865 bits/point +EBPFP 0.4865 equivalent bits/point +MSE 3067.955144 +---------------------- -------------------------------------------------------- +Time: 5.168s Load: 0.052s, Pack+Encode: 2.624s, Decode+Unpack: 2.492s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3067.9551 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02259212-0.000032_chain _ chain_0.6590295.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n02259212-0.000032_chain _ chain_0.6590295.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02279972-0.000576_apron _ apron_0.7661352.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02279972-0.000576_apron _ apron_0.7661352.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 224,192B, BPFP=0.4255 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 308,848B, BPFP=0.5862 +⌛️ [2/4] FRONTEND: Frontend time: 2.612s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.502s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12772729 25.76943635 + layer.39.0 1038.59135083 3219.28984451 + ------------------------------------------------------------------------------------- + TOTAL 519.35953906 1622.52964043 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 533040 +BPFP 0.5059 bits/point +EBPFP 0.5059 equivalent bits/point +MSE 1622.529640 +---------------------- -------------------------------------------------------- +Time: 5.176s Load: 0.062s, Pack+Encode: 2.612s, Decode+Unpack: 2.502s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1622.5296 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02279972-0.000576_apron _ apron_0.7661352.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n02279972-0.000576_apron _ apron_0.7661352.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02280649-0.001599_custard apple _ leafhopper_0.9128452.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02280649-0.001599_custard apple _ leafhopper_0.9128452.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 156,256B, BPFP=0.2966 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 336,272B, BPFP=0.6383 +⌛️ [2/4] FRONTEND: Frontend time: 2.618s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.485s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09488542 0.86408764 + layer.39.0 1031.59973275 3915.95189504 + ------------------------------------------------------------------------------------- + TOTAL 515.84730909 1958.40799134 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 492528 +BPFP 0.4674 bits/point +EBPFP 0.4674 equivalent bits/point +MSE 1958.407991 +---------------------- -------------------------------------------------------- +Time: 5.164s Load: 0.061s, Pack+Encode: 2.618s, Decode+Unpack: 2.485s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1958.4080 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02280649-0.001599_custard apple _ leafhopper_0.9128452.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n02280649-0.001599_custard apple _ leafhopper_0.9128452.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02317335-0.033681_flatworm _ flatworm_0.6070924.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02317335-0.033681_flatworm _ flatworm_0.6070924.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 157,908B, BPFP=0.2997 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 278,952B, BPFP=0.5295 +⌛️ [2/4] FRONTEND: Frontend time: 2.619s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.493s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09575805 12.45968287 + layer.39.0 62.35741238 3004.03474247 + ------------------------------------------------------------------------------------- + TOTAL 31.22658522 1508.24721267 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 436860 +BPFP 0.4146 bits/point +EBPFP 0.4146 equivalent bits/point +MSE 1508.247213 +---------------------- -------------------------------------------------------- +Time: 5.173s Load: 0.062s, Pack+Encode: 2.619s, Decode+Unpack: 2.493s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1508.2472 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02317335-0.033681_flatworm _ flatworm_0.6070924.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n02317335-0.033681_flatworm _ flatworm_0.6070924.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02325366-0.000350_flatworm _ flatworm_0.87634724.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02325366-0.000350_flatworm _ flatworm_0.87634724.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 125,168B, BPFP=0.2376 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 243,132B, BPFP=0.4615 +⌛️ [2/4] FRONTEND: Frontend time: 2.615s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.494s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09712043 8.84007437 + layer.39.0 30.59439155 1533.16399417 + ------------------------------------------------------------------------------------- + TOTAL 15.34575599 771.00203427 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 368300 +BPFP 0.3495 bits/point +EBPFP 0.3495 equivalent bits/point +MSE 771.002034 +---------------------- -------------------------------------------------------- +Time: 5.170s Load: 0.061s, Pack+Encode: 2.615s, Decode+Unpack: 2.494s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 771.0020 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02325366-0.000350_flatworm _ flatworm_0.87634724.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n02325366-0.000350_flatworm _ flatworm_0.87634724.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02346627-0.011107_fountain _ skunk_0.28641737.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02346627-0.011107_fountain _ skunk_0.28641737.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 127,704B, BPFP=0.2424 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 215,144B, BPFP=0.4084 +⌛️ [2/4] FRONTEND: Frontend time: 2.608s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.504s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09705289 12.53340147 + layer.39.0 9.52721088 1263.34997570 + ------------------------------------------------------------------------------------- + TOTAL 4.81213189 637.94168859 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 342848 +BPFP 0.3254 bits/point +EBPFP 0.3254 equivalent bits/point +MSE 637.941689 +---------------------- -------------------------------------------------------- +Time: 5.164s Load: 0.052s, Pack+Encode: 2.608s, Decode+Unpack: 2.504s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 637.9417 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02346627-0.011107_fountain _ skunk_0.28641737.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n02346627-0.011107_fountain _ skunk_0.28641737.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02445715-0.036432_shovel _ tarantula_0.26785344.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02445715-0.036432_shovel _ tarantula_0.26785344.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.055s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 216,056B, BPFP=0.4101 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 232,852B, BPFP=0.4420 +⌛️ [2/4] FRONTEND: Frontend time: 2.618s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.511s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09708806 21.19820517 + layer.39.0 8.00606437 1464.65719145 + ------------------------------------------------------------------------------------- + TOTAL 4.05157622 742.92769831 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 448908 +BPFP 0.4260 bits/point +EBPFP 0.4260 equivalent bits/point +MSE 742.927698 +---------------------- -------------------------------------------------------- +Time: 5.183s Load: 0.055s, Pack+Encode: 2.618s, Decode+Unpack: 2.511s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 742.9277 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02445715-0.036432_shovel _ tarantula_0.26785344.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n02445715-0.036432_shovel _ tarantula_0.26785344.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02454379-0.082010_koala _ koala_0.7052893.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02454379-0.082010_koala _ koala_0.7052893.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 299,184B, BPFP=0.5679 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 251,692B, BPFP=0.4777 +⌛️ [2/4] FRONTEND: Frontend time: 2.638s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.502s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14585212 214.27763605 + layer.39.0 44.19989826 1618.95687561 + ------------------------------------------------------------------------------------- + TOTAL 22.17287519 916.61725583 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 550876 +BPFP 0.5228 bits/point +EBPFP 0.5228 equivalent bits/point +MSE 916.617256 +---------------------- -------------------------------------------------------- +Time: 5.192s Load: 0.051s, Pack+Encode: 2.638s, Decode+Unpack: 2.502s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 916.6173 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02454379-0.082010_koala _ koala_0.7052893.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n02454379-0.082010_koala _ koala_0.7052893.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02782093-0.007542_American alligator _ American alligator_0.49872985.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02782093-0.007542_American alligator _ American alligator_0.49872985.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 135,636B, BPFP=0.2574 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 270,272B, BPFP=0.5130 +⌛️ [2/4] FRONTEND: Frontend time: 2.603s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.500s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09848133 0.85946605 + layer.39.0 9.18780844 1529.28777940 + ------------------------------------------------------------------------------------- + TOTAL 4.64314488 765.07362272 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 405908 +BPFP 0.3852 bits/point +EBPFP 0.3852 equivalent bits/point +MSE 765.073623 +---------------------- -------------------------------------------------------- +Time: 5.155s Load: 0.052s, Pack+Encode: 2.603s, Decode+Unpack: 2.500s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 765.0736 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02782093-0.007542_American alligator _ American alligator_0.49872985.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n02782093-0.007542_American alligator _ American alligator_0.49872985.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02787622-0.004599_marimba _ accordion_0.25991488.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02787622-0.004599_marimba _ accordion_0.25991488.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 225,948B, BPFP=0.4289 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 367,312B, BPFP=0.6972 +⌛️ [2/4] FRONTEND: Frontend time: 2.607s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.484s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12856446 51.79204628 + layer.39.0 1004.59450923 9792.13702624 + ------------------------------------------------------------------------------------- + TOTAL 502.36153685 4921.96453626 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 593260 +BPFP 0.5630 bits/point +EBPFP 0.5630 equivalent bits/point +MSE 4921.964536 +---------------------- -------------------------------------------------------- +Time: 5.143s Load: 0.051s, Pack+Encode: 2.607s, Decode+Unpack: 2.484s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4921.9645 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02787622-0.004599_marimba _ accordion_0.25991488.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n02787622-0.004599_marimba _ accordion_0.25991488.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02793495-0.000130_flatworm _ stingray_0.5528234.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02793495-0.000130_flatworm _ stingray_0.5528234.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 136,428B, BPFP=0.2590 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 243,748B, BPFP=0.4627 +⌛️ [2/4] FRONTEND: Frontend time: 2.595s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.486s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09706621 12.55276380 + layer.39.0 8.05872662 1720.69412051 + ------------------------------------------------------------------------------------- + TOTAL 4.07789641 866.62344215 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 380176 +BPFP 0.3608 bits/point +EBPFP 0.3608 equivalent bits/point +MSE 866.623442 +---------------------- -------------------------------------------------------- +Time: 5.134s Load: 0.052s, Pack+Encode: 2.595s, Decode+Unpack: 2.486s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 866.6234 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02793495-0.000130_flatworm _ stingray_0.5528234.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n02793495-0.000130_flatworm _ stingray_0.5528234.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02797295-0.002775_hair dryer _ box turtle_0.2407761.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02797295-0.002775_hair dryer _ box turtle_0.2407761.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 222,204B, BPFP=0.4218 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 385,080B, BPFP=0.7309 +⌛️ [2/4] FRONTEND: Frontend time: 2.625s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.510s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11161610 27.42056722 + layer.39.0 373.09438776 7063.18756074 + ------------------------------------------------------------------------------------- + TOTAL 186.60300193 3545.30406398 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 607284 +BPFP 0.5763 bits/point +EBPFP 0.5763 equivalent bits/point +MSE 3545.304064 +---------------------- -------------------------------------------------------- +Time: 5.187s Load: 0.052s, Pack+Encode: 2.625s, Decode+Unpack: 2.510s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3545.3041 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02797295-0.002775_hair dryer _ box turtle_0.2407761.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n02797295-0.002775_hair dryer _ box turtle_0.2407761.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02814860-0.006340_fountain _ fountain_0.7891514.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02814860-0.006340_fountain _ fountain_0.7891514.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 120,420B, BPFP=0.2286 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 247,692B, BPFP=0.4701 +⌛️ [2/4] FRONTEND: Frontend time: 2.616s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.481s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.04615183 12.89864629 + layer.39.0 7.48662090 1385.34098639 + ------------------------------------------------------------------------------------- + TOTAL 7.76638637 699.11981634 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 368112 +BPFP 0.3494 bits/point +EBPFP 0.3494 equivalent bits/point +MSE 699.119816 +---------------------- -------------------------------------------------------- +Time: 5.159s Load: 0.061s, Pack+Encode: 2.616s, Decode+Unpack: 2.481s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 699.1198 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02814860-0.006340_fountain _ fountain_0.7891514.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n02814860-0.006340_fountain _ fountain_0.7891514.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02879718-0.003578_maraca _ maraca_0.6809677.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02879718-0.003578_maraca _ maraca_0.6809677.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 190,956B, BPFP=0.3624 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 389,876B, BPFP=0.7400 +⌛️ [2/4] FRONTEND: Frontend time: 2.608s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.492s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10989876 25.08316061 + layer.39.0 33.03751367 6313.17055394 + ------------------------------------------------------------------------------------- + TOTAL 16.57370621 3169.12685727 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 580832 +BPFP 0.5512 bits/point +EBPFP 0.5512 equivalent bits/point +MSE 3169.126857 +---------------------- -------------------------------------------------------- +Time: 5.161s Load: 0.061s, Pack+Encode: 2.608s, Decode+Unpack: 2.492s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3169.1269 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02879718-0.003578_maraca _ maraca_0.6809677.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n02879718-0.003578_maraca _ maraca_0.6809677.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02883205-0.000262_syringe _ syringe_0.7098205.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02883205-0.000262_syringe _ syringe_0.7098205.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 150,692B, BPFP=0.2860 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 294,448B, BPFP=0.5589 +⌛️ [2/4] FRONTEND: Frontend time: 2.603s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.494s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09610580 25.03549031 + layer.39.0 8.14318931 2158.42905734 + ------------------------------------------------------------------------------------- + TOTAL 4.11964755 1091.73227382 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 445140 +BPFP 0.4225 bits/point +EBPFP 0.4225 equivalent bits/point +MSE 1091.732274 +---------------------- -------------------------------------------------------- +Time: 5.147s Load: 0.050s, Pack+Encode: 2.603s, Decode+Unpack: 2.494s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1091.7323 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02883205-0.000262_syringe _ syringe_0.7098205.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n02883205-0.000262_syringe _ syringe_0.7098205.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02906734-0.000163_stethoscope _ stethoscope_0.9290994.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02906734-0.000163_stethoscope _ stethoscope_0.9290994.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.058s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 210,020B, BPFP=0.3986 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 362,240B, BPFP=0.6876 +⌛️ [2/4] FRONTEND: Frontend time: 2.620s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.482s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12024398 12.42277564 + layer.39.0 47.23105336 5078.42517007 + ------------------------------------------------------------------------------------- + TOTAL 23.67564867 2545.42397285 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 572260 +BPFP 0.5431 bits/point +EBPFP 0.5431 equivalent bits/point +MSE 2545.423973 +---------------------- -------------------------------------------------------- +Time: 5.159s Load: 0.058s, Pack+Encode: 2.620s, Decode+Unpack: 2.482s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2545.4240 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02906734-0.000163_stethoscope _ stethoscope_0.9290994.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n02906734-0.000163_stethoscope _ stethoscope_0.9290994.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02948072-0.002303_digital clock _ digital clock_0.928374.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02948072-0.002303_digital clock _ digital clock_0.928374.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.054s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 165,436B, BPFP=0.3140 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 329,368B, BPFP=0.6252 +⌛️ [2/4] FRONTEND: Frontend time: 2.616s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.500s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09670976 12.73443859 + layer.39.0 81.62974520 3605.71477162 + ------------------------------------------------------------------------------------- + TOTAL 40.86322748 1809.22460510 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 494804 +BPFP 0.4696 bits/point +EBPFP 0.4696 equivalent bits/point +MSE 1809.224605 +---------------------- -------------------------------------------------------- +Time: 5.170s Load: 0.054s, Pack+Encode: 2.616s, Decode+Unpack: 2.500s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1809.2246 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02948072-0.002303_digital clock _ digital clock_0.928374.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n02948072-0.002303_digital clock _ digital clock_0.928374.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02999410-0.000148_chest _ chest_0.9948565.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02999410-0.000148_chest _ chest_0.9948565.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 160,332B, BPFP=0.3043 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 275,116B, BPFP=0.5222 +⌛️ [2/4] FRONTEND: Frontend time: 2.607s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.491s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10256943 0.86462960 + layer.39.0 13.72598738 1985.22145287 + ------------------------------------------------------------------------------------- + TOTAL 6.91427841 993.04304123 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 435448 +BPFP 0.4133 bits/point +EBPFP 0.4133 equivalent bits/point +MSE 993.043041 +---------------------- -------------------------------------------------------- +Time: 5.160s Load: 0.062s, Pack+Encode: 2.607s, Decode+Unpack: 2.491s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 993.0430 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02999410-0.000148_chest _ chest_0.9948565.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n02999410-0.000148_chest _ chest_0.9948565.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03026506-0.001828_basketball _ basketball_0.6904969.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03026506-0.001828_basketball _ basketball_0.6904969.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 156,972B, BPFP=0.2979 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 318,664B, BPFP=0.6048 +⌛️ [2/4] FRONTEND: Frontend time: 2.627s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.493s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09484169 12.51807352 + layer.39.0 87.31533194 2114.23372206 + ------------------------------------------------------------------------------------- + TOTAL 43.70508681 1063.37589779 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 475636 +BPFP 0.4514 bits/point +EBPFP 0.4514 equivalent bits/point +MSE 1063.375898 +---------------------- -------------------------------------------------------- +Time: 5.172s Load: 0.052s, Pack+Encode: 2.627s, Decode+Unpack: 2.493s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1063.3759 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03026506-0.001828_basketball _ basketball_0.6904969.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n03026506-0.001828_basketball _ basketball_0.6904969.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03124043-0.001154_bow tie _ bow tie_0.9622156.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03124043-0.001154_bow tie _ bow tie_0.9622156.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 160,572B, BPFP=0.3048 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 295,792B, BPFP=0.5614 +⌛️ [2/4] FRONTEND: Frontend time: 2.634s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.492s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09893820 13.77571140 + layer.39.0 13.24554141 2167.66520894 + ------------------------------------------------------------------------------------- + TOTAL 6.67223981 1090.72046017 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 456364 +BPFP 0.4331 bits/point +EBPFP 0.4331 equivalent bits/point +MSE 1090.720460 +---------------------- -------------------------------------------------------- +Time: 5.186s Load: 0.060s, Pack+Encode: 2.634s, Decode+Unpack: 2.492s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1090.7205 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03124043-0.001154_bow tie _ bow tie_0.9622156.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n03124043-0.001154_bow tie _ bow tie_0.9622156.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03196217-0.015958_soap dispenser _ envelope_0.80274254.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03196217-0.015958_soap dispenser _ envelope_0.80274254.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 142,608B, BPFP=0.2707 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 279,340B, BPFP=0.5302 +⌛️ [2/4] FRONTEND: Frontend time: 2.626s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.496s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10340443 24.63729007 + layer.39.0 8.70910111 3135.58381924 + ------------------------------------------------------------------------------------- + TOTAL 4.40625277 1580.11055466 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 421948 +BPFP 0.4004 bits/point +EBPFP 0.4004 equivalent bits/point +MSE 1580.110555 +---------------------- -------------------------------------------------------- +Time: 5.174s Load: 0.052s, Pack+Encode: 2.626s, Decode+Unpack: 2.496s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1580.1106 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03196217-0.015958_soap dispenser _ envelope_0.80274254.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n03196217-0.015958_soap dispenser _ envelope_0.80274254.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03223299-0.001528_hot dog _ hot dog_0.9147216.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03223299-0.001528_hot dog _ hot dog_0.9147216.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 198,328B, BPFP=0.3764 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 299,020B, BPFP=0.5676 +⌛️ [2/4] FRONTEND: Frontend time: 2.606s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.500s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10130972 37.79944880 + layer.39.0 352.09596696 2694.00437318 + ------------------------------------------------------------------------------------- + TOTAL 176.09863834 1365.90191099 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 497348 +BPFP 0.4720 bits/point +EBPFP 0.4720 equivalent bits/point +MSE 1365.901911 +---------------------- -------------------------------------------------------- +Time: 5.158s Load: 0.052s, Pack+Encode: 2.606s, Decode+Unpack: 2.500s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1365.9019 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03223299-0.001528_hot dog _ hot dog_0.9147216.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n03223299-0.001528_hot dog _ hot dog_0.9147216.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03255030-0.005469_bubble _ bubble_0.9381716.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03255030-0.005469_bubble _ bubble_0.9381716.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 176,616B, BPFP=0.3352 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 379,920B, BPFP=0.7211 +⌛️ [2/4] FRONTEND: Frontend time: 2.611s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.492s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09675161 12.52946391 + layer.39.0 42.23478499 4170.29446064 + ------------------------------------------------------------------------------------- + TOTAL 21.16576830 2091.41196227 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 556536 +BPFP 0.5282 bits/point +EBPFP 0.5282 equivalent bits/point +MSE 2091.411962 +---------------------- -------------------------------------------------------- +Time: 5.164s Load: 0.061s, Pack+Encode: 2.611s, Decode+Unpack: 2.492s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2091.4120 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03255030-0.005469_bubble _ bubble_0.9381716.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n03255030-0.005469_bubble _ bubble_0.9381716.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03325584-0.000773_candle _ candle_0.810919.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03325584-0.000773_candle _ candle_0.810919.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 208,008B, BPFP=0.3948 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 374,296B, BPFP=0.7104 +⌛️ [2/4] FRONTEND: Frontend time: 2.620s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.491s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10394677 26.32801301 + layer.39.0 140.58187561 7604.66229349 + ------------------------------------------------------------------------------------- + TOTAL 70.34291119 3815.49515325 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 582304 +BPFP 0.5526 bits/point +EBPFP 0.5526 equivalent bits/point +MSE 3815.495153 +---------------------- -------------------------------------------------------- +Time: 5.172s Load: 0.062s, Pack+Encode: 2.620s, Decode+Unpack: 2.491s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3815.4952 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03325584-0.000773_candle _ candle_0.810919.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n03325584-0.000773_candle _ candle_0.810919.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03355925-0.004997_spider web _ spider web_0.9142101.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03355925-0.004997_spider web _ spider web_0.9142101.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 106,608B, BPFP=0.2024 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 232,256B, BPFP=0.4408 +⌛️ [2/4] FRONTEND: Frontend time: 2.608s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.482s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09873271 12.72362332 + layer.39.0 6.60211199 1691.11807580 + ------------------------------------------------------------------------------------- + TOTAL 3.35042235 851.92084956 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 338864 +BPFP 0.3216 bits/point +EBPFP 0.3216 equivalent bits/point +MSE 851.920850 +---------------------- -------------------------------------------------------- +Time: 5.151s Load: 0.061s, Pack+Encode: 2.608s, Decode+Unpack: 2.482s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 851.9208 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03355925-0.004997_spider web _ spider web_0.9142101.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n03355925-0.004997_spider web _ spider web_0.9142101.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03384352-0.002273_viaduct _ viaduct_0.6161024.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03384352-0.002273_viaduct _ viaduct_0.6161024.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 168,800B, BPFP=0.3204 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 301,332B, BPFP=0.5720 +⌛️ [2/4] FRONTEND: Frontend time: 2.632s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.510s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09647940 36.46906129 + layer.39.0 175.50411504 2149.70821186 + ------------------------------------------------------------------------------------- + TOTAL 87.80029722 1093.08863657 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 470132 +BPFP 0.4462 bits/point +EBPFP 0.4462 equivalent bits/point +MSE 1093.088637 +---------------------- -------------------------------------------------------- +Time: 5.203s Load: 0.061s, Pack+Encode: 2.632s, Decode+Unpack: 2.510s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1093.0886 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03384352-0.002273_viaduct _ viaduct_0.6161024.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n03384352-0.002273_viaduct _ viaduct_0.6161024.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03388043-0.005154_candle _ candle_0.9636924.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03388043-0.005154_candle _ candle_0.9636924.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 160,380B, BPFP=0.3044 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 283,300B, BPFP=0.5377 +⌛️ [2/4] FRONTEND: Frontend time: 2.608s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.493s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09640297 12.28531094 + layer.39.0 7.87377147 1571.12305637 + ------------------------------------------------------------------------------------- + TOTAL 3.98508722 791.70418365 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 443680 +BPFP 0.4211 bits/point +EBPFP 0.4211 equivalent bits/point +MSE 791.704184 +---------------------- -------------------------------------------------------- +Time: 5.152s Load: 0.051s, Pack+Encode: 2.608s, Decode+Unpack: 2.493s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 791.7042 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03388043-0.005154_candle _ candle_0.9636924.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n03388043-0.005154_candle _ candle_0.9636924.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03417042-0.001187_tank _ tank_0.70379025.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03417042-0.001187_tank _ tank_0.70379025.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 147,372B, BPFP=0.2797 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 313,060B, BPFP=0.5942 +⌛️ [2/4] FRONTEND: Frontend time: 2.610s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.492s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09848782 0.90675584 + layer.39.0 16.63742104 2723.11273081 + ------------------------------------------------------------------------------------- + TOTAL 8.36795443 1362.00974332 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 460432 +BPFP 0.4370 bits/point +EBPFP 0.4370 equivalent bits/point +MSE 1362.009743 +---------------------- -------------------------------------------------------- +Time: 5.163s Load: 0.061s, Pack+Encode: 2.610s, Decode+Unpack: 2.492s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1362.0097 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03417042-0.001187_tank _ tank_0.70379025.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n03417042-0.001187_tank _ tank_0.70379025.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03444034-0.002100_maraca _ maraca_0.502369.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03444034-0.002100_maraca _ maraca_0.502369.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 220,820B, BPFP=0.4191 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 413,400B, BPFP=0.7847 +⌛️ [2/4] FRONTEND: Frontend time: 2.602s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.508s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11197850 37.11791257 + layer.39.0 347.54634354 5920.50485909 + ------------------------------------------------------------------------------------- + TOTAL 173.82916102 2978.81138583 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 634220 +BPFP 0.6019 bits/point +EBPFP 0.6019 equivalent bits/point +MSE 2978.811386 +---------------------- -------------------------------------------------------- +Time: 5.171s Load: 0.061s, Pack+Encode: 2.602s, Decode+Unpack: 2.508s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2978.8114 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03444034-0.002100_maraca _ maraca_0.502369.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n03444034-0.002100_maraca _ maraca_0.502369.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03445924-0.003505_baseball player _ baseball player_0.6723684.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03445924-0.003505_baseball player _ baseball player_0.6723684.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 156,364B, BPFP=0.2968 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 333,680B, BPFP=0.6334 +⌛️ [2/4] FRONTEND: Frontend time: 2.602s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.493s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09665277 12.44095640 + layer.39.0 26.28463618 4548.65646259 + ------------------------------------------------------------------------------------- + TOTAL 13.19064447 2280.54870949 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 490044 +BPFP 0.4651 bits/point +EBPFP 0.4651 equivalent bits/point +MSE 2280.548709 +---------------------- -------------------------------------------------------- +Time: 5.156s Load: 0.061s, Pack+Encode: 2.602s, Decode+Unpack: 2.493s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2280.5487 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03445924-0.003505_baseball player _ baseball player_0.6723684.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n03445924-0.003505_baseball player _ baseball player_0.6723684.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03452741-0.002771_chain _ chain_0.9575044.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03452741-0.002771_chain _ chain_0.9575044.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 202,892B, BPFP=0.3851 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 383,984B, BPFP=0.7288 +⌛️ [2/4] FRONTEND: Frontend time: 2.614s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.496s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12351380 14.19591419 + layer.39.0 42.82565370 4813.42662779 + ------------------------------------------------------------------------------------- + TOTAL 21.47458375 2413.81127099 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 586876 +BPFP 0.5570 bits/point +EBPFP 0.5570 equivalent bits/point +MSE 2413.811271 +---------------------- -------------------------------------------------------- +Time: 5.162s Load: 0.051s, Pack+Encode: 2.614s, Decode+Unpack: 2.496s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2413.8113 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03452741-0.002771_chain _ chain_0.9575044.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n03452741-0.002771_chain _ chain_0.9575044.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03483316-0.004974_lighter _ lighter_0.27796906.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03483316-0.004974_lighter _ lighter_0.27796906.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 246,048B, BPFP=0.4670 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 327,548B, BPFP=0.6217 +⌛️ [2/4] FRONTEND: Frontend time: 2.612s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.495s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12993333 51.99665558 + layer.39.0 87.07173986 2586.58794947 + ------------------------------------------------------------------------------------- + TOTAL 43.60083660 1319.29230252 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 573596 +BPFP 0.5444 bits/point +EBPFP 0.5444 equivalent bits/point +MSE 1319.292303 +---------------------- -------------------------------------------------------- +Time: 5.167s Load: 0.060s, Pack+Encode: 2.612s, Decode+Unpack: 2.495s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1319.2923 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03483316-0.004974_lighter _ lighter_0.27796906.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n03483316-0.004974_lighter _ lighter_0.27796906.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03584829-0.104805_golf cart _ golf cart_0.73568964.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03584829-0.104805_golf cart _ golf cart_0.73568964.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 187,460B, BPFP=0.3558 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 327,260B, BPFP=0.6212 +⌛️ [2/4] FRONTEND: Frontend time: 2.618s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.493s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09917131 60.87019026 + layer.39.0 24.34873246 4306.66472303 + ------------------------------------------------------------------------------------- + TOTAL 12.22395189 2183.76745665 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 514720 +BPFP 0.4885 bits/point +EBPFP 0.4885 equivalent bits/point +MSE 2183.767457 +---------------------- -------------------------------------------------------- +Time: 5.181s Load: 0.071s, Pack+Encode: 2.618s, Decode+Unpack: 2.493s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2183.7675 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03584829-0.104805_golf cart _ golf cart_0.73568964.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n03584829-0.104805_golf cart _ golf cart_0.73568964.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03590841-0.001115_rocking chair _ rocking chair_0.2192492.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03590841-0.001115_rocking chair _ rocking chair_0.2192492.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.056s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 204,076B, BPFP=0.3874 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 287,140B, BPFP=0.5450 +⌛️ [2/4] FRONTEND: Frontend time: 2.620s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.499s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11329899 33.50418717 + layer.39.0 19.97532495 3159.43756074 + ------------------------------------------------------------------------------------- + TOTAL 10.04431197 1596.47087395 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 491216 +BPFP 0.4662 bits/point +EBPFP 0.4662 equivalent bits/point +MSE 1596.470874 +---------------------- -------------------------------------------------------- +Time: 5.176s Load: 0.056s, Pack+Encode: 2.620s, Decode+Unpack: 2.499s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1596.4709 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03590841-0.001115_rocking chair _ rocking chair_0.2192492.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n03590841-0.001115_rocking chair _ rocking chair_0.2192492.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03617480-0.003238_basketball _ basketball_0.67568874.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03617480-0.003238_basketball _ basketball_0.67568874.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 246,728B, BPFP=0.4683 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 399,104B, BPFP=0.7575 +⌛️ [2/4] FRONTEND: Frontend time: 2.606s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.484s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12967051 46.59512421 + layer.39.0 57.10576865 6921.30660836 + ------------------------------------------------------------------------------------- + TOTAL 28.61771958 3483.95086628 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 645832 +BPFP 0.6129 bits/point +EBPFP 0.6129 equivalent bits/point +MSE 3483.950866 +---------------------- -------------------------------------------------------- +Time: 5.141s Load: 0.051s, Pack+Encode: 2.606s, Decode+Unpack: 2.484s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3483.9509 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03617480-0.003238_basketball _ basketball_0.67568874.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n03617480-0.003238_basketball _ basketball_0.67568874.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03666591-0.004622_torch _ torch_0.99906796.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03666591-0.004622_torch _ torch_0.99906796.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 131,800B, BPFP=0.2502 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 256,760B, BPFP=0.4874 +⌛️ [2/4] FRONTEND: Frontend time: 2.595s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.488s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.05477861 12.76651995 + layer.39.0 7.78975672 1273.44982993 + ------------------------------------------------------------------------------------- + TOTAL 7.92226767 643.10817494 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 388560 +BPFP 0.3688 bits/point +EBPFP 0.3688 equivalent bits/point +MSE 643.108175 +---------------------- -------------------------------------------------------- +Time: 5.143s Load: 0.060s, Pack+Encode: 2.595s, Decode+Unpack: 2.488s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 643.1082 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03666591-0.004622_torch _ torch_0.99906796.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n03666591-0.004622_torch _ torch_0.99906796.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03670208-0.003899_hair dryer _ grand piano_0.7359066.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03670208-0.003899_hair dryer _ grand piano_0.7359066.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 192,904B, BPFP=0.3661 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 378,464B, BPFP=0.7184 +⌛️ [2/4] FRONTEND: Frontend time: 2.625s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.505s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11232473 49.77159636 + layer.39.0 36.60432231 4837.18270165 + ------------------------------------------------------------------------------------- + TOTAL 18.35832352 2443.47714901 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 571368 +BPFP 0.5423 bits/point +EBPFP 0.5423 equivalent bits/point +MSE 2443.477149 +---------------------- -------------------------------------------------------- +Time: 5.199s Load: 0.070s, Pack+Encode: 2.625s, Decode+Unpack: 2.505s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2443.4771 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03670208-0.003899_hair dryer _ grand piano_0.7359066.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n03670208-0.003899_hair dryer _ grand piano_0.7359066.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03717622-0.001175_sundial _ sundial_0.9998197.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03717622-0.001175_sundial _ sundial_0.9998197.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 232,784B, BPFP=0.4418 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 369,376B, BPFP=0.7011 +⌛️ [2/4] FRONTEND: Frontend time: 2.619s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.488s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13381931 88.01243622 + layer.39.0 773.52204810 5885.11807580 + ------------------------------------------------------------------------------------- + TOTAL 386.82793371 2986.56525601 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 602160 +BPFP 0.5715 bits/point +EBPFP 0.5715 equivalent bits/point +MSE 2986.565256 +---------------------- -------------------------------------------------------- +Time: 5.167s Load: 0.061s, Pack+Encode: 2.619s, Decode+Unpack: 2.488s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2986.5653 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03717622-0.001175_sundial _ sundial_0.9998197.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n03717622-0.001175_sundial _ sundial_0.9998197.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03720891-0.001854_African bush elephant _ African bush elephant_0.722115.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03720891-0.001854_African bush elephant _ African bush elephant_0.722115.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.056s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 173,144B, BPFP=0.3286 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 405,228B, BPFP=0.7692 +⌛️ [2/4] FRONTEND: Frontend time: 2.615s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.506s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09642763 12.42561517 + layer.39.0 155.23232507 6834.18756074 + ------------------------------------------------------------------------------------- + TOTAL 77.66437635 3423.30658795 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 578372 +BPFP 0.5489 bits/point +EBPFP 0.5489 equivalent bits/point +MSE 3423.306588 +---------------------- -------------------------------------------------------- +Time: 5.177s Load: 0.056s, Pack+Encode: 2.615s, Decode+Unpack: 2.506s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3423.3066 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03720891-0.001854_African bush elephant _ African bush elephant_0.722115.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n03720891-0.001854_African bush elephant _ African bush elephant_0.722115.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03721384-0.003327_chain _ chain_0.5599652.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03721384-0.003327_chain _ chain_0.5599652.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 148,860B, BPFP=0.2825 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 277,624B, BPFP=0.5270 +⌛️ [2/4] FRONTEND: Frontend time: 2.599s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.497s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09561452 12.24620763 + layer.39.0 742.66502672 5541.42031098 + ------------------------------------------------------------------------------------- + TOTAL 371.38032062 2776.83325931 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 426484 +BPFP 0.4048 bits/point +EBPFP 0.4048 equivalent bits/point +MSE 2776.833259 +---------------------- -------------------------------------------------------- +Time: 5.155s Load: 0.059s, Pack+Encode: 2.599s, Decode+Unpack: 2.497s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2776.8333 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03721384-0.003327_chain _ chain_0.5599652.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n03721384-0.003327_chain _ chain_0.5599652.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03724870-0.001366_Christmas stocking _ Christmas stocking_0.5709616.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03724870-0.001366_Christmas stocking _ Christmas stocking_0.5709616.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 198,044B, BPFP=0.3759 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 310,548B, BPFP=0.5894 +⌛️ [2/4] FRONTEND: Frontend time: 2.628s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.490s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10329660 25.78743964 + layer.39.0 513.92243683 5634.89115646 + ------------------------------------------------------------------------------------- + TOTAL 257.01286671 2830.33929805 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 508592 +BPFP 0.4827 bits/point +EBPFP 0.4827 equivalent bits/point +MSE 2830.339298 +---------------------- -------------------------------------------------------- +Time: 5.178s Load: 0.060s, Pack+Encode: 2.628s, Decode+Unpack: 2.490s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2830.3393 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03724870-0.001366_Christmas stocking _ Christmas stocking_0.5709616.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n03724870-0.001366_Christmas stocking _ Christmas stocking_0.5709616.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03775071-0.000145_Christmas stocking _ Christmas stocking_0.9986047.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03775071-0.000145_Christmas stocking _ Christmas stocking_0.9986047.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 182,176B, BPFP=0.3458 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 351,724B, BPFP=0.6676 +⌛️ [2/4] FRONTEND: Frontend time: 2.612s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.503s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09700392 12.36111079 + layer.39.0 284.92189018 3418.75364431 + ------------------------------------------------------------------------------------- + TOTAL 142.50944705 1715.55737755 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 533900 +BPFP 0.5067 bits/point +EBPFP 0.5067 equivalent bits/point +MSE 1715.557378 +---------------------- -------------------------------------------------------- +Time: 5.166s Load: 0.051s, Pack+Encode: 2.612s, Decode+Unpack: 2.503s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1715.5574 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03775071-0.000145_Christmas stocking _ Christmas stocking_0.9986047.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n03775071-0.000145_Christmas stocking _ Christmas stocking_0.9986047.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03788195-0.005975_steam locomotive _ steam locomotive_0.42419377.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03788195-0.005975_steam locomotive _ steam locomotive_0.42419377.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 223,968B, BPFP=0.4251 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 302,976B, BPFP=0.5751 +⌛️ [2/4] FRONTEND: Frontend time: 2.626s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.494s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10790903 13.48742427 + layer.39.0 10.34781284 2031.83138970 + ------------------------------------------------------------------------------------- + TOTAL 5.22786094 1022.65940698 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 526944 +BPFP 0.5001 bits/point +EBPFP 0.5001 equivalent bits/point +MSE 1022.659407 +---------------------- -------------------------------------------------------- +Time: 5.180s Load: 0.061s, Pack+Encode: 2.626s, Decode+Unpack: 2.494s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1022.6594 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03788195-0.005975_steam locomotive _ steam locomotive_0.42419377.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n03788195-0.005975_steam locomotive _ steam locomotive_0.42419377.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03837869-0.002023_lighthouse _ lighthouse_0.5898798.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03837869-0.002023_lighthouse _ lighthouse_0.5898798.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 175,088B, BPFP=0.3323 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 255,088B, BPFP=0.4842 +⌛️ [2/4] FRONTEND: Frontend time: 2.587s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.504s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12703056 14.47951402 + layer.39.0 141.21340500 1562.98870262 + ------------------------------------------------------------------------------------- + TOTAL 70.67021778 788.73410832 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 430176 +BPFP 0.4083 bits/point +EBPFP 0.4083 equivalent bits/point +MSE 788.734108 +---------------------- -------------------------------------------------------- +Time: 5.143s Load: 0.052s, Pack+Encode: 2.587s, Decode+Unpack: 2.504s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 788.7341 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03837869-0.002023_lighthouse _ lighthouse_0.5898798.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n03837869-0.002023_lighthouse _ lighthouse_0.5898798.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03840681-0.002188_ladybug _ ladybug_0.9972365.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03840681-0.002188_ladybug _ ladybug_0.9972365.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 125,284B, BPFP=0.2378 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 250,500B, BPFP=0.4755 +⌛️ [2/4] FRONTEND: Frontend time: 2.616s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.493s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09487485 12.63628124 + layer.39.0 29.40353574 3343.87317784 + ------------------------------------------------------------------------------------- + TOTAL 14.74920530 1678.25472954 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 375784 +BPFP 0.3566 bits/point +EBPFP 0.3566 equivalent bits/point +MSE 1678.254730 +---------------------- -------------------------------------------------------- +Time: 5.171s Load: 0.062s, Pack+Encode: 2.616s, Decode+Unpack: 2.493s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1678.2547 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03840681-0.002188_ladybug _ ladybug_0.9972365.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n03840681-0.002188_ladybug _ ladybug_0.9972365.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03888257-0.004083_rugby ball _ snail_0.41588444.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03888257-0.004083_rugby ball _ snail_0.41588444.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 140,784B, BPFP=0.2672 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 237,860B, BPFP=0.4515 +⌛️ [2/4] FRONTEND: Frontend time: 2.610s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.478s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10005040 12.72804490 + layer.39.0 7.47115060 1204.74501944 + ------------------------------------------------------------------------------------- + TOTAL 3.78560050 608.73653217 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 378644 +BPFP 0.3593 bits/point +EBPFP 0.3593 equivalent bits/point +MSE 608.736532 +---------------------- -------------------------------------------------------- +Time: 5.150s Load: 0.061s, Pack+Encode: 2.610s, Decode+Unpack: 2.478s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 608.7365 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03888257-0.004083_rugby ball _ snail_0.41588444.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n03888257-0.004083_rugby ball _ snail_0.41588444.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03891332-0.003727_syringe _ syringe_0.93799996.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03891332-0.003727_syringe _ syringe_0.93799996.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 155,168B, BPFP=0.2945 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 342,016B, BPFP=0.6492 +⌛️ [2/4] FRONTEND: Frontend time: 2.609s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.487s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09617506 12.23473943 + layer.39.0 18.45312310 5510.76287658 + ------------------------------------------------------------------------------------- + TOTAL 9.27464908 2761.49880801 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 497184 +BPFP 0.4718 bits/point +EBPFP 0.4718 equivalent bits/point +MSE 2761.498808 +---------------------- -------------------------------------------------------- +Time: 5.147s Load: 0.052s, Pack+Encode: 2.609s, Decode+Unpack: 2.487s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2761.4988 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03891332-0.003727_syringe _ syringe_0.93799996.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n03891332-0.003727_syringe _ syringe_0.93799996.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03982430-0.005102_couch _ couch_0.9976859.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03982430-0.005102_couch _ couch_0.9976859.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 118,896B, BPFP=0.2257 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 289,360B, BPFP=0.5492 +⌛️ [2/4] FRONTEND: Frontend time: 2.613s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.504s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09691652 12.55765040 + layer.39.0 169.89398081 4258.92662779 + ------------------------------------------------------------------------------------- + TOTAL 84.99544866 2135.74213910 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 408256 +BPFP 0.3875 bits/point +EBPFP 0.3875 equivalent bits/point +MSE 2135.742139 +---------------------- -------------------------------------------------------- +Time: 5.177s Load: 0.061s, Pack+Encode: 2.613s, Decode+Unpack: 2.504s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2135.7421 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03982430-0.005102_couch _ couch_0.9976859.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n03982430-0.005102_couch _ couch_0.9976859.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04033901-0.007476_envelope _ envelope_0.9990971.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04033901-0.007476_envelope _ envelope_0.9990971.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 141,740B, BPFP=0.2690 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 298,268B, BPFP=0.5661 +⌛️ [2/4] FRONTEND: Frontend time: 2.627s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.510s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10364226 12.69081784 + layer.39.0 7.34252906 2451.20894072 + ------------------------------------------------------------------------------------- + TOTAL 3.72308566 1231.94987928 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 440008 +BPFP 0.4176 bits/point +EBPFP 0.4176 equivalent bits/point +MSE 1231.949879 +---------------------- -------------------------------------------------------- +Time: 5.188s Load: 0.051s, Pack+Encode: 2.627s, Decode+Unpack: 2.510s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1231.9499 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04033901-0.007476_envelope _ envelope_0.9990971.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n04033901-0.007476_envelope _ envelope_0.9990971.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04039381-0.000054_hair dryer _ hair dryer_0.5667548.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04039381-0.000054_hair dryer _ hair dryer_0.5667548.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 145,804B, BPFP=0.2767 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 345,500B, BPFP=0.6558 +⌛️ [2/4] FRONTEND: Frontend time: 2.618s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.498s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09588603 12.65568722 + layer.39.0 26.21653304 2407.13848397 + ------------------------------------------------------------------------------------- + TOTAL 13.15620954 1209.89708559 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 491304 +BPFP 0.4663 bits/point +EBPFP 0.4663 equivalent bits/point +MSE 1209.897086 +---------------------- -------------------------------------------------------- +Time: 5.177s Load: 0.061s, Pack+Encode: 2.618s, Decode+Unpack: 2.498s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1209.8971 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04039381-0.000054_hair dryer _ hair dryer_0.5667548.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n04039381-0.000054_hair dryer _ hair dryer_0.5667548.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04118538-0.005804_cowboy boot _ cowboy boot_0.21208441.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04118538-0.005804_cowboy boot _ cowboy boot_0.21208441.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 185,940B, BPFP=0.3529 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 288,372B, BPFP=0.5474 +⌛️ [2/4] FRONTEND: Frontend time: 2.618s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.502s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09664223 0.87213129 + layer.39.0 8.64007266 2508.29689018 + ------------------------------------------------------------------------------------- + TOTAL 4.36835744 1254.58451074 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 474312 +BPFP 0.4501 bits/point +EBPFP 0.4501 equivalent bits/point +MSE 1254.584511 +---------------------- -------------------------------------------------------- +Time: 5.171s Load: 0.051s, Pack+Encode: 2.618s, Decode+Unpack: 2.502s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1254.5845 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04118538-0.005804_cowboy boot _ cowboy boot_0.21208441.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n04118538-0.005804_cowboy boot _ cowboy boot_0.21208441.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04146614-0.008793_marimba _ marimba_0.54555196.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04146614-0.008793_marimba _ marimba_0.54555196.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 186,888B, BPFP=0.3547 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 344,300B, BPFP=0.6535 +⌛️ [2/4] FRONTEND: Frontend time: 2.605s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.512s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09774729 8.59658858 + layer.39.0 155.07908163 9258.22643343 + ------------------------------------------------------------------------------------- + TOTAL 77.58841446 4633.41151101 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 531188 +BPFP 0.5041 bits/point +EBPFP 0.5041 equivalent bits/point +MSE 4633.411511 +---------------------- -------------------------------------------------------- +Time: 5.178s Load: 0.061s, Pack+Encode: 2.605s, Decode+Unpack: 2.512s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4633.4115 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04146614-0.008793_marimba _ marimba_0.54555196.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n04146614-0.008793_marimba _ marimba_0.54555196.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04179913-0.006024_guacamole _ custard apple_0.5550306.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04179913-0.006024_guacamole _ custard apple_0.5550306.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 209,320B, BPFP=0.3973 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 359,500B, BPFP=0.6824 +⌛️ [2/4] FRONTEND: Frontend time: 2.631s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.502s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11409367 21.29442648 + layer.39.0 68.43204871 5028.20165209 + ------------------------------------------------------------------------------------- + TOTAL 34.27307119 2524.74803928 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 568820 +BPFP 0.5398 bits/point +EBPFP 0.5398 equivalent bits/point +MSE 2524.748039 +---------------------- -------------------------------------------------------- +Time: 5.185s Load: 0.052s, Pack+Encode: 2.631s, Decode+Unpack: 2.502s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2524.7480 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04179913-0.006024_guacamole _ custard apple_0.5550306.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n04179913-0.006024_guacamole _ custard apple_0.5550306.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04208210-0.007213_doormat _ doormat_0.81736016.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04208210-0.007213_doormat _ doormat_0.81736016.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.053s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 229,056B, BPFP=0.4348 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 311,972B, BPFP=0.5921 +⌛️ [2/4] FRONTEND: Frontend time: 2.621s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.487s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10601767 50.60474368 + layer.39.0 349.44518343 2516.11686103 + ------------------------------------------------------------------------------------- + TOTAL 174.77560055 1283.36080236 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 541028 +BPFP 0.5135 bits/point +EBPFP 0.5135 equivalent bits/point +MSE 1283.360802 +---------------------- -------------------------------------------------------- +Time: 5.161s Load: 0.053s, Pack+Encode: 2.621s, Decode+Unpack: 2.487s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1283.3608 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04208210-0.007213_doormat _ doormat_0.81736016.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n04208210-0.007213_doormat _ doormat_0.81736016.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04270147-0.000435_rotary dial telephone _ rotary dial telephone_0.9268941.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04270147-0.000435_rotary dial telephone _ rotary dial telephone_0.9268941.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 146,880B, BPFP=0.2788 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 369,348B, BPFP=0.7011 +⌛️ [2/4] FRONTEND: Frontend time: 2.621s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.503s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09464848 12.50319921 + layer.39.0 229.78908528 2973.29956268 + ------------------------------------------------------------------------------------- + TOTAL 114.94186688 1492.90138095 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 516228 +BPFP 0.4899 bits/point +EBPFP 0.4899 equivalent bits/point +MSE 1492.901381 +---------------------- -------------------------------------------------------- +Time: 5.176s Load: 0.052s, Pack+Encode: 2.621s, Decode+Unpack: 2.503s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1492.9014 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04270147-0.000435_rotary dial telephone _ rotary dial telephone_0.9268941.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n04270147-0.000435_rotary dial telephone _ rotary dial telephone_0.9268941.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04317175-0.006894_bow tie _ bow tie_0.95877784.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04317175-0.006894_bow tie _ bow tie_0.95877784.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 165,572B, BPFP=0.3143 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 356,856B, BPFP=0.6773 +⌛️ [2/4] FRONTEND: Frontend time: 2.628s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.498s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09706025 8.72415763 + layer.39.0 10.87108806 3479.99732750 + ------------------------------------------------------------------------------------- + TOTAL 5.48407415 1744.36074257 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 522428 +BPFP 0.4958 bits/point +EBPFP 0.4958 equivalent bits/point +MSE 1744.360743 +---------------------- -------------------------------------------------------- +Time: 5.178s Load: 0.052s, Pack+Encode: 2.628s, Decode+Unpack: 2.498s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1744.3607 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04317175-0.006894_bow tie _ bow tie_0.95877784.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n04317175-0.006894_bow tie _ bow tie_0.95877784.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04347754-0.001405_viaduct _ viaduct_0.8445672.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04347754-0.001405_viaduct _ viaduct_0.8445672.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 122,564B, BPFP=0.2326 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 313,096B, BPFP=0.5943 +⌛️ [2/4] FRONTEND: Frontend time: 2.619s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.505s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09586499 12.68613813 + layer.39.0 267.55718537 2838.09207969 + ------------------------------------------------------------------------------------- + TOTAL 133.82652518 1425.38910891 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 435660 +BPFP 0.4135 bits/point +EBPFP 0.4135 equivalent bits/point +MSE 1425.389109 +---------------------- -------------------------------------------------------- +Time: 5.185s Load: 0.061s, Pack+Encode: 2.619s, Decode+Unpack: 2.505s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1425.3891 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04347754-0.001405_viaduct _ viaduct_0.8445672.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n04347754-0.001405_viaduct _ viaduct_0.8445672.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04355338-0.000791_envelope _ envelope_0.9969598.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04355338-0.000791_envelope _ envelope_0.9969598.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 165,796B, BPFP=0.3147 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 318,660B, BPFP=0.6048 +⌛️ [2/4] FRONTEND: Frontend time: 2.618s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.495s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10273007 60.65299289 + layer.39.0 331.89978134 4422.47521866 + ------------------------------------------------------------------------------------- + TOTAL 166.00125571 2241.56410578 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 484456 +BPFP 0.4598 bits/point +EBPFP 0.4598 equivalent bits/point +MSE 2241.564106 +---------------------- -------------------------------------------------------- +Time: 5.175s Load: 0.061s, Pack+Encode: 2.618s, Decode+Unpack: 2.495s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2241.5641 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04355338-0.000791_envelope _ envelope_0.9969598.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n04355338-0.000791_envelope _ envelope_0.9969598.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04366367-0.002021_parachute _ parachute_0.9226023.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04366367-0.002021_parachute _ parachute_0.9226023.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 142,792B, BPFP=0.2710 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 227,204B, BPFP=0.4313 +⌛️ [2/4] FRONTEND: Frontend time: 2.639s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.509s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09577132 12.63082236 + layer.39.0 47.60657343 1475.11443149 + ------------------------------------------------------------------------------------- + TOTAL 23.85117238 743.87262692 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 369996 +BPFP 0.3511 bits/point +EBPFP 0.3511 equivalent bits/point +MSE 743.872627 +---------------------- -------------------------------------------------------- +Time: 5.198s Load: 0.050s, Pack+Encode: 2.639s, Decode+Unpack: 2.509s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 743.8726 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04366367-0.002021_parachute _ parachute_0.9226023.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n04366367-0.002021_parachute _ parachute_0.9226023.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04376876-0.004615_lighter _ lighter_0.69176143.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04376876-0.004615_lighter _ lighter_0.69176143.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 163,628B, BPFP=0.3106 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 334,292B, BPFP=0.6345 +⌛️ [2/4] FRONTEND: Frontend time: 2.631s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.495s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09912059 0.89896914 + layer.39.0 173.01079628 2726.84207969 + ------------------------------------------------------------------------------------- + TOTAL 86.55495844 1363.87052441 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 497920 +BPFP 0.4725 bits/point +EBPFP 0.4725 equivalent bits/point +MSE 1363.870524 +---------------------- -------------------------------------------------------- +Time: 5.186s Load: 0.059s, Pack+Encode: 2.631s, Decode+Unpack: 2.495s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1363.8705 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04376876-0.004615_lighter _ lighter_0.69176143.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n04376876-0.004615_lighter _ lighter_0.69176143.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04442312-0.001690_washing machine _ washing machine_0.8494714.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04442312-0.001690_washing machine _ washing machine_0.8494714.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 124,380B, BPFP=0.2361 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 292,964B, BPFP=0.5561 +⌛️ [2/4] FRONTEND: Frontend time: 2.611s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.496s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.08302300 12.78346031 + layer.39.0 28.24609944 2555.86200194 + ------------------------------------------------------------------------------------- + TOTAL 18.16456122 1284.32273113 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 417344 +BPFP 0.3961 bits/point +EBPFP 0.3961 equivalent bits/point +MSE 1284.322731 +---------------------- -------------------------------------------------------- +Time: 5.177s Load: 0.070s, Pack+Encode: 2.611s, Decode+Unpack: 2.496s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1284.3227 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04442312-0.001690_washing machine _ washing machine_0.8494714.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n04442312-0.001690_washing machine _ washing machine_0.8494714.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04456115-0.002573_hair dryer _ envelope_0.34823084.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04456115-0.002573_hair dryer _ envelope_0.34823084.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.087s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 129,932B, BPFP=0.2466 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 323,884B, BPFP=0.6148 +⌛️ [2/4] FRONTEND: Frontend time: 2.622s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.498s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09444211 12.47063954 + layer.39.0 8.80792942 2313.41958212 + ------------------------------------------------------------------------------------- + TOTAL 4.45118577 1162.94511083 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 453816 +BPFP 0.4307 bits/point +EBPFP 0.4307 equivalent bits/point +MSE 1162.945111 +---------------------- -------------------------------------------------------- +Time: 5.208s Load: 0.087s, Pack+Encode: 2.622s, Decode+Unpack: 2.498s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1162.9451 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04456115-0.002573_hair dryer _ envelope_0.34823084.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n04456115-0.002573_hair dryer _ envelope_0.34823084.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04509417-0.000350_sundial _ sundial_0.99936897.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04509417-0.000350_sundial _ sundial_0.99936897.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 165,068B, BPFP=0.3133 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 290,304B, BPFP=0.5510 +⌛️ [2/4] FRONTEND: Frontend time: 2.620s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.501s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10319057 97.07033528 + layer.39.0 8.14296913 1961.03753644 + ------------------------------------------------------------------------------------- + TOTAL 4.12307985 1029.05393586 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 455372 +BPFP 0.4322 bits/point +EBPFP 0.4322 equivalent bits/point +MSE 1029.053936 +---------------------- -------------------------------------------------------- +Time: 5.171s Load: 0.050s, Pack+Encode: 2.620s, Decode+Unpack: 2.501s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1029.0539 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04509417-0.000350_sundial _ sundial_0.99936897.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n04509417-0.000350_sundial _ sundial_0.99936897.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04532670-0.000670_spider web _ spider web_0.70711935.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04532670-0.000670_spider web _ spider web_0.70711935.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 195,428B, BPFP=0.3709 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 328,240B, BPFP=0.6230 +⌛️ [2/4] FRONTEND: Frontend time: 2.603s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.513s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09618602 8.62689523 + layer.39.0 175.41615039 1991.23882410 + ------------------------------------------------------------------------------------- + TOTAL 87.75616821 999.93285967 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 523668 +BPFP 0.4970 bits/point +EBPFP 0.4970 equivalent bits/point +MSE 999.932860 +---------------------- -------------------------------------------------------- +Time: 5.166s Load: 0.050s, Pack+Encode: 2.603s, Decode+Unpack: 2.513s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 999.9329 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04532670-0.000670_spider web _ spider web_0.70711935.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n04532670-0.000670_spider web _ spider web_0.70711935.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04540053-0.000734_balance beam _ balance beam_0.99765223.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04540053-0.000734_balance beam _ balance beam_0.99765223.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 125,248B, BPFP=0.2377 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 271,124B, BPFP=0.5146 +⌛️ [2/4] FRONTEND: Frontend time: 2.605s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.488s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09941827 12.73268950 + layer.39.0 8.11341412 1526.41241497 + ------------------------------------------------------------------------------------- + TOTAL 4.10641619 769.57255224 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 396372 +BPFP 0.3762 bits/point +EBPFP 0.3762 equivalent bits/point +MSE 769.572552 +---------------------- -------------------------------------------------------- +Time: 5.165s Load: 0.072s, Pack+Encode: 2.605s, Decode+Unpack: 2.488s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 769.5726 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04540053-0.000734_balance beam _ balance beam_0.99765223.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n04540053-0.000734_balance beam _ balance beam_0.99765223.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04606251-0.003483_cliff _ cliff_0.9972174.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04606251-0.003483_cliff _ cliff_0.9972174.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 191,712B, BPFP=0.3639 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 277,508B, BPFP=0.5267 +⌛️ [2/4] FRONTEND: Frontend time: 2.613s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.509s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09940710 1.30692890 + layer.39.0 906.86880466 4583.85908649 + ------------------------------------------------------------------------------------- + TOTAL 453.48410588 2292.58300769 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 469220 +BPFP 0.4453 bits/point +EBPFP 0.4453 equivalent bits/point +MSE 2292.583008 +---------------------- -------------------------------------------------------- +Time: 5.193s Load: 0.071s, Pack+Encode: 2.613s, Decode+Unpack: 2.509s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2292.5830 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04606251-0.003483_cliff _ cliff_0.9972174.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n04606251-0.003483_cliff _ cliff_0.9972174.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07583066-0.003935_maraca _ maraca_0.5370013.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07583066-0.003935_maraca _ maraca_0.5370013.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 163,064B, BPFP=0.3095 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 298,764B, BPFP=0.5671 +⌛️ [2/4] FRONTEND: Frontend time: 2.621s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.509s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12045678 61.76540103 + layer.39.0 38.29438092 3615.98493683 + ------------------------------------------------------------------------------------- + TOTAL 19.20741885 1838.87516893 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 461828 +BPFP 0.4383 bits/point +EBPFP 0.4383 equivalent bits/point +MSE 1838.875169 +---------------------- -------------------------------------------------------- +Time: 5.181s Load: 0.051s, Pack+Encode: 2.621s, Decode+Unpack: 2.509s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1838.8752 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07583066-0.003935_maraca _ maraca_0.5370013.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n07583066-0.003935_maraca _ maraca_0.5370013.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07695742-0.003226_ant _ ant_0.64413536.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07695742-0.003226_ant _ ant_0.64413536.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 276,268B, BPFP=0.5244 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 332,960B, BPFP=0.6320 +⌛️ [2/4] FRONTEND: Frontend time: 2.618s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.504s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.16263347 249.06933309 + layer.39.0 172.10254191 3726.25218659 + ------------------------------------------------------------------------------------- + TOTAL 86.13258769 1987.66075984 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 609228 +BPFP 0.5782 bits/point +EBPFP 0.5782 equivalent bits/point +MSE 1987.660760 +---------------------- -------------------------------------------------------- +Time: 5.183s Load: 0.061s, Pack+Encode: 2.618s, Decode+Unpack: 2.504s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1987.6608 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07695742-0.003226_ant _ ant_0.64413536.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n07695742-0.003226_ant _ ant_0.64413536.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07718472-0.001330_spatula _ spatula_0.5388231.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07718472-0.001330_spatula _ spatula_0.5388231.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 177,264B, BPFP=0.3365 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 349,700B, BPFP=0.6638 +⌛️ [2/4] FRONTEND: Frontend time: 2.621s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.522s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09672572 0.88608503 + layer.39.0 34.52145211 5708.14042760 + ------------------------------------------------------------------------------------- + TOTAL 17.30908891 2854.51325631 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 526964 +BPFP 0.5001 bits/point +EBPFP 0.5001 equivalent bits/point +MSE 2854.513256 +---------------------- -------------------------------------------------------- +Time: 5.213s Load: 0.070s, Pack+Encode: 2.621s, Decode+Unpack: 2.522s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2854.5133 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07718472-0.001330_spatula _ spatula_0.5388231.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n07718472-0.001330_spatula _ spatula_0.5388231.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07831146-0.002710_guacamole _ guacamole_0.99510396.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07831146-0.002710_guacamole _ guacamole_0.99510396.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 191,428B, BPFP=0.3633 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 367,432B, BPFP=0.6974 +⌛️ [2/4] FRONTEND: Frontend time: 2.619s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.506s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09717902 25.12988946 + layer.39.0 26.55584533 4776.84936832 + ------------------------------------------------------------------------------------- + TOTAL 13.32651218 2400.98962889 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 558860 +BPFP 0.5304 bits/point +EBPFP 0.5304 equivalent bits/point +MSE 2400.989629 +---------------------- -------------------------------------------------------- +Time: 5.196s Load: 0.070s, Pack+Encode: 2.619s, Decode+Unpack: 2.506s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2400.9896 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07831146-0.002710_guacamole _ guacamole_0.99510396.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n07831146-0.002710_guacamole _ guacamole_0.99510396.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n09229709-0.002628_stethoscope _ stethoscope_0.9726458.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n09229709-0.002628_stethoscope _ stethoscope_0.9726458.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 165,044B, BPFP=0.3133 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 279,184B, BPFP=0.5299 +⌛️ [2/4] FRONTEND: Frontend time: 2.607s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.507s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10247729 24.43291424 + layer.39.0 58.71458181 1626.96768707 + ------------------------------------------------------------------------------------- + TOTAL 29.40852955 825.70030066 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 444228 +BPFP 0.4216 bits/point +EBPFP 0.4216 equivalent bits/point +MSE 825.700301 +---------------------- -------------------------------------------------------- +Time: 5.186s Load: 0.072s, Pack+Encode: 2.607s, Decode+Unpack: 2.507s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 825.7003 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n09229709-0.002628_stethoscope _ stethoscope_0.9726458.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n09229709-0.002628_stethoscope _ stethoscope_0.9726458.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12057211-0.000404_nail _ newt_0.31321314.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12057211-0.000404_nail _ newt_0.31321314.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 274,428B, BPFP=0.5209 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 256,636B, BPFP=0.4871 +⌛️ [2/4] FRONTEND: Frontend time: 2.635s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.501s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11577855 107.53465136 + layer.39.0 8.72387956 1669.67832847 + ------------------------------------------------------------------------------------- + TOTAL 4.41982905 888.60648992 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 531064 +BPFP 0.5040 bits/point +EBPFP 0.5040 equivalent bits/point +MSE 888.606490 +---------------------- -------------------------------------------------------- +Time: 5.187s Load: 0.051s, Pack+Encode: 2.635s, Decode+Unpack: 2.501s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 888.6065 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12057211-0.000404_nail _ newt_0.31321314.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n12057211-0.000404_nail _ newt_0.31321314.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12144580-0.002806_banana _ banana_0.999156.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12144580-0.002806_banana _ banana_0.999156.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 189,316B, BPFP=0.3593 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 359,416B, BPFP=0.6822 +⌛️ [2/4] FRONTEND: Frontend time: 2.613s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.491s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09629347 0.89375511 + layer.39.0 105.38953930 7496.23420797 + ------------------------------------------------------------------------------------- + TOTAL 52.74291638 3748.56398154 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 548732 +BPFP 0.5208 bits/point +EBPFP 0.5208 equivalent bits/point +MSE 3748.563982 +---------------------- -------------------------------------------------------- +Time: 5.175s Load: 0.071s, Pack+Encode: 2.613s, Decode+Unpack: 2.491s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3748.5640 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12144580-0.002806_banana _ banana_0.999156.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n12144580-0.002806_banana _ banana_0.999156.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12267677-0.004617_pomegranate _ pomegranate_0.9159373.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12267677-0.004617_pomegranate _ pomegranate_0.9159373.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 191,988B, BPFP=0.3644 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 326,376B, BPFP=0.6195 +⌛️ [2/4] FRONTEND: Frontend time: 2.620s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.506s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10323383 9.01202054 + layer.39.0 78.12042942 2936.60835763 + ------------------------------------------------------------------------------------- + TOTAL 39.11183162 1472.81018909 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 518364 +BPFP 0.4919 bits/point +EBPFP 0.4919 equivalent bits/point +MSE 1472.810189 +---------------------- -------------------------------------------------------- +Time: 5.185s Load: 0.059s, Pack+Encode: 2.620s, Decode+Unpack: 2.506s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1472.8102 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12267677-0.004617_pomegranate _ pomegranate_0.9159373.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1ka/n12267677-0.004617_pomegranate _ pomegranate_0.9159373.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 0.4637 bits/point +Avg EBPFP 0.4637 equivalent bits/point +Avg MSE 1662.453155 +Avg Time 5.177s +------------------------ ---------------------------- diff --git a/lambda0.007/elic-featurecoding-8bit-individual/cls_in1kr/dtufc_elic-featurecoding_dinov3-total_individual.log b/lambda0.007/elic-featurecoding-8bit-individual/cls_in1kr/dtufc_elic-featurecoding_dinov3-total_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..e3f4606fc20516eb84c1cd83957051b3b0e797de --- /dev/null +++ b/lambda0.007/elic-featurecoding-8bit-individual/cls_in1kr/dtufc_elic-featurecoding_dinov3-total_individual.log @@ -0,0 +1,9344 @@ +Experiment: dtufc_elic-featurecoding_dinov3-total_individual +Log file: output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/dtufc_elic-featurecoding_dinov3-total_individual.log +DTUFCCodecConfig: + arch: elic-featurecoding + handler: dinov3-total + checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.007_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.007_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 333 +Loaded elic-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.9' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.19' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.29' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.39' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json +Loaded per-key mappings: model=dinov3-total + Keys: ['layer.9', 'layer.19', 'layer.29', 'layer.39'] +---------------- -------------------------------------------------------------------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +Checkpoint codec_weights/elic_hybrid/elic2022-official_lambda0.007_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-DINOv3/imagenet1k-r +Output output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr +---------------- -------------------------------------------------------------------------------------------------------------------- +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01443537-graffiti_3.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01443537-graffiti_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.073s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 176,904B, BPFP=0.3358 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 283,484B, BPFP=0.5381 +⌛️ [2/4] FRONTEND: Frontend time: 2.935s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.633s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09690064 12.66546708 + layer.39.0 23.14008974 1867.33066084 + ------------------------------------------------------------------------------------- + TOTAL 11.61849519 939.99806396 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 460388 +BPFP 0.4369 bits/point +EBPFP 0.4369 equivalent bits/point +MSE 939.998064 +---------------------- -------------------------------------------------------- +Time: 5.641s Load: 0.073s, Pack+Encode: 2.935s, Decode+Unpack: 2.633s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 939.9981 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01443537-graffiti_3.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n01443537-graffiti_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01494475-misc_31.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01494475-misc_31.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.092s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 158,080B, BPFP=0.3000 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 359,072B, BPFP=0.6815 +⌛️ [2/4] FRONTEND: Frontend time: 2.693s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.506s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09558801 0.85221698 + layer.39.0 281.54433916 5799.99757046 + ------------------------------------------------------------------------------------- + TOTAL 140.81996359 2900.42489372 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 517152 +BPFP 0.4908 bits/point +EBPFP 0.4908 equivalent bits/point +MSE 2900.424894 +---------------------- -------------------------------------------------------- +Time: 5.292s Load: 0.092s, Pack+Encode: 2.693s, Decode+Unpack: 2.506s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2900.4249 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01494475-misc_31.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n01494475-misc_31.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01531178-cartoon_22.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01531178-cartoon_22.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 176,608B, BPFP=0.3352 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 292,056B, BPFP=0.5543 +⌛️ [2/4] FRONTEND: Frontend time: 2.619s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.500s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10319715 12.65161773 + layer.39.0 12.97479918 1655.10799320 + ------------------------------------------------------------------------------------- + TOTAL 6.53899817 833.87980547 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 468664 +BPFP 0.4448 bits/point +EBPFP 0.4448 equivalent bits/point +MSE 833.879805 +---------------------- -------------------------------------------------------- +Time: 5.169s Load: 0.050s, Pack+Encode: 2.619s, Decode+Unpack: 2.500s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 833.8798 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01531178-cartoon_22.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n01531178-cartoon_22.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01534433-painting_9.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01534433-painting_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 215,412B, BPFP=0.4089 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 260,532B, BPFP=0.4945 +⌛️ [2/4] FRONTEND: Frontend time: 2.612s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.508s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10660143 38.59005254 + layer.39.0 8.42910859 1940.72254616 + ------------------------------------------------------------------------------------- + TOTAL 4.26785501 989.65629935 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 475944 +BPFP 0.4517 bits/point +EBPFP 0.4517 equivalent bits/point +MSE 989.656299 +---------------------- -------------------------------------------------------- +Time: 5.179s Load: 0.060s, Pack+Encode: 2.612s, Decode+Unpack: 2.508s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 989.6563 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01534433-painting_9.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n01534433-painting_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01632777-toy_21.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01632777-toy_21.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 164,764B, BPFP=0.3127 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 371,236B, BPFP=0.7046 +⌛️ [2/4] FRONTEND: Frontend time: 2.614s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.519s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09516629 25.03806980 + layer.39.0 31.73491595 6042.13994169 + ------------------------------------------------------------------------------------- + TOTAL 15.91504112 3033.58900575 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 536000 +BPFP 0.5087 bits/point +EBPFP 0.5087 equivalent bits/point +MSE 3033.589006 +---------------------- -------------------------------------------------------- +Time: 5.185s Load: 0.052s, Pack+Encode: 2.614s, Decode+Unpack: 2.519s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3033.5890 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01632777-toy_21.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n01632777-toy_21.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01748264-misc_18.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01748264-misc_18.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.057s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 248,740B, BPFP=0.4721 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 341,496B, BPFP=0.6482 +⌛️ [2/4] FRONTEND: Frontend time: 2.630s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.500s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.16139180 26.56431457 + layer.39.0 362.83485180 3619.45116618 + ------------------------------------------------------------------------------------- + TOTAL 181.49812180 1823.00774037 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 590236 +BPFP 0.5602 bits/point +EBPFP 0.5602 equivalent bits/point +MSE 1823.007740 +---------------------- -------------------------------------------------------- +Time: 5.186s Load: 0.057s, Pack+Encode: 2.630s, Decode+Unpack: 2.500s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1823.0077 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01748264-misc_18.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n01748264-misc_18.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01784675-painting_2.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01784675-painting_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 238,988B, BPFP=0.4536 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 319,612B, BPFP=0.6066 +⌛️ [2/4] FRONTEND: Frontend time: 2.614s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.505s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13866578 51.66159500 + layer.39.0 232.10166120 3874.82798834 + ------------------------------------------------------------------------------------- + TOTAL 116.12016349 1963.24479167 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 558600 +BPFP 0.5301 bits/point +EBPFP 0.5301 equivalent bits/point +MSE 1963.244792 +---------------------- -------------------------------------------------------- +Time: 5.170s Load: 0.051s, Pack+Encode: 2.614s, Decode+Unpack: 2.505s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1963.2448 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01784675-painting_2.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n01784675-painting_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01820546-painting_29.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01820546-painting_29.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.081s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 229,196B, BPFP=0.4350 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 310,060B, BPFP=0.5885 +⌛️ [2/4] FRONTEND: Frontend time: 2.636s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.504s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10398871 25.98648187 + layer.39.0 202.99580904 2974.32215743 + ------------------------------------------------------------------------------------- + TOTAL 101.54989888 1500.15431965 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 539256 +BPFP 0.5118 bits/point +EBPFP 0.5118 equivalent bits/point +MSE 1500.154320 +---------------------- -------------------------------------------------------- +Time: 5.221s Load: 0.081s, Pack+Encode: 2.636s, Decode+Unpack: 2.504s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1500.1543 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01820546-painting_29.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n01820546-painting_29.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01833805-painting_23.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01833805-painting_23.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 180,672B, BPFP=0.3429 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 291,452B, BPFP=0.5532 +⌛️ [2/4] FRONTEND: Frontend time: 2.632s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.516s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09675035 24.78306077 + layer.39.0 56.43029868 2066.40986395 + ------------------------------------------------------------------------------------- + TOTAL 28.26352451 1045.59646236 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 472124 +BPFP 0.4481 bits/point +EBPFP 0.4481 equivalent bits/point +MSE 1045.596462 +---------------------- -------------------------------------------------------- +Time: 5.197s Load: 0.050s, Pack+Encode: 2.632s, Decode+Unpack: 2.516s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1045.5965 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01833805-painting_23.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n01833805-painting_23.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01860187-painting_2.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01860187-painting_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 176,400B, BPFP=0.3348 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 282,420B, BPFP=0.5361 +⌛️ [2/4] FRONTEND: Frontend time: 2.615s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.511s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09532418 1.29171732 + layer.39.0 11.39113179 1597.28765792 + ------------------------------------------------------------------------------------- + TOTAL 5.74322799 799.28968762 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 458820 +BPFP 0.4354 bits/point +EBPFP 0.4354 equivalent bits/point +MSE 799.289688 +---------------------- -------------------------------------------------------- +Time: 5.177s Load: 0.051s, Pack+Encode: 2.615s, Decode+Unpack: 2.511s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 799.2897 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01860187-painting_2.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n01860187-painting_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01944390-deviantart_6.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01944390-deviantart_6.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 175,908B, BPFP=0.3339 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 296,400B, BPFP=0.5626 +⌛️ [2/4] FRONTEND: Frontend time: 2.620s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.522s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10713051 25.16898043 + layer.39.0 82.30322218 3249.25461613 + ------------------------------------------------------------------------------------- + TOTAL 41.20517635 1637.21179828 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 472308 +BPFP 0.4482 bits/point +EBPFP 0.4482 equivalent bits/point +MSE 1637.211798 +---------------------- -------------------------------------------------------- +Time: 5.193s Load: 0.051s, Pack+Encode: 2.620s, Decode+Unpack: 2.522s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1637.2118 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01944390-deviantart_6.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n01944390-deviantart_6.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01983481-misc_6.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01983481-misc_6.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 213,800B, BPFP=0.4058 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 308,116B, BPFP=0.5848 +⌛️ [2/4] FRONTEND: Frontend time: 2.607s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.523s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10315659 37.76362442 + layer.39.0 236.29731535 3963.84378037 + ------------------------------------------------------------------------------------- + TOTAL 118.20023597 2000.80370240 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 521916 +BPFP 0.4953 bits/point +EBPFP 0.4953 equivalent bits/point +MSE 2000.803702 +---------------------- -------------------------------------------------------- +Time: 5.191s Load: 0.061s, Pack+Encode: 2.607s, Decode+Unpack: 2.523s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2000.8037 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01983481-misc_6.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n01983481-misc_6.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02051845-cartoon_2.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02051845-cartoon_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.089s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 180,676B, BPFP=0.3429 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 302,916B, BPFP=0.5750 +⌛️ [2/4] FRONTEND: Frontend time: 2.620s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.512s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11657756 8.60376143 + layer.39.0 123.57765428 1843.24696307 + ------------------------------------------------------------------------------------- + TOTAL 61.84711592 925.92536225 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 483592 +BPFP 0.4589 bits/point +EBPFP 0.4589 equivalent bits/point +MSE 925.925362 +---------------------- -------------------------------------------------------- +Time: 5.222s Load: 0.089s, Pack+Encode: 2.620s, Decode+Unpack: 2.512s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 925.9254 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02051845-cartoon_2.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n02051845-cartoon_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02056570-art_2.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02056570-art_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.057s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 157,164B, BPFP=0.2983 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 293,480B, BPFP=0.5570 +⌛️ [2/4] FRONTEND: Frontend time: 2.609s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.509s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09569211 12.54302095 + layer.39.0 33.39981930 3711.38216715 + ------------------------------------------------------------------------------------- + TOTAL 16.74775571 1861.96259405 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 450644 +BPFP 0.4277 bits/point +EBPFP 0.4277 equivalent bits/point +MSE 1861.962594 +---------------------- -------------------------------------------------------- +Time: 5.175s Load: 0.057s, Pack+Encode: 2.609s, Decode+Unpack: 2.509s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1861.9626 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02056570-art_2.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n02056570-art_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02085620-misc_90.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02085620-misc_90.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 176,944B, BPFP=0.3359 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 352,216B, BPFP=0.6685 +⌛️ [2/4] FRONTEND: Frontend time: 2.620s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.514s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09843166 1.30416660 + layer.39.0 72.76188958 5336.98688047 + ------------------------------------------------------------------------------------- + TOTAL 36.43016062 2669.14552353 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 529160 +BPFP 0.5022 bits/point +EBPFP 0.5022 equivalent bits/point +MSE 2669.145524 +---------------------- -------------------------------------------------------- +Time: 5.185s Load: 0.051s, Pack+Encode: 2.620s, Decode+Unpack: 2.514s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2669.1455 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02085620-misc_90.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n02085620-misc_90.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088094-misc_39.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088094-misc_39.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 189,436B, BPFP=0.3596 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 284,036B, BPFP=0.5391 +⌛️ [2/4] FRONTEND: Frontend time: 2.619s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.502s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09820385 21.34603909 + layer.39.0 12.32374423 2417.81195335 + ------------------------------------------------------------------------------------- + TOTAL 6.21097404 1219.57899622 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 473472 +BPFP 0.4493 bits/point +EBPFP 0.4493 equivalent bits/point +MSE 1219.578996 +---------------------- -------------------------------------------------------- +Time: 5.170s Load: 0.050s, Pack+Encode: 2.619s, Decode+Unpack: 2.502s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1219.5790 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088094-misc_39.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n02088094-misc_39.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088466-sketch_11.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088466-sketch_11.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.056s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 138,200B, BPFP=0.2623 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 318,528B, BPFP=0.6046 +⌛️ [2/4] FRONTEND: Frontend time: 2.632s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.507s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09459993 0.83743633 + layer.39.0 16.33682960 2616.69897959 + ------------------------------------------------------------------------------------- + TOTAL 8.21571477 1308.76820796 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 456728 +BPFP 0.4335 bits/point +EBPFP 0.4335 equivalent bits/point +MSE 1308.768208 +---------------------- -------------------------------------------------------- +Time: 5.195s Load: 0.056s, Pack+Encode: 2.632s, Decode+Unpack: 2.507s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1308.7682 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088466-sketch_11.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n02088466-sketch_11.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02094433-misc_20.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02094433-misc_20.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 176,668B, BPFP=0.3353 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 312,444B, BPFP=0.5930 +⌛️ [2/4] FRONTEND: Frontend time: 2.639s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.511s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09538842 20.84162415 + layer.39.0 94.83275632 4182.46841594 + ------------------------------------------------------------------------------------- + TOTAL 47.46407237 2101.65502004 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 489112 +BPFP 0.4642 bits/point +EBPFP 0.4642 equivalent bits/point +MSE 2101.655020 +---------------------- -------------------------------------------------------- +Time: 5.202s Load: 0.051s, Pack+Encode: 2.639s, Decode+Unpack: 2.511s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2101.6550 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02094433-misc_20.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n02094433-misc_20.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02097298-misc_9.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02097298-misc_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 259,592B, BPFP=0.4927 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 322,176B, BPFP=0.6115 +⌛️ [2/4] FRONTEND: Frontend time: 2.635s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.520s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11199322 51.88373117 + layer.39.0 26.16675018 2453.75170068 + ------------------------------------------------------------------------------------- + TOTAL 13.13937170 1252.81771593 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 581768 +BPFP 0.5521 bits/point +EBPFP 0.5521 equivalent bits/point +MSE 1252.817716 +---------------------- -------------------------------------------------------- +Time: 5.206s Load: 0.051s, Pack+Encode: 2.635s, Decode+Unpack: 2.520s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1252.8177 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02097298-misc_9.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n02097298-misc_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02106662-misc_55.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02106662-misc_55.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 191,036B, BPFP=0.3626 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 317,552B, BPFP=0.6027 +⌛️ [2/4] FRONTEND: Frontend time: 2.629s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.508s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09642073 24.42017052 + layer.39.0 14.86428154 2170.92274052 + ------------------------------------------------------------------------------------- + TOTAL 7.48035113 1097.67145552 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 508588 +BPFP 0.4827 bits/point +EBPFP 0.4827 equivalent bits/point +MSE 1097.671456 +---------------------- -------------------------------------------------------- +Time: 5.216s Load: 0.080s, Pack+Encode: 2.629s, Decode+Unpack: 2.508s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1097.6715 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02106662-misc_55.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n02106662-misc_55.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02109525-sketch_23.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02109525-sketch_23.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 148,108B, BPFP=0.2811 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 329,704B, BPFP=0.6258 +⌛️ [2/4] FRONTEND: Frontend time: 2.644s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.508s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09568003 12.81082494 + layer.39.0 14.01675815 2463.64528669 + ------------------------------------------------------------------------------------- + TOTAL 7.05621909 1238.22805581 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 477812 +BPFP 0.4535 bits/point +EBPFP 0.4535 equivalent bits/point +MSE 1238.228056 +---------------------- -------------------------------------------------------- +Time: 5.223s Load: 0.071s, Pack+Encode: 2.644s, Decode+Unpack: 2.508s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1238.2281 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02109525-sketch_23.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n02109525-sketch_23.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110185-painting_33.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110185-painting_33.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.056s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 165,772B, BPFP=0.3146 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 359,372B, BPFP=0.6821 +⌛️ [2/4] FRONTEND: Frontend time: 2.618s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.516s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09599521 0.86652246 + layer.39.0 22.05506522 2742.88459670 + ------------------------------------------------------------------------------------- + TOTAL 11.07553021 1371.87555958 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 525144 +BPFP 0.4984 bits/point +EBPFP 0.4984 equivalent bits/point +MSE 1371.875560 +---------------------- -------------------------------------------------------- +Time: 5.190s Load: 0.056s, Pack+Encode: 2.618s, Decode+Unpack: 2.516s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1371.8756 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110185-painting_33.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n02110185-painting_33.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110341-misc_162.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110341-misc_162.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 157,336B, BPFP=0.2986 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 281,412B, BPFP=0.5341 +⌛️ [2/4] FRONTEND: Frontend time: 2.626s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.508s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11124049 8.77268871 + layer.39.0 14.33747210 1693.38411079 + ------------------------------------------------------------------------------------- + TOTAL 7.22435629 851.07839975 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 438748 +BPFP 0.4164 bits/point +EBPFP 0.4164 equivalent bits/point +MSE 851.078400 +---------------------- -------------------------------------------------------- +Time: 5.184s Load: 0.050s, Pack+Encode: 2.626s, Decode+Unpack: 2.508s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 851.0784 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110341-misc_162.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n02110341-misc_162.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02165456-tattoo_37.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02165456-tattoo_37.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 205,540B, BPFP=0.3901 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 319,468B, BPFP=0.6064 +⌛️ [2/4] FRONTEND: Frontend time: 2.624s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.499s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09780899 12.46730936 + layer.39.0 88.96013271 2765.25655977 + ------------------------------------------------------------------------------------- + TOTAL 44.52897085 1388.86193456 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 525008 +BPFP 0.4983 bits/point +EBPFP 0.4983 equivalent bits/point +MSE 1388.861935 +---------------------- -------------------------------------------------------- +Time: 5.174s Load: 0.051s, Pack+Encode: 2.624s, Decode+Unpack: 2.499s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1388.8619 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02165456-tattoo_37.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n02165456-tattoo_37.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02219486-misc_3.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02219486-misc_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 142,224B, BPFP=0.2700 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 255,864B, BPFP=0.4857 +⌛️ [2/4] FRONTEND: Frontend time: 2.613s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.517s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10021695 12.70120319 + layer.39.0 75.73793580 1291.40974247 + ------------------------------------------------------------------------------------- + TOTAL 37.91907638 652.05547283 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 398088 +BPFP 0.3778 bits/point +EBPFP 0.3778 equivalent bits/point +MSE 652.055473 +---------------------- -------------------------------------------------------- +Time: 5.181s Load: 0.051s, Pack+Encode: 2.613s, Decode+Unpack: 2.517s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 652.0555 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02219486-misc_3.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n02219486-misc_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02226429-tattoo_0.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02226429-tattoo_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.058s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 172,528B, BPFP=0.3275 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 311,016B, BPFP=0.5903 +⌛️ [2/4] FRONTEND: Frontend time: 2.614s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.494s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09506506 12.09985518 + layer.39.0 201.13660107 3037.60325559 + ------------------------------------------------------------------------------------- + TOTAL 100.61583306 1524.85155538 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 483544 +BPFP 0.4589 bits/point +EBPFP 0.4589 equivalent bits/point +MSE 1524.851555 +---------------------- -------------------------------------------------------- +Time: 5.166s Load: 0.058s, Pack+Encode: 2.614s, Decode+Unpack: 2.494s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1524.8516 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02226429-tattoo_0.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n02226429-tattoo_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02233338-tattoo_5.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02233338-tattoo_5.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.056s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 168,948B, BPFP=0.3207 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 367,560B, BPFP=0.6977 +⌛️ [2/4] FRONTEND: Frontend time: 2.629s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.517s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09502332 12.45698285 + layer.39.0 172.43500972 4217.99465500 + ------------------------------------------------------------------------------------- + TOTAL 86.26501652 2115.22581893 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 536508 +BPFP 0.5092 bits/point +EBPFP 0.5092 equivalent bits/point +MSE 2115.225819 +---------------------- -------------------------------------------------------- +Time: 5.202s Load: 0.056s, Pack+Encode: 2.629s, Decode+Unpack: 2.517s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2115.2258 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02233338-tattoo_5.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n02233338-tattoo_5.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02279972-painting_2.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02279972-painting_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.055s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 254,380B, BPFP=0.4828 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 336,860B, BPFP=0.6394 +⌛️ [2/4] FRONTEND: Frontend time: 2.629s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.521s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11337867 102.84441053 + layer.39.0 361.17623299 3198.58309038 + ------------------------------------------------------------------------------------- + TOTAL 180.64480583 1650.71375046 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 591240 +BPFP 0.5611 bits/point +EBPFP 0.5611 equivalent bits/point +MSE 1650.713750 +---------------------- -------------------------------------------------------- +Time: 5.205s Load: 0.055s, Pack+Encode: 2.629s, Decode+Unpack: 2.521s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1650.7138 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02279972-painting_2.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n02279972-painting_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02317335-sculpture_0.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02317335-sculpture_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.049s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 168,152B, BPFP=0.3192 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 376,648B, BPFP=0.7149 +⌛️ [2/4] FRONTEND: Frontend time: 2.643s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.522s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09546056 12.87703569 + layer.39.0 1163.18707483 4854.28911565 + ------------------------------------------------------------------------------------- + TOTAL 581.64126769 2433.58307567 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 544800 +BPFP 0.5170 bits/point +EBPFP 0.5170 equivalent bits/point +MSE 2433.583076 +---------------------- -------------------------------------------------------- +Time: 5.214s Load: 0.049s, Pack+Encode: 2.643s, Decode+Unpack: 2.522s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2433.5831 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02317335-sculpture_0.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n02317335-sculpture_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02346627-painting_9.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02346627-painting_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 215,316B, BPFP=0.4087 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 330,472B, BPFP=0.6273 +⌛️ [2/4] FRONTEND: Frontend time: 2.632s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.527s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13205896 14.14190905 + layer.39.0 503.01482021 4303.05830904 + ------------------------------------------------------------------------------------- + TOTAL 251.57343959 2158.60010904 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 545788 +BPFP 0.5180 bits/point +EBPFP 0.5180 equivalent bits/point +MSE 2158.600109 +---------------------- -------------------------------------------------------- +Time: 5.210s Load: 0.050s, Pack+Encode: 2.632s, Decode+Unpack: 2.527s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2158.6001 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02346627-painting_9.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n02346627-painting_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02391049-deviantart_0.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02391049-deviantart_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.091s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 164,020B, BPFP=0.3113 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 277,336B, BPFP=0.5264 +⌛️ [2/4] FRONTEND: Frontend time: 2.659s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.500s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10116939 13.20759118 + layer.39.0 17.42674737 1809.73177843 + ------------------------------------------------------------------------------------- + TOTAL 8.76395838 911.46968480 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 441356 +BPFP 0.4189 bits/point +EBPFP 0.4189 equivalent bits/point +MSE 911.469685 +---------------------- -------------------------------------------------------- +Time: 5.251s Load: 0.091s, Pack+Encode: 2.659s, Decode+Unpack: 2.500s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 911.4697 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02391049-deviantart_0.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n02391049-deviantart_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02395406-sculpture_31.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02395406-sculpture_31.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 257,556B, BPFP=0.4889 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 276,108B, BPFP=0.5241 +⌛️ [2/4] FRONTEND: Frontend time: 2.622s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.495s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11469608 51.27530901 + layer.39.0 30.55020044 2548.85592809 + ------------------------------------------------------------------------------------- + TOTAL 15.33244826 1300.06561855 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 533664 +BPFP 0.5065 bits/point +EBPFP 0.5065 equivalent bits/point +MSE 1300.065619 +---------------------- -------------------------------------------------------- +Time: 5.167s Load: 0.050s, Pack+Encode: 2.622s, Decode+Unpack: 2.495s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1300.0656 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02395406-sculpture_31.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n02395406-sculpture_31.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02445715-art_3.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02445715-art_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 168,856B, BPFP=0.3205 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 315,272B, BPFP=0.5984 +⌛️ [2/4] FRONTEND: Frontend time: 2.688s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.490s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09587883 8.72768521 + layer.39.0 77.63827138 2391.34863946 + ------------------------------------------------------------------------------------- + TOTAL 38.86707511 1200.03816234 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 484128 +BPFP 0.4595 bits/point +EBPFP 0.4595 equivalent bits/point +MSE 1200.038162 +---------------------- -------------------------------------------------------- +Time: 5.229s Load: 0.051s, Pack+Encode: 2.688s, Decode+Unpack: 2.490s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1200.0382 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02445715-art_3.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n02445715-art_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02672831-sculpture_2.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02672831-sculpture_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.056s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 222,796B, BPFP=0.4229 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 362,632B, BPFP=0.6883 +⌛️ [2/4] FRONTEND: Frontend time: 2.612s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.498s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11638676 49.53089696 + layer.39.0 42.74346681 6811.33867833 + ------------------------------------------------------------------------------------- + TOTAL 21.42992678 3430.43478764 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 585428 +BPFP 0.5556 bits/point +EBPFP 0.5556 equivalent bits/point +MSE 3430.434788 +---------------------- -------------------------------------------------------- +Time: 5.166s Load: 0.056s, Pack+Encode: 2.612s, Decode+Unpack: 2.498s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3430.4348 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02672831-sculpture_2.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n02672831-sculpture_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02701002-videogame_1.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02701002-videogame_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 198,288B, BPFP=0.3764 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 338,768B, BPFP=0.6430 +⌛️ [2/4] FRONTEND: Frontend time: 2.619s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.511s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10320827 36.52620870 + layer.39.0 160.61054422 6162.42905734 + ------------------------------------------------------------------------------------- + TOTAL 80.35687624 3099.47763302 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 537056 +BPFP 0.5097 bits/point +EBPFP 0.5097 equivalent bits/point +MSE 3099.477633 +---------------------- -------------------------------------------------------- +Time: 5.179s Load: 0.050s, Pack+Encode: 2.619s, Decode+Unpack: 2.511s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3099.4776 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02701002-videogame_1.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n02701002-videogame_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02749479-misc_35.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02749479-misc_35.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 154,844B, BPFP=0.2939 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 314,516B, BPFP=0.5970 +⌛️ [2/4] FRONTEND: Frontend time: 2.609s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.524s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09764870 12.30975538 + layer.39.0 172.65676628 4153.47570457 + ------------------------------------------------------------------------------------- + TOTAL 86.37720749 2082.89272997 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 469360 +BPFP 0.4454 bits/point +EBPFP 0.4454 equivalent bits/point +MSE 2082.892730 +---------------------- -------------------------------------------------------- +Time: 5.185s Load: 0.052s, Pack+Encode: 2.609s, Decode+Unpack: 2.524s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2082.8927 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02749479-misc_35.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n02749479-misc_35.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02769748-cartoon_11.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02769748-cartoon_11.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 159,772B, BPFP=0.3033 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 315,068B, BPFP=0.5980 +⌛️ [2/4] FRONTEND: Frontend time: 2.619s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.496s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12263774 1.03713512 + layer.39.0 11.02823964 2083.60787172 + ------------------------------------------------------------------------------------- + TOTAL 5.57543869 1042.32250342 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 474840 +BPFP 0.4506 bits/point +EBPFP 0.4506 equivalent bits/point +MSE 1042.322503 +---------------------- -------------------------------------------------------- +Time: 5.176s Load: 0.060s, Pack+Encode: 2.619s, Decode+Unpack: 2.496s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1042.3225 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02769748-cartoon_11.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n02769748-cartoon_11.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02793495-sketch_11.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02793495-sketch_11.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.057s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 157,860B, BPFP=0.2996 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 272,948B, BPFP=0.5181 +⌛️ [2/4] FRONTEND: Frontend time: 2.616s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.528s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09793751 24.81080501 + layer.39.0 182.75789602 1944.61066569 + ------------------------------------------------------------------------------------- + TOTAL 91.42791676 984.71073535 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 430808 +BPFP 0.4089 bits/point +EBPFP 0.4089 equivalent bits/point +MSE 984.710735 +---------------------- -------------------------------------------------------- +Time: 5.201s Load: 0.057s, Pack+Encode: 2.616s, Decode+Unpack: 2.528s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 984.7107 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02793495-sketch_11.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n02793495-sketch_11.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02797295-misc_13.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02797295-misc_13.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 287,568B, BPFP=0.5458 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 366,464B, BPFP=0.6956 +⌛️ [2/4] FRONTEND: Frontend time: 2.635s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.510s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.17140635 102.55818756 + layer.39.0 172.50999150 8526.02332362 + ------------------------------------------------------------------------------------- + TOTAL 86.34069892 4314.29075559 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 654032 +BPFP 0.6207 bits/point +EBPFP 0.6207 equivalent bits/point +MSE 4314.290756 +---------------------- -------------------------------------------------------- +Time: 5.198s Load: 0.052s, Pack+Encode: 2.635s, Decode+Unpack: 2.510s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4314.2908 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02797295-misc_13.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n02797295-misc_13.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02802426-art_1.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02802426-art_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 263,572B, BPFP=0.5003 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 384,344B, BPFP=0.7295 +⌛️ [2/4] FRONTEND: Frontend time: 2.621s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.523s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.16523854 185.44336127 + layer.39.0 477.65184645 6450.70456754 + ------------------------------------------------------------------------------------- + TOTAL 238.90854250 3318.07396441 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 647916 +BPFP 0.6149 bits/point +EBPFP 0.6149 equivalent bits/point +MSE 3318.073964 +---------------------- -------------------------------------------------------- +Time: 5.214s Load: 0.070s, Pack+Encode: 2.621s, Decode+Unpack: 2.523s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3318.0740 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02802426-art_1.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n02802426-art_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02814860-sticker_8.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02814860-sticker_8.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 186,808B, BPFP=0.3546 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 307,384B, BPFP=0.5834 +⌛️ [2/4] FRONTEND: Frontend time: 2.612s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.515s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12757226 1.69319744 + layer.39.0 19.27598852 1636.31523324 + ------------------------------------------------------------------------------------- + TOTAL 9.70178039 819.00421534 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 494192 +BPFP 0.4690 bits/point +EBPFP 0.4690 equivalent bits/point +MSE 819.004215 +---------------------- -------------------------------------------------------- +Time: 5.179s Load: 0.052s, Pack+Encode: 2.612s, Decode+Unpack: 2.515s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 819.0042 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02814860-sticker_8.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n02814860-sticker_8.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02841315-graphic_4.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02841315-graphic_4.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.057s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 199,020B, BPFP=0.3778 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 368,076B, BPFP=0.6986 +⌛️ [2/4] FRONTEND: Frontend time: 2.587s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.501s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11826141 12.74743190 + layer.39.0 55.46440340 5070.89407191 + ------------------------------------------------------------------------------------- + TOTAL 27.79133240 2541.82075191 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 567096 +BPFP 0.5382 bits/point +EBPFP 0.5382 equivalent bits/point +MSE 2541.820752 +---------------------- -------------------------------------------------------- +Time: 5.145s Load: 0.057s, Pack+Encode: 2.587s, Decode+Unpack: 2.501s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2541.8208 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02841315-graphic_4.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n02841315-graphic_4.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02843684-cartoon_9.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02843684-cartoon_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.065s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 228,028B, BPFP=0.4328 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 308,980B, BPFP=0.5865 +⌛️ [2/4] FRONTEND: Frontend time: 2.607s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.519s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12386809 86.71112731 + layer.39.0 312.00962707 2475.88022352 + ------------------------------------------------------------------------------------- + TOTAL 156.06674758 1281.29567541 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 537008 +BPFP 0.5096 bits/point +EBPFP 0.5096 equivalent bits/point +MSE 1281.295675 +---------------------- -------------------------------------------------------- +Time: 5.191s Load: 0.065s, Pack+Encode: 2.607s, Decode+Unpack: 2.519s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1281.2957 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02843684-cartoon_9.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n02843684-cartoon_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02883205-sketch_15.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02883205-sketch_15.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 166,036B, BPFP=0.3151 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 304,104B, BPFP=0.5772 +⌛️ [2/4] FRONTEND: Frontend time: 2.616s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.492s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09796664 25.80879495 + layer.39.0 103.64267493 3300.55126336 + ------------------------------------------------------------------------------------- + TOTAL 51.87032078 1663.18002915 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 470140 +BPFP 0.4462 bits/point +EBPFP 0.4462 equivalent bits/point +MSE 1663.180029 +---------------------- -------------------------------------------------------- +Time: 5.158s Load: 0.050s, Pack+Encode: 2.616s, Decode+Unpack: 2.492s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1663.1800 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02883205-sketch_15.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n02883205-sketch_15.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02906734-graffiti_3.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02906734-graffiti_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 309,940B, BPFP=0.5883 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 376,004B, BPFP=0.7137 +⌛️ [2/4] FRONTEND: Frontend time: 2.619s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.503s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.17339475 161.92337828 + layer.39.0 166.12656402 5278.33819242 + ------------------------------------------------------------------------------------- + TOTAL 83.14997939 2720.13078535 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 685944 +BPFP 0.6510 bits/point +EBPFP 0.6510 equivalent bits/point +MSE 2720.130785 +---------------------- -------------------------------------------------------- +Time: 5.174s Load: 0.051s, Pack+Encode: 2.619s, Decode+Unpack: 2.503s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2720.1308 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02906734-graffiti_3.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n02906734-graffiti_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02909870-sketch_0.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02909870-sketch_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 202,940B, BPFP=0.3852 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 289,700B, BPFP=0.5499 +⌛️ [2/4] FRONTEND: Frontend time: 2.627s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.503s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.15317524 14.33267850 + layer.39.0 167.75886783 1793.34353741 + ------------------------------------------------------------------------------------- + TOTAL 83.95602154 903.83810796 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 492640 +BPFP 0.4675 bits/point +EBPFP 0.4675 equivalent bits/point +MSE 903.838108 +---------------------- -------------------------------------------------------- +Time: 5.180s Load: 0.050s, Pack+Encode: 2.627s, Decode+Unpack: 2.503s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 903.8381 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02909870-sketch_0.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n02909870-sketch_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02939185-painting_0.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02939185-painting_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.079s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 145,916B, BPFP=0.2770 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 308,424B, BPFP=0.5854 +⌛️ [2/4] FRONTEND: Frontend time: 2.635s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.512s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09512242 12.59265386 + layer.39.0 131.28711127 2515.15111759 + ------------------------------------------------------------------------------------- + TOTAL 65.69111684 1263.87188572 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 454340 +BPFP 0.4312 bits/point +EBPFP 0.4312 equivalent bits/point +MSE 1263.871886 +---------------------- -------------------------------------------------------- +Time: 5.226s Load: 0.079s, Pack+Encode: 2.635s, Decode+Unpack: 2.512s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1263.8719 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02939185-painting_0.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n02939185-painting_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02948072-misc_10.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02948072-misc_10.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 159,384B, BPFP=0.3025 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 346,184B, BPFP=0.6571 +⌛️ [2/4] FRONTEND: Frontend time: 2.621s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.508s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09566823 12.31219726 + layer.39.0 102.81622783 4604.15160350 + ------------------------------------------------------------------------------------- + TOTAL 51.45594803 2308.23190038 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 505568 +BPFP 0.4798 bits/point +EBPFP 0.4798 equivalent bits/point +MSE 2308.231900 +---------------------- -------------------------------------------------------- +Time: 5.189s Load: 0.060s, Pack+Encode: 2.621s, Decode+Unpack: 2.508s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2308.2319 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02948072-misc_10.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n02948072-misc_10.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02950826-art_1.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02950826-art_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.088s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 175,104B, BPFP=0.3324 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 384,396B, BPFP=0.7296 +⌛️ [2/4] FRONTEND: Frontend time: 2.631s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.506s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09506074 12.62566907 + layer.39.0 1071.96149174 4055.47667638 + ------------------------------------------------------------------------------------- + TOTAL 536.02827624 2034.05117273 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 559500 +BPFP 0.5310 bits/point +EBPFP 0.5310 equivalent bits/point +MSE 2034.051173 +---------------------- -------------------------------------------------------- +Time: 5.224s Load: 0.088s, Pack+Encode: 2.631s, Decode+Unpack: 2.506s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2034.0512 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02950826-art_1.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n02950826-art_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02951358-misc_1.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02951358-misc_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 140,628B, BPFP=0.2669 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 261,372B, BPFP=0.4961 +⌛️ [2/4] FRONTEND: Frontend time: 2.607s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.505s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09568294 0.85744874 + layer.39.0 598.97078474 2921.65864917 + ------------------------------------------------------------------------------------- + TOTAL 299.53323384 1461.25804896 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 402000 +BPFP 0.3815 bits/point +EBPFP 0.3815 equivalent bits/point +MSE 1461.258049 +---------------------- -------------------------------------------------------- +Time: 5.182s Load: 0.070s, Pack+Encode: 2.607s, Decode+Unpack: 2.505s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1461.2580 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02951358-misc_1.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n02951358-misc_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02966193-cartoon_22.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02966193-cartoon_22.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 268,316B, BPFP=0.5093 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 377,852B, BPFP=0.7172 +⌛️ [2/4] FRONTEND: Frontend time: 2.629s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.510s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10376222 103.77871416 + layer.39.0 767.85532070 9099.20019436 + ------------------------------------------------------------------------------------- + TOTAL 383.97954146 4601.48945426 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 646168 +BPFP 0.6132 bits/point +EBPFP 0.6132 equivalent bits/point +MSE 4601.489454 +---------------------- -------------------------------------------------------- +Time: 5.190s Load: 0.051s, Pack+Encode: 2.629s, Decode+Unpack: 2.510s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4601.4895 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02966193-cartoon_22.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n02966193-cartoon_22.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02980441-graphic_3.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02980441-graphic_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.090s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 140,432B, BPFP=0.2666 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 224,800B, BPFP=0.4267 +⌛️ [2/4] FRONTEND: Frontend time: 2.637s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.492s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09509088 12.52360073 + layer.39.0 13.13791359 1997.94630709 + ------------------------------------------------------------------------------------- + TOTAL 6.61650224 1005.23495391 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 365232 +BPFP 0.3466 bits/point +EBPFP 0.3466 equivalent bits/point +MSE 1005.234954 +---------------------- -------------------------------------------------------- +Time: 5.219s Load: 0.090s, Pack+Encode: 2.637s, Decode+Unpack: 2.492s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1005.2350 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02980441-graphic_3.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n02980441-graphic_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03124170-painting_15.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03124170-painting_15.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 211,320B, BPFP=0.4011 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 422,992B, BPFP=0.8029 +⌛️ [2/4] FRONTEND: Frontend time: 2.632s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.508s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10783903 14.25067382 + layer.39.0 326.57091229 9451.02623907 + ------------------------------------------------------------------------------------- + TOTAL 163.33937566 4732.63845644 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 634312 +BPFP 0.6020 bits/point +EBPFP 0.6020 equivalent bits/point +MSE 4732.638456 +---------------------- -------------------------------------------------------- +Time: 5.191s Load: 0.050s, Pack+Encode: 2.632s, Decode+Unpack: 2.508s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4732.6385 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03124170-painting_15.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n03124170-painting_15.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03345487-toy_17.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03345487-toy_17.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 174,692B, BPFP=0.3316 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 321,488B, BPFP=0.6102 +⌛️ [2/4] FRONTEND: Frontend time: 2.628s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.502s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10662318 12.29121397 + layer.39.0 198.63900024 7027.07240039 + ------------------------------------------------------------------------------------- + TOTAL 99.37281171 3519.68180718 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 496180 +BPFP 0.4709 bits/point +EBPFP 0.4709 equivalent bits/point +MSE 3519.681807 +---------------------- -------------------------------------------------------- +Time: 5.190s Load: 0.060s, Pack+Encode: 2.628s, Decode+Unpack: 2.502s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3519.6818 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03345487-toy_17.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n03345487-toy_17.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03372029-sketch_14.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03372029-sketch_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 214,948B, BPFP=0.4080 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 350,076B, BPFP=0.6645 +⌛️ [2/4] FRONTEND: Frontend time: 2.611s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.501s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12162214 64.16744108 + layer.39.0 228.06095117 3773.41642371 + ------------------------------------------------------------------------------------- + TOTAL 114.09128665 1918.79193240 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 565024 +BPFP 0.5362 bits/point +EBPFP 0.5362 equivalent bits/point +MSE 1918.791932 +---------------------- -------------------------------------------------------- +Time: 5.163s Load: 0.051s, Pack+Encode: 2.611s, Decode+Unpack: 2.501s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1918.7919 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03372029-sketch_14.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n03372029-sketch_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03424325-misc_4.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03424325-misc_4.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.092s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 204,292B, BPFP=0.3878 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 348,848B, BPFP=0.6621 +⌛️ [2/4] FRONTEND: Frontend time: 2.629s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.514s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10761499 1.34899463 + layer.39.0 21.03287666 4768.92419825 + ------------------------------------------------------------------------------------- + TOTAL 10.57024582 2385.13659644 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 553140 +BPFP 0.5250 bits/point +EBPFP 0.5250 equivalent bits/point +MSE 2385.136596 +---------------------- -------------------------------------------------------- +Time: 5.235s Load: 0.092s, Pack+Encode: 2.629s, Decode+Unpack: 2.514s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2385.1366 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03424325-misc_4.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n03424325-misc_4.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03467068-sketch_17.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03467068-sketch_17.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 163,740B, BPFP=0.3108 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 332,808B, BPFP=0.6317 +⌛️ [2/4] FRONTEND: Frontend time: 2.602s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.500s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09564773 12.60110601 + layer.39.0 208.14688107 2402.17541302 + ------------------------------------------------------------------------------------- + TOTAL 104.12126440 1207.38825952 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 496548 +BPFP 0.4712 bits/point +EBPFP 0.4712 equivalent bits/point +MSE 1207.388260 +---------------------- -------------------------------------------------------- +Time: 5.161s Load: 0.059s, Pack+Encode: 2.602s, Decode+Unpack: 2.500s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1207.3883 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03467068-sketch_17.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n03467068-sketch_17.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03481172-sketch_9.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03481172-sketch_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 184,540B, BPFP=0.3503 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 315,412B, BPFP=0.5987 +⌛️ [2/4] FRONTEND: Frontend time: 2.616s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.512s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14641065 37.45800307 + layer.39.0 516.28267736 3585.01773567 + ------------------------------------------------------------------------------------- + TOTAL 258.21454400 1811.23786937 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 499952 +BPFP 0.4745 bits/point +EBPFP 0.4745 equivalent bits/point +MSE 1811.237869 +---------------------- -------------------------------------------------------- +Time: 5.178s Load: 0.050s, Pack+Encode: 2.616s, Decode+Unpack: 2.512s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1811.2379 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03481172-sketch_9.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n03481172-sketch_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03494278-deviantart_3.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03494278-deviantart_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.091s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 158,900B, BPFP=0.3016 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 280,500B, BPFP=0.5324 +⌛️ [2/4] FRONTEND: Frontend time: 2.657s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.515s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09714438 12.23388719 + layer.39.0 11.38600982 1732.95286686 + ------------------------------------------------------------------------------------- + TOTAL 5.74157710 872.59337703 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 439400 +BPFP 0.4170 bits/point +EBPFP 0.4170 equivalent bits/point +MSE 872.593377 +---------------------- -------------------------------------------------------- +Time: 5.262s Load: 0.091s, Pack+Encode: 2.657s, Decode+Unpack: 2.515s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 872.5934 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03494278-deviantart_3.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n03494278-deviantart_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03495258-painting_3.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03495258-painting_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 219,332B, BPFP=0.4163 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 354,932B, BPFP=0.6737 +⌛️ [2/4] FRONTEND: Frontend time: 2.610s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.492s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10398556 47.05285775 + layer.39.0 359.17207240 3308.92808552 + ------------------------------------------------------------------------------------- + TOTAL 179.63802898 1677.99047164 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 574264 +BPFP 0.5450 bits/point +EBPFP 0.5450 equivalent bits/point +MSE 1677.990472 +---------------------- -------------------------------------------------------- +Time: 5.174s Load: 0.072s, Pack+Encode: 2.610s, Decode+Unpack: 2.492s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1677.9905 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03495258-painting_3.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n03495258-painting_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03498962-sketch_8.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03498962-sketch_8.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 255,384B, BPFP=0.4847 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 316,028B, BPFP=0.5998 +⌛️ [2/4] FRONTEND: Frontend time: 2.607s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.510s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.16074808 60.89682034 + layer.39.0 476.99061589 3384.23566569 + ------------------------------------------------------------------------------------- + TOTAL 238.57568198 1722.56624302 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 571412 +BPFP 0.5423 bits/point +EBPFP 0.5423 equivalent bits/point +MSE 1722.566243 +---------------------- -------------------------------------------------------- +Time: 5.167s Load: 0.050s, Pack+Encode: 2.607s, Decode+Unpack: 2.510s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1722.5662 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03498962-sketch_8.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n03498962-sketch_8.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03602883-misc_12.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03602883-misc_12.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 137,076B, BPFP=0.2602 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 247,144B, BPFP=0.4691 +⌛️ [2/4] FRONTEND: Frontend time: 2.596s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.494s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.09080038 12.90403779 + layer.39.0 100.93773536 1332.48056365 + ------------------------------------------------------------------------------------- + TOTAL 54.51426787 672.69230072 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 384220 +BPFP 0.3646 bits/point +EBPFP 0.3646 equivalent bits/point +MSE 672.692301 +---------------------- -------------------------------------------------------- +Time: 5.139s Load: 0.050s, Pack+Encode: 2.596s, Decode+Unpack: 2.494s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 672.6923 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03602883-misc_12.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n03602883-misc_12.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03630383-toy_1.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03630383-toy_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 136,352B, BPFP=0.2588 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 311,684B, BPFP=0.5916 +⌛️ [2/4] FRONTEND: Frontend time: 2.622s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.498s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09574974 12.55255216 + layer.39.0 14.66923857 2161.16812439 + ------------------------------------------------------------------------------------- + TOTAL 7.38249415 1086.86033828 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 448036 +BPFP 0.4252 bits/point +EBPFP 0.4252 equivalent bits/point +MSE 1086.860338 +---------------------- -------------------------------------------------------- +Time: 5.172s Load: 0.052s, Pack+Encode: 2.622s, Decode+Unpack: 2.498s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1086.8603 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03630383-toy_1.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n03630383-toy_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03649909-toy_22.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03649909-toy_22.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.090s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 134,724B, BPFP=0.2557 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 215,568B, BPFP=0.4092 +⌛️ [2/4] FRONTEND: Frontend time: 2.646s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.493s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09878858 24.38356794 + layer.39.0 29.68475348 1250.05636540 + ------------------------------------------------------------------------------------- + TOTAL 14.89177103 637.21996667 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 350292 +BPFP 0.3324 bits/point +EBPFP 0.3324 equivalent bits/point +MSE 637.219967 +---------------------- -------------------------------------------------------- +Time: 5.229s Load: 0.090s, Pack+Encode: 2.646s, Decode+Unpack: 2.493s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 637.2200 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03649909-toy_22.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n03649909-toy_22.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03676483-sculpture_3.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03676483-sculpture_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 149,688B, BPFP=0.2841 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 371,248B, BPFP=0.7047 +⌛️ [2/4] FRONTEND: Frontend time: 2.597s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.496s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09491264 0.85769016 + layer.39.0 32.22669916 4890.87949466 + ------------------------------------------------------------------------------------- + TOTAL 16.16080590 2445.86859241 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 520936 +BPFP 0.4944 bits/point +EBPFP 0.4944 equivalent bits/point +MSE 2445.868592 +---------------------- -------------------------------------------------------- +Time: 5.152s Load: 0.060s, Pack+Encode: 2.597s, Decode+Unpack: 2.496s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2445.8686 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03676483-sculpture_3.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n03676483-sculpture_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03710193-sketch_14.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03710193-sketch_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 179,864B, BPFP=0.3414 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 294,904B, BPFP=0.5598 +⌛️ [2/4] FRONTEND: Frontend time: 2.589s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.495s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.47394152 14.70057398 + layer.39.0 335.99814747 2442.87414966 + ------------------------------------------------------------------------------------- + TOTAL 168.23604450 1228.78736182 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 474768 +BPFP 0.4506 bits/point +EBPFP 0.4506 equivalent bits/point +MSE 1228.787362 +---------------------- -------------------------------------------------------- +Time: 5.143s Load: 0.060s, Pack+Encode: 2.589s, Decode+Unpack: 2.495s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1228.7874 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03710193-sketch_14.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n03710193-sketch_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03773504-graphic_4.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03773504-graphic_4.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.056s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 125,072B, BPFP=0.2374 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 248,076B, BPFP=0.4709 +⌛️ [2/4] FRONTEND: Frontend time: 2.647s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.519s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09681199 12.73557554 + layer.39.0 18.83313593 1821.91569485 + ------------------------------------------------------------------------------------- + TOTAL 9.46497396 917.32563519 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 373148 +BPFP 0.3541 bits/point +EBPFP 0.3541 equivalent bits/point +MSE 917.325635 +---------------------- -------------------------------------------------------- +Time: 5.223s Load: 0.056s, Pack+Encode: 2.647s, Decode+Unpack: 2.519s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 917.3256 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03773504-graphic_4.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n03773504-graphic_4.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03775071-painting_1.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03775071-painting_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 196,516B, BPFP=0.3730 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 291,768B, BPFP=0.5538 +⌛️ [2/4] FRONTEND: Frontend time: 2.586s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.497s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11048905 13.08452723 + layer.39.0 386.73560496 2584.92006803 + ------------------------------------------------------------------------------------- + TOTAL 193.42304701 1299.00229763 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 488284 +BPFP 0.4634 bits/point +EBPFP 0.4634 equivalent bits/point +MSE 1299.002298 +---------------------- -------------------------------------------------------- +Time: 5.134s Load: 0.051s, Pack+Encode: 2.586s, Decode+Unpack: 2.497s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1299.0023 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03775071-painting_1.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n03775071-painting_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03888257-cartoon_30.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03888257-cartoon_30.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 193,440B, BPFP=0.3672 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 323,484B, BPFP=0.6140 +⌛️ [2/4] FRONTEND: Frontend time: 2.610s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.487s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13203045 63.03413508 + layer.39.0 375.96832483 2650.37439261 + ------------------------------------------------------------------------------------- + TOTAL 188.05017764 1356.70426385 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 516924 +BPFP 0.4906 bits/point +EBPFP 0.4906 equivalent bits/point +MSE 1356.704264 +---------------------- -------------------------------------------------------- +Time: 5.148s Load: 0.052s, Pack+Encode: 2.610s, Decode+Unpack: 2.487s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1356.7043 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03888257-cartoon_30.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n03888257-cartoon_30.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03930630-toy_2.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03930630-toy_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 129,232B, BPFP=0.2453 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 290,968B, BPFP=0.5523 +⌛️ [2/4] FRONTEND: Frontend time: 2.623s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.504s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09699417 12.65105590 + layer.39.0 46.17573949 3593.75704568 + ------------------------------------------------------------------------------------- + TOTAL 23.13636683 1803.20405079 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 420200 +BPFP 0.3988 bits/point +EBPFP 0.3988 equivalent bits/point +MSE 1803.204051 +---------------------- -------------------------------------------------------- +Time: 5.178s Load: 0.051s, Pack+Encode: 2.623s, Decode+Unpack: 2.504s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1803.2041 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03930630-toy_2.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n03930630-toy_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04086273-sticker_5.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04086273-sticker_5.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.091s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 152,920B, BPFP=0.2903 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 282,092B, BPFP=0.5354 +⌛️ [2/4] FRONTEND: Frontend time: 2.629s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.497s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10161624 12.52337866 + layer.39.0 24.98063198 3039.83090379 + ------------------------------------------------------------------------------------- + TOTAL 12.54112411 1526.17714122 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 435012 +BPFP 0.4128 bits/point +EBPFP 0.4128 equivalent bits/point +MSE 1526.177141 +---------------------- -------------------------------------------------------- +Time: 5.217s Load: 0.091s, Pack+Encode: 2.629s, Decode+Unpack: 2.497s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1526.1771 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04086273-sticker_5.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n04086273-sticker_5.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04118538-sculpture_0.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04118538-sculpture_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.091s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 184,700B, BPFP=0.3506 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 347,092B, BPFP=0.6588 +⌛️ [2/4] FRONTEND: Frontend time: 2.633s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.494s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09846411 12.26447097 + layer.39.0 11.87055944 2408.74173955 + ------------------------------------------------------------------------------------- + TOTAL 5.98451177 1210.50310526 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 531792 +BPFP 0.5047 bits/point +EBPFP 0.5047 equivalent bits/point +MSE 1210.503105 +---------------------- -------------------------------------------------------- +Time: 5.218s Load: 0.091s, Pack+Encode: 2.633s, Decode+Unpack: 2.494s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1210.5031 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04118538-sculpture_0.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n04118538-sculpture_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04133789-cartoon_7.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04133789-cartoon_7.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.081s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 257,640B, BPFP=0.4890 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 362,272B, BPFP=0.6876 +⌛️ [2/4] FRONTEND: Frontend time: 2.673s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.499s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13739287 76.50772898 + layer.39.0 370.52532799 5093.47473275 + ------------------------------------------------------------------------------------- + TOTAL 185.33136043 2584.99123087 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 619912 +BPFP 0.5883 bits/point +EBPFP 0.5883 equivalent bits/point +MSE 2584.991231 +---------------------- -------------------------------------------------------- +Time: 5.254s Load: 0.081s, Pack+Encode: 2.673s, Decode+Unpack: 2.499s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2584.9912 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04133789-cartoon_7.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n04133789-cartoon_7.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04141076-cartoon_14.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04141076-cartoon_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 178,732B, BPFP=0.3392 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 338,124B, BPFP=0.6418 +⌛️ [2/4] FRONTEND: Frontend time: 2.600s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.506s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11960477 24.83421784 + layer.39.0 53.25505649 2473.42468416 + ------------------------------------------------------------------------------------- + TOTAL 26.68733063 1249.12945100 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 516856 +BPFP 0.4905 bits/point +EBPFP 0.4905 equivalent bits/point +MSE 1249.129451 +---------------------- -------------------------------------------------------- +Time: 5.155s Load: 0.050s, Pack+Encode: 2.600s, Decode+Unpack: 2.506s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1249.1295 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04141076-cartoon_14.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n04141076-cartoon_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04146614-misc_4.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04146614-misc_4.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 178,124B, BPFP=0.3381 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 331,936B, BPFP=0.6300 +⌛️ [2/4] FRONTEND: Frontend time: 2.620s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.503s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10047569 25.84741899 + layer.39.0 167.29959305 6584.00194363 + ------------------------------------------------------------------------------------- + TOTAL 83.70003437 3304.92468131 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 510060 +BPFP 0.4841 bits/point +EBPFP 0.4841 equivalent bits/point +MSE 3304.924681 +---------------------- -------------------------------------------------------- +Time: 5.175s Load: 0.052s, Pack+Encode: 2.620s, Decode+Unpack: 2.503s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3304.9247 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04146614-misc_4.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n04146614-misc_4.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04147183-art_9.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04147183-art_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 176,144B, BPFP=0.3343 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 278,972B, BPFP=0.5295 +⌛️ [2/4] FRONTEND: Frontend time: 2.601s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.490s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11332939 58.47381408 + layer.39.0 22.95352360 3098.84596696 + ------------------------------------------------------------------------------------- + TOTAL 11.53342649 1578.65989052 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 455116 +BPFP 0.4319 bits/point +EBPFP 0.4319 equivalent bits/point +MSE 1578.659891 +---------------------- -------------------------------------------------------- +Time: 5.150s Load: 0.059s, Pack+Encode: 2.601s, Decode+Unpack: 2.490s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1578.6599 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04147183-art_9.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n04147183-art_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04192698-videogame_16.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04192698-videogame_16.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.057s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 194,768B, BPFP=0.3697 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 309,328B, BPFP=0.5871 +⌛️ [2/4] FRONTEND: Frontend time: 2.611s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.497s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09706018 12.79561847 + layer.39.0 404.66927843 3066.96938776 + ------------------------------------------------------------------------------------- + TOTAL 202.38316930 1539.88250311 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 504096 +BPFP 0.4784 bits/point +EBPFP 0.4784 equivalent bits/point +MSE 1539.882503 +---------------------- -------------------------------------------------------- +Time: 5.165s Load: 0.057s, Pack+Encode: 2.611s, Decode+Unpack: 2.497s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1539.8825 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04192698-videogame_16.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n04192698-videogame_16.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04254680-deviantart_17.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04254680-deviantart_17.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.089s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 172,980B, BPFP=0.3283 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 313,616B, BPFP=0.5953 +⌛️ [2/4] FRONTEND: Frontend time: 2.646s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.505s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10685510 33.02598852 + layer.39.0 151.81593173 1959.29045190 + ------------------------------------------------------------------------------------- + TOTAL 75.96139341 996.15822021 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 486596 +BPFP 0.4618 bits/point +EBPFP 0.4618 equivalent bits/point +MSE 996.158220 +---------------------- -------------------------------------------------------- +Time: 5.239s Load: 0.089s, Pack+Encode: 2.646s, Decode+Unpack: 2.505s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 996.1582 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04254680-deviantart_17.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n04254680-deviantart_17.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04266014-painting_13.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04266014-painting_13.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.055s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 178,044B, BPFP=0.3379 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 277,560B, BPFP=0.5268 +⌛️ [2/4] FRONTEND: Frontend time: 2.598s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.496s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09568562 8.69162927 + layer.39.0 29.62437363 3263.21137026 + ------------------------------------------------------------------------------------- + TOTAL 14.86002963 1635.95149977 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 455604 +BPFP 0.4324 bits/point +EBPFP 0.4324 equivalent bits/point +MSE 1635.951500 +---------------------- -------------------------------------------------------- +Time: 5.150s Load: 0.055s, Pack+Encode: 2.598s, Decode+Unpack: 2.496s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1635.9515 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04266014-painting_13.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n04266014-painting_13.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04310018-art_3.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04310018-art_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 220,156B, BPFP=0.4179 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 306,104B, BPFP=0.5810 +⌛️ [2/4] FRONTEND: Frontend time: 2.609s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.486s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13375617 61.67788812 + layer.39.0 75.24515610 3921.25898931 + ------------------------------------------------------------------------------------- + TOTAL 37.68945614 1991.46843871 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 526260 +BPFP 0.4994 bits/point +EBPFP 0.4994 equivalent bits/point +MSE 1991.468439 +---------------------- -------------------------------------------------------- +Time: 5.147s Load: 0.052s, Pack+Encode: 2.609s, Decode+Unpack: 2.486s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1991.4684 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04310018-art_3.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n04310018-art_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04347754-sculpture_0.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04347754-sculpture_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 253,888B, BPFP=0.4819 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 278,460B, BPFP=0.5285 +⌛️ [2/4] FRONTEND: Frontend time: 2.600s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.498s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14257451 43.72379510 + layer.39.0 394.23636419 2558.98299320 + ------------------------------------------------------------------------------------- + TOTAL 197.18946935 1301.35339415 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 532348 +BPFP 0.5052 bits/point +EBPFP 0.5052 equivalent bits/point +MSE 1301.353394 +---------------------- -------------------------------------------------------- +Time: 5.159s Load: 0.060s, Pack+Encode: 2.600s, Decode+Unpack: 2.498s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1301.3534 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04347754-sculpture_0.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n04347754-sculpture_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04409515-deviantart_1.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04409515-deviantart_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.086s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 153,504B, BPFP=0.2914 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 288,200B, BPFP=0.5470 +⌛️ [2/4] FRONTEND: Frontend time: 2.613s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.498s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09627266 8.73936411 + layer.39.0 9.33068077 1802.00607386 + ------------------------------------------------------------------------------------- + TOTAL 4.71347671 905.37271898 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 441704 +BPFP 0.4192 bits/point +EBPFP 0.4192 equivalent bits/point +MSE 905.372719 +---------------------- -------------------------------------------------------- +Time: 5.198s Load: 0.086s, Pack+Encode: 2.613s, Decode+Unpack: 2.498s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 905.3727 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04409515-deviantart_1.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n04409515-deviantart_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04487394-deviantart_8.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04487394-deviantart_8.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 183,096B, BPFP=0.3475 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 305,716B, BPFP=0.5803 +⌛️ [2/4] FRONTEND: Frontend time: 2.601s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.507s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09911632 27.14383845 + layer.39.0 99.63155977 2672.10592809 + ------------------------------------------------------------------------------------- + TOTAL 49.86533804 1349.62488327 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 488812 +BPFP 0.4639 bits/point +EBPFP 0.4639 equivalent bits/point +MSE 1349.624883 +---------------------- -------------------------------------------------------- +Time: 5.159s Load: 0.050s, Pack+Encode: 2.601s, Decode+Unpack: 2.507s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1349.6249 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04487394-deviantart_8.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n04487394-deviantart_8.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04522168-painting_32.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04522168-painting_32.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.089s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 162,420B, BPFP=0.3083 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 255,668B, BPFP=0.4853 +⌛️ [2/4] FRONTEND: Frontend time: 2.678s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.495s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11740584 45.61055181 + layer.39.0 10.95138066 1530.46805151 + ------------------------------------------------------------------------------------- + TOTAL 5.53439325 788.03930166 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 418088 +BPFP 0.3968 bits/point +EBPFP 0.3968 equivalent bits/point +MSE 788.039302 +---------------------- -------------------------------------------------------- +Time: 5.263s Load: 0.089s, Pack+Encode: 2.678s, Decode+Unpack: 2.495s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 788.0393 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04522168-painting_32.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n04522168-painting_32.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04591713-painting_14.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04591713-painting_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.055s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 231,300B, BPFP=0.4390 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 338,384B, BPFP=0.6423 +⌛️ [2/4] FRONTEND: Frontend time: 2.624s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.530s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11212821 14.50253014 + layer.39.0 165.22564383 2295.56268222 + ------------------------------------------------------------------------------------- + TOTAL 82.66888602 1155.03260618 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 569684 +BPFP 0.5407 bits/point +EBPFP 0.5407 equivalent bits/point +MSE 1155.032606 +---------------------- -------------------------------------------------------- +Time: 5.210s Load: 0.055s, Pack+Encode: 2.624s, Decode+Unpack: 2.530s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1155.0326 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04591713-painting_14.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n04591713-painting_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07693725-sketch_15.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07693725-sketch_15.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 209,308B, BPFP=0.3973 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 304,236B, BPFP=0.5775 +⌛️ [2/4] FRONTEND: Frontend time: 2.614s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.496s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10569874 47.42135113 + layer.39.0 214.96065658 2357.61564626 + ------------------------------------------------------------------------------------- + TOTAL 107.53317766 1202.51849869 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 513544 +BPFP 0.4874 bits/point +EBPFP 0.4874 equivalent bits/point +MSE 1202.518499 +---------------------- -------------------------------------------------------- +Time: 5.189s Load: 0.080s, Pack+Encode: 2.614s, Decode+Unpack: 2.496s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1202.5185 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07693725-sketch_15.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n07693725-sketch_15.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07695742-videogame_1.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07695742-videogame_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 244,852B, BPFP=0.4647 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 314,648B, BPFP=0.5972 +⌛️ [2/4] FRONTEND: Frontend time: 2.609s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.512s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12460778 63.37324997 + layer.39.0 438.29433916 3491.95238095 + ------------------------------------------------------------------------------------- + TOTAL 219.20947347 1777.66281546 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 559500 +BPFP 0.5310 bits/point +EBPFP 0.5310 equivalent bits/point +MSE 1777.662815 +---------------------- -------------------------------------------------------- +Time: 5.191s Load: 0.070s, Pack+Encode: 2.609s, Decode+Unpack: 2.512s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1777.6628 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07695742-videogame_1.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n07695742-videogame_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697313-deviantart_7.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697313-deviantart_7.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.090s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 162,308B, BPFP=0.3081 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 289,488B, BPFP=0.5495 +⌛️ [2/4] FRONTEND: Frontend time: 2.708s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.492s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09520741 12.46160278 + layer.39.0 14.69109212 3925.82240039 + ------------------------------------------------------------------------------------- + TOTAL 7.39314977 1969.14200158 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 451796 +BPFP 0.4288 bits/point +EBPFP 0.4288 equivalent bits/point +MSE 1969.142002 +---------------------- -------------------------------------------------------- +Time: 5.289s Load: 0.090s, Pack+Encode: 2.708s, Decode+Unpack: 2.492s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1969.1420 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697313-deviantart_7.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n07697313-deviantart_7.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697537-deviantart_16.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697537-deviantart_16.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.081s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 185,588B, BPFP=0.3523 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 320,348B, BPFP=0.6080 +⌛️ [2/4] FRONTEND: Frontend time: 2.637s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.512s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09755328 0.89876557 + layer.39.0 90.32537658 3558.56025267 + ------------------------------------------------------------------------------------- + TOTAL 45.21146493 1779.72950912 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 505936 +BPFP 0.4802 bits/point +EBPFP 0.4802 equivalent bits/point +MSE 1779.729509 +---------------------- -------------------------------------------------------- +Time: 5.230s Load: 0.081s, Pack+Encode: 2.637s, Decode+Unpack: 2.512s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1779.7295 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697537-deviantart_16.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n07697537-deviantart_16.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714571-deviantart_0.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714571-deviantart_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 171,248B, BPFP=0.3250 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 357,788B, BPFP=0.6791 +⌛️ [2/4] FRONTEND: Frontend time: 2.618s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.518s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09528512 0.84835320 + layer.39.0 45.81401467 5200.08940719 + ------------------------------------------------------------------------------------- + TOTAL 22.95464989 2600.46888020 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 529036 +BPFP 0.5021 bits/point +EBPFP 0.5021 equivalent bits/point +MSE 2600.468880 +---------------------- -------------------------------------------------------- +Time: 5.186s Load: 0.050s, Pack+Encode: 2.618s, Decode+Unpack: 2.518s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2600.4689 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714571-deviantart_0.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n07714571-deviantart_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714990-toy_14.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714990-toy_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 171,428B, BPFP=0.3254 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 362,212B, BPFP=0.6875 +⌛️ [2/4] FRONTEND: Frontend time: 2.641s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.499s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09793257 12.28788000 + layer.39.0 322.50334062 4522.47230321 + ------------------------------------------------------------------------------------- + TOTAL 161.30063660 2267.38009160 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 533640 +BPFP 0.5064 bits/point +EBPFP 0.5064 equivalent bits/point +MSE 2267.380092 +---------------------- -------------------------------------------------------- +Time: 5.220s Load: 0.080s, Pack+Encode: 2.641s, Decode+Unpack: 2.499s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2267.3801 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714990-toy_14.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n07714990-toy_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07718472-cartoon_1.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07718472-cartoon_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 164,916B, BPFP=0.3130 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 262,456B, BPFP=0.4982 +⌛️ [2/4] FRONTEND: Frontend time: 2.607s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.510s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11235230 25.03573516 + layer.39.0 14.49942963 3491.14795918 + ------------------------------------------------------------------------------------- + TOTAL 7.30589096 1758.09184717 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 427372 +BPFP 0.4056 bits/point +EBPFP 0.4056 equivalent bits/point +MSE 1758.091847 +---------------------- -------------------------------------------------------- +Time: 5.168s Load: 0.052s, Pack+Encode: 2.607s, Decode+Unpack: 2.510s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1758.0918 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07718472-cartoon_1.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n07718472-cartoon_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07742313-deviantart_8.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07742313-deviantart_8.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.079s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 134,140B, BPFP=0.2546 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 270,904B, BPFP=0.5142 +⌛️ [2/4] FRONTEND: Frontend time: 2.614s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.501s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09669835 12.78345082 + layer.39.0 8.77690150 2486.32798834 + ------------------------------------------------------------------------------------- + TOTAL 4.43679992 1249.55571958 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 405044 +BPFP 0.3844 bits/point +EBPFP 0.3844 equivalent bits/point +MSE 1249.555720 +---------------------- -------------------------------------------------------- +Time: 5.194s Load: 0.079s, Pack+Encode: 2.614s, Decode+Unpack: 2.501s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1249.5557 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07742313-deviantart_8.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n07742313-deviantart_8.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07749582-sticker_0.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07749582-sticker_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 188,924B, BPFP=0.3586 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 362,092B, BPFP=0.6873 +⌛️ [2/4] FRONTEND: Frontend time: 2.618s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.493s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09550123 12.85808336 + layer.39.0 34.64631545 3727.82312925 + ------------------------------------------------------------------------------------- + TOTAL 17.37090834 1870.34060630 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 551016 +BPFP 0.5229 bits/point +EBPFP 0.5229 equivalent bits/point +MSE 1870.340606 +---------------------- -------------------------------------------------------- +Time: 5.161s Load: 0.050s, Pack+Encode: 2.618s, Decode+Unpack: 2.493s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1870.3406 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07749582-sticker_0.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n07749582-sticker_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07753275-painting_2.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07753275-painting_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.089s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 274,872B, BPFP=0.5217 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 335,856B, BPFP=0.6375 +⌛️ [2/4] FRONTEND: Frontend time: 2.677s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.509s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10429548 104.17054634 + layer.39.0 540.43106171 7400.42468416 + ------------------------------------------------------------------------------------- + TOTAL 270.26767859 3752.29761525 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 610728 +BPFP 0.5796 bits/point +EBPFP 0.5796 equivalent bits/point +MSE 3752.297615 +---------------------- -------------------------------------------------------- +Time: 5.275s Load: 0.089s, Pack+Encode: 2.677s, Decode+Unpack: 2.509s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3752.2976 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07753275-painting_2.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n07753275-painting_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07768694-painting_12.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07768694-painting_12.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 214,700B, BPFP=0.4075 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 339,384B, BPFP=0.6442 +⌛️ [2/4] FRONTEND: Frontend time: 2.637s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.485s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09821300 12.59729277 + layer.39.0 635.68343052 4737.70602527 + ------------------------------------------------------------------------------------- + TOTAL 317.89082176 2375.15165902 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 554084 +BPFP 0.5258 bits/point +EBPFP 0.5258 equivalent bits/point +MSE 2375.151659 +---------------------- -------------------------------------------------------- +Time: 5.172s Load: 0.050s, Pack+Encode: 2.637s, Decode+Unpack: 2.485s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2375.1517 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07768694-painting_12.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n07768694-painting_12.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07920052-painting_1.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07920052-painting_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 179,496B, BPFP=0.3407 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 335,940B, BPFP=0.6376 +⌛️ [2/4] FRONTEND: Frontend time: 2.608s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.505s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09582097 12.84260356 + layer.39.0 9.59182155 3050.41302235 + ------------------------------------------------------------------------------------- + TOTAL 4.84382126 1531.62781296 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 515436 +BPFP 0.4892 bits/point +EBPFP 0.4892 equivalent bits/point +MSE 1531.627813 +---------------------- -------------------------------------------------------- +Time: 5.163s Load: 0.050s, Pack+Encode: 2.608s, Decode+Unpack: 2.505s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1531.6278 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07920052-painting_1.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n07920052-painting_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09472597-painting_15.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09472597-painting_15.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 134,984B, BPFP=0.2562 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 262,584B, BPFP=0.4984 +⌛️ [2/4] FRONTEND: Frontend time: 2.615s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.512s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09164813 12.78861930 + layer.39.0 9.11265014 1639.60434888 + ------------------------------------------------------------------------------------- + TOTAL 4.60214913 826.19648409 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 397568 +BPFP 0.3773 bits/point +EBPFP 0.3773 equivalent bits/point +MSE 826.196484 +---------------------- -------------------------------------------------------- +Time: 5.179s Load: 0.052s, Pack+Encode: 2.615s, Decode+Unpack: 2.512s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 826.1965 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09472597-painting_15.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n09472597-painting_15.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09835506-videogame_3.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09835506-videogame_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 170,640B, BPFP=0.3239 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 327,832B, BPFP=0.6223 +⌛️ [2/4] FRONTEND: Frontend time: 2.605s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.503s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09585661 13.22285460 + layer.39.0 12.34450164 3147.96744412 + ------------------------------------------------------------------------------------- + TOTAL 6.22017912 1580.59514936 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 498472 +BPFP 0.4731 bits/point +EBPFP 0.4731 equivalent bits/point +MSE 1580.595149 +---------------------- -------------------------------------------------------- +Time: 5.168s Load: 0.060s, Pack+Encode: 2.605s, Decode+Unpack: 2.503s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1580.5951 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09835506-videogame_3.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n09835506-videogame_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n12267677-misc_105.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n12267677-misc_105.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 158,272B, BPFP=0.3004 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 312,256B, BPFP=0.5927 +⌛️ [2/4] FRONTEND: Frontend time: 2.601s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.503s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10166193 12.70596358 + layer.39.0 219.41089650 2281.17006803 + ------------------------------------------------------------------------------------- + TOTAL 109.75627921 1146.93801580 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 470528 +BPFP 0.4466 bits/point +EBPFP 0.4466 equivalent bits/point +MSE 1146.938016 +---------------------- -------------------------------------------------------- +Time: 5.156s Load: 0.052s, Pack+Encode: 2.601s, Decode+Unpack: 2.503s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1146.9380 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n12267677-misc_105.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kr/n12267677-misc_105.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 0.4781 bits/point +Avg EBPFP 0.4781 equivalent bits/point +Avg MSE 1736.772943 +Avg Time 5.196s +------------------------ ---------------------------- diff --git a/lambda0.007/elic-featurecoding-8bit-individual/cls_in1kval/dtufc_elic-featurecoding_dinov3-total_individual.log b/lambda0.007/elic-featurecoding-8bit-individual/cls_in1kval/dtufc_elic-featurecoding_dinov3-total_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..b800a0301034923392f7c2c82b01224fb6351f82 --- /dev/null +++ b/lambda0.007/elic-featurecoding-8bit-individual/cls_in1kval/dtufc_elic-featurecoding_dinov3-total_individual.log @@ -0,0 +1,9344 @@ +Experiment: dtufc_elic-featurecoding_dinov3-total_individual +Log file: output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/dtufc_elic-featurecoding_dinov3-total_individual.log +DTUFCCodecConfig: + arch: elic-featurecoding + handler: dinov3-total + checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.007_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.007_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 333 +Loaded elic-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.9' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.19' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.29' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.39' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json +Loaded per-key mappings: model=dinov3-total + Keys: ['layer.9', 'layer.19', 'layer.29', 'layer.39'] +---------------- -------------------------------------------------------------------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +Checkpoint codec_weights/elic_hybrid/elic2022-official_lambda0.007_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-DINOv3/imagenet1k-val +Output output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval +---------------- -------------------------------------------------------------------------------------------------------------------- +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02825657-ILSVRC2012_val_00001103.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02825657-ILSVRC2012_val_00001103.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 162,520B, BPFP=0.3085 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 271,268B, BPFP=0.5149 +⌛️ [2/4] FRONTEND: Frontend time: 3.064s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.561s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10264289 24.45864841 + layer.39.0 9.47367932 1498.38070943 + ------------------------------------------------------------------------------------- + TOTAL 4.78816110 761.41967892 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 433788 +BPFP 0.4117 bits/point +EBPFP 0.4117 equivalent bits/point +MSE 761.419679 +---------------------- -------------------------------------------------------- +Time: 5.696s Load: 0.070s, Pack+Encode: 3.064s, Decode+Unpack: 2.561s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 761.4197 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02825657-ILSVRC2012_val_00001103.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n02825657-ILSVRC2012_val_00001103.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02834397-ILSVRC2012_val_00001252.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02834397-ILSVRC2012_val_00001252.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 251,704B, BPFP=0.4778 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 366,540B, BPFP=0.6957 +⌛️ [2/4] FRONTEND: Frontend time: 2.618s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.502s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14789204 163.59955053 + layer.39.0 415.43227648 4340.57045675 + ------------------------------------------------------------------------------------- + TOTAL 207.79008426 2252.08500364 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 618244 +BPFP 0.5867 bits/point +EBPFP 0.5867 equivalent bits/point +MSE 2252.085004 +---------------------- -------------------------------------------------------- +Time: 5.188s Load: 0.069s, Pack+Encode: 2.618s, Decode+Unpack: 2.502s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2252.0850 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02834397-ILSVRC2012_val_00001252.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n02834397-ILSVRC2012_val_00001252.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02840245-ILSVRC2012_val_00003446.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02840245-ILSVRC2012_val_00003446.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 149,872B, BPFP=0.2845 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 301,544B, BPFP=0.5724 +⌛️ [2/4] FRONTEND: Frontend time: 2.600s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.502s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10761288 12.39229911 + layer.39.0 28.71820525 2308.32750243 + ------------------------------------------------------------------------------------- + TOTAL 14.41290906 1160.35990077 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 451416 +BPFP 0.4284 bits/point +EBPFP 0.4284 equivalent bits/point +MSE 1160.359901 +---------------------- -------------------------------------------------------- +Time: 5.153s Load: 0.051s, Pack+Encode: 2.600s, Decode+Unpack: 2.502s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1160.3599 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02840245-ILSVRC2012_val_00003446.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n02840245-ILSVRC2012_val_00003446.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02843684-ILSVRC2012_val_00000514.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02843684-ILSVRC2012_val_00000514.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 180,844B, BPFP=0.3433 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 300,420B, BPFP=0.5702 +⌛️ [2/4] FRONTEND: Frontend time: 2.618s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.488s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11482661 12.91051689 + layer.39.0 84.54469600 3142.59791059 + ------------------------------------------------------------------------------------- + TOTAL 42.32976130 1577.75421374 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 481264 +BPFP 0.4567 bits/point +EBPFP 0.4567 equivalent bits/point +MSE 1577.754214 +---------------------- -------------------------------------------------------- +Time: 5.157s Load: 0.051s, Pack+Encode: 2.618s, Decode+Unpack: 2.488s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1577.7542 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02843684-ILSVRC2012_val_00000514.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n02843684-ILSVRC2012_val_00000514.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02859443-ILSVRC2012_val_00000193.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02859443-ILSVRC2012_val_00000193.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 174,924B, BPFP=0.3320 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 228,924B, BPFP=0.4345 +⌛️ [2/4] FRONTEND: Frontend time: 2.604s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.507s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11417333 0.95015800 + layer.39.0 9.67809406 2162.90208941 + ------------------------------------------------------------------------------------- + TOTAL 4.89613370 1081.92612371 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 403848 +BPFP 0.3833 bits/point +EBPFP 0.3833 equivalent bits/point +MSE 1081.926124 +---------------------- -------------------------------------------------------- +Time: 5.163s Load: 0.051s, Pack+Encode: 2.604s, Decode+Unpack: 2.507s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1081.9261 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02859443-ILSVRC2012_val_00000193.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n02859443-ILSVRC2012_val_00000193.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02860847-ILSVRC2012_val_00000601.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02860847-ILSVRC2012_val_00000601.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 213,556B, BPFP=0.4053 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 297,360B, BPFP=0.5644 +⌛️ [2/4] FRONTEND: Frontend time: 2.602s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.495s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12653054 42.10551810 + layer.39.0 266.35249636 4746.50728863 + ------------------------------------------------------------------------------------- + TOTAL 133.23951345 2394.30640336 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 510916 +BPFP 0.4849 bits/point +EBPFP 0.4849 equivalent bits/point +MSE 2394.306403 +---------------------- -------------------------------------------------------- +Time: 5.166s Load: 0.069s, Pack+Encode: 2.602s, Decode+Unpack: 2.495s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2394.3064 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02860847-ILSVRC2012_val_00000601.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n02860847-ILSVRC2012_val_00000601.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02865351-ILSVRC2012_val_00000763.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02865351-ILSVRC2012_val_00000763.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 154,976B, BPFP=0.2942 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 314,316B, BPFP=0.5966 +⌛️ [2/4] FRONTEND: Frontend time: 2.602s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.502s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09467571 12.35631814 + layer.39.0 15.47581086 2439.21064140 + ------------------------------------------------------------------------------------- + TOTAL 7.78524328 1225.78347977 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 469292 +BPFP 0.4454 bits/point +EBPFP 0.4454 equivalent bits/point +MSE 1225.783480 +---------------------- -------------------------------------------------------- +Time: 5.154s Load: 0.051s, Pack+Encode: 2.602s, Decode+Unpack: 2.502s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1225.7835 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02865351-ILSVRC2012_val_00000763.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n02865351-ILSVRC2012_val_00000763.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02869837-ILSVRC2012_val_00000906.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02869837-ILSVRC2012_val_00000906.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 198,480B, BPFP=0.3767 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 395,304B, BPFP=0.7503 +⌛️ [2/4] FRONTEND: Frontend time: 2.623s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.492s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09659988 0.91781506 + layer.39.0 16.39405483 5072.96307094 + ------------------------------------------------------------------------------------- + TOTAL 8.24532736 2536.94044300 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 593784 +BPFP 0.5635 bits/point +EBPFP 0.5635 equivalent bits/point +MSE 2536.940443 +---------------------- -------------------------------------------------------- +Time: 5.187s Load: 0.071s, Pack+Encode: 2.623s, Decode+Unpack: 2.492s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2536.9404 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02869837-ILSVRC2012_val_00000906.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n02869837-ILSVRC2012_val_00000906.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02870880-ILSVRC2012_val_00003274.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02870880-ILSVRC2012_val_00003274.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.056s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 230,644B, BPFP=0.4378 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 325,716B, BPFP=0.6182 +⌛️ [2/4] FRONTEND: Frontend time: 2.615s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.516s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10254154 14.22604622 + layer.39.0 9.36513093 3753.17930029 + ------------------------------------------------------------------------------------- + TOTAL 4.73383623 1883.70267326 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 556360 +BPFP 0.5280 bits/point +EBPFP 0.5280 equivalent bits/point +MSE 1883.702673 +---------------------- -------------------------------------------------------- +Time: 5.187s Load: 0.056s, Pack+Encode: 2.615s, Decode+Unpack: 2.516s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1883.7027 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02870880-ILSVRC2012_val_00003274.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n02870880-ILSVRC2012_val_00003274.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02871525-ILSVRC2012_val_00000879.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02871525-ILSVRC2012_val_00000879.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 263,244B, BPFP=0.4997 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 335,860B, BPFP=0.6375 +⌛️ [2/4] FRONTEND: Frontend time: 2.613s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.499s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.17072899 101.33914905 + layer.39.0 20.29403547 4737.85276968 + ------------------------------------------------------------------------------------- + TOTAL 10.23238223 2419.59595937 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 599104 +BPFP 0.5686 bits/point +EBPFP 0.5686 equivalent bits/point +MSE 2419.595959 +---------------------- -------------------------------------------------------- +Time: 5.162s Load: 0.050s, Pack+Encode: 2.613s, Decode+Unpack: 2.499s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2419.5960 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02871525-ILSVRC2012_val_00000879.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n02871525-ILSVRC2012_val_00000879.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02877765-ILSVRC2012_val_00000634.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02877765-ILSVRC2012_val_00000634.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.049s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 173,876B, BPFP=0.3300 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 334,164B, BPFP=0.6343 +⌛️ [2/4] FRONTEND: Frontend time: 2.612s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.494s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10908128 12.62224968 + layer.39.0 364.97770894 5839.17930029 + ------------------------------------------------------------------------------------- + TOTAL 182.54339511 2925.90077499 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 508040 +BPFP 0.4822 bits/point +EBPFP 0.4822 equivalent bits/point +MSE 2925.900775 +---------------------- -------------------------------------------------------- +Time: 5.155s Load: 0.049s, Pack+Encode: 2.612s, Decode+Unpack: 2.494s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2925.9008 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02877765-ILSVRC2012_val_00000634.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n02877765-ILSVRC2012_val_00000634.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02879718-ILSVRC2012_val_00001354.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02879718-ILSVRC2012_val_00001354.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 208,960B, BPFP=0.3966 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 349,324B, BPFP=0.6630 +⌛️ [2/4] FRONTEND: Frontend time: 2.597s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.503s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10948122 13.85652693 + layer.39.0 55.92460444 5341.97521866 + ------------------------------------------------------------------------------------- + TOTAL 28.01704283 2677.91587279 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 558284 +BPFP 0.5298 bits/point +EBPFP 0.5298 equivalent bits/point +MSE 2677.915873 +---------------------- -------------------------------------------------------- +Time: 5.159s Load: 0.060s, Pack+Encode: 2.597s, Decode+Unpack: 2.503s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2677.9159 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02879718-ILSVRC2012_val_00001354.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n02879718-ILSVRC2012_val_00001354.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02883205-ILSVRC2012_val_00000126.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02883205-ILSVRC2012_val_00000126.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 121,020B, BPFP=0.2297 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 284,876B, BPFP=0.5407 +⌛️ [2/4] FRONTEND: Frontend time: 2.596s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.493s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.06711708 12.42934869 + layer.39.0 7.82069686 1552.99866375 + ------------------------------------------------------------------------------------- + TOTAL 7.94390697 782.71400622 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 405896 +BPFP 0.3852 bits/point +EBPFP 0.3852 equivalent bits/point +MSE 782.714006 +---------------------- -------------------------------------------------------- +Time: 5.140s Load: 0.051s, Pack+Encode: 2.596s, Decode+Unpack: 2.493s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 782.7140 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02883205-ILSVRC2012_val_00000126.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n02883205-ILSVRC2012_val_00000126.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892201-ILSVRC2012_val_00001145.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892201-ILSVRC2012_val_00001145.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 238,056B, BPFP=0.4518 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 346,908B, BPFP=0.6585 +⌛️ [2/4] FRONTEND: Frontend time: 2.602s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.511s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11297333 86.08567177 + layer.39.0 15.09638643 3030.06559767 + ------------------------------------------------------------------------------------- + TOTAL 7.60467988 1558.07563472 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 584964 +BPFP 0.5552 bits/point +EBPFP 0.5552 equivalent bits/point +MSE 1558.075635 +---------------------- -------------------------------------------------------- +Time: 5.173s Load: 0.060s, Pack+Encode: 2.602s, Decode+Unpack: 2.511s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1558.0756 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892201-ILSVRC2012_val_00001145.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n02892201-ILSVRC2012_val_00001145.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892767-ILSVRC2012_val_00000808.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892767-ILSVRC2012_val_00000808.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 164,196B, BPFP=0.3117 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 393,280B, BPFP=0.7465 +⌛️ [2/4] FRONTEND: Frontend time: 2.596s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.508s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09598007 0.86819651 + layer.39.0 31.15013059 4894.69290573 + ------------------------------------------------------------------------------------- + TOTAL 15.62305533 2447.78055112 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 557476 +BPFP 0.5291 bits/point +EBPFP 0.5291 equivalent bits/point +MSE 2447.780551 +---------------------- -------------------------------------------------------- +Time: 5.165s Load: 0.060s, Pack+Encode: 2.596s, Decode+Unpack: 2.508s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2447.7806 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892767-ILSVRC2012_val_00000808.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n02892767-ILSVRC2012_val_00000808.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02895154-ILSVRC2012_val_00000080.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02895154-ILSVRC2012_val_00000080.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.089s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 176,500B, BPFP=0.3350 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 375,652B, BPFP=0.7130 +⌛️ [2/4] FRONTEND: Frontend time: 2.623s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.500s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09530723 12.54552262 + layer.39.0 971.40427600 5544.41205053 + ------------------------------------------------------------------------------------- + TOTAL 485.74979162 2778.47878658 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 552152 +BPFP 0.5240 bits/point +EBPFP 0.5240 equivalent bits/point +MSE 2778.478787 +---------------------- -------------------------------------------------------- +Time: 5.213s Load: 0.089s, Pack+Encode: 2.623s, Decode+Unpack: 2.500s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2778.4788 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02895154-ILSVRC2012_val_00000080.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n02895154-ILSVRC2012_val_00000080.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02906734-ILSVRC2012_val_00002937.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02906734-ILSVRC2012_val_00002937.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.053s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 186,140B, BPFP=0.3533 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 282,000B, BPFP=0.5353 +⌛️ [2/4] FRONTEND: Frontend time: 2.604s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.494s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09767962 12.88849725 + layer.39.0 32.09536716 1673.14261419 + ------------------------------------------------------------------------------------- + TOTAL 16.09652339 843.01555572 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 468140 +BPFP 0.4443 bits/point +EBPFP 0.4443 equivalent bits/point +MSE 843.015556 +---------------------- -------------------------------------------------------- +Time: 5.151s Load: 0.053s, Pack+Encode: 2.604s, Decode+Unpack: 2.494s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 843.0156 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02906734-ILSVRC2012_val_00002937.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n02906734-ILSVRC2012_val_00002937.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02910353-ILSVRC2012_val_00000558.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02910353-ILSVRC2012_val_00000558.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 182,204B, BPFP=0.3458 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 315,372B, BPFP=0.5986 +⌛️ [2/4] FRONTEND: Frontend time: 2.616s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.510s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11017090 37.30450528 + layer.39.0 483.40066205 5422.88532556 + ------------------------------------------------------------------------------------- + TOTAL 241.75541648 2730.09491542 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 497576 +BPFP 0.4722 bits/point +EBPFP 0.4722 equivalent bits/point +MSE 2730.094915 +---------------------- -------------------------------------------------------- +Time: 5.187s Load: 0.061s, Pack+Encode: 2.616s, Decode+Unpack: 2.510s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2730.0949 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02910353-ILSVRC2012_val_00000558.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n02910353-ILSVRC2012_val_00000558.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02916936-ILSVRC2012_val_00000366.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02916936-ILSVRC2012_val_00000366.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 163,188B, BPFP=0.3097 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 328,564B, BPFP=0.6236 +⌛️ [2/4] FRONTEND: Frontend time: 2.608s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.497s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10657579 12.56477580 + layer.39.0 435.18944363 5703.88046647 + ------------------------------------------------------------------------------------- + TOTAL 217.64800971 2858.22262114 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 491752 +BPFP 0.4667 bits/point +EBPFP 0.4667 equivalent bits/point +MSE 2858.222621 +---------------------- -------------------------------------------------------- +Time: 5.165s Load: 0.060s, Pack+Encode: 2.608s, Decode+Unpack: 2.497s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2858.2226 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02916936-ILSVRC2012_val_00000366.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n02916936-ILSVRC2012_val_00000366.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02917067-ILSVRC2012_val_00000562.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02917067-ILSVRC2012_val_00000562.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 228,612B, BPFP=0.4339 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 347,792B, BPFP=0.6601 +⌛️ [2/4] FRONTEND: Frontend time: 2.599s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.512s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10760244 37.81494473 + layer.39.0 37.55795979 6858.51652089 + ------------------------------------------------------------------------------------- + TOTAL 18.83278111 3448.16573281 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 576404 +BPFP 0.5470 bits/point +EBPFP 0.5470 equivalent bits/point +MSE 3448.165733 +---------------------- -------------------------------------------------------- +Time: 5.162s Load: 0.051s, Pack+Encode: 2.599s, Decode+Unpack: 2.512s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3448.1657 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02917067-ILSVRC2012_val_00000562.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n02917067-ILSVRC2012_val_00000562.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02930766-ILSVRC2012_val_00000056.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02930766-ILSVRC2012_val_00000056.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 207,260B, BPFP=0.3934 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 364,604B, BPFP=0.6920 +⌛️ [2/4] FRONTEND: Frontend time: 2.629s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.510s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10591127 44.89892341 + layer.39.0 18.32421875 3807.45578231 + ------------------------------------------------------------------------------------- + TOTAL 9.21506501 1926.17735286 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 571864 +BPFP 0.5427 bits/point +EBPFP 0.5427 equivalent bits/point +MSE 1926.177353 +---------------------- -------------------------------------------------------- +Time: 5.189s Load: 0.050s, Pack+Encode: 2.629s, Decode+Unpack: 2.510s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1926.1774 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02930766-ILSVRC2012_val_00000056.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n02930766-ILSVRC2012_val_00000056.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02939185-ILSVRC2012_val_00000302.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02939185-ILSVRC2012_val_00000302.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 185,296B, BPFP=0.3517 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 311,852B, BPFP=0.5919 +⌛️ [2/4] FRONTEND: Frontend time: 2.600s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.500s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09694758 12.52198452 + layer.39.0 25.52453269 2878.61151603 + ------------------------------------------------------------------------------------- + TOTAL 12.81074014 1445.56675028 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 497148 +BPFP 0.4718 bits/point +EBPFP 0.4718 equivalent bits/point +MSE 1445.566750 +---------------------- -------------------------------------------------------- +Time: 5.152s Load: 0.052s, Pack+Encode: 2.600s, Decode+Unpack: 2.500s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1445.5668 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02939185-ILSVRC2012_val_00000302.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n02939185-ILSVRC2012_val_00000302.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02950826-ILSVRC2012_val_00000392.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02950826-ILSVRC2012_val_00000392.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 199,352B, BPFP=0.3784 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 318,732B, BPFP=0.6050 +⌛️ [2/4] FRONTEND: Frontend time: 2.603s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.517s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10873010 107.70441569 + layer.39.0 707.96944849 6092.63216715 + ------------------------------------------------------------------------------------- + TOTAL 354.03908930 3100.16829142 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 518084 +BPFP 0.4917 bits/point +EBPFP 0.4917 equivalent bits/point +MSE 3100.168291 +---------------------- -------------------------------------------------------- +Time: 5.171s Load: 0.052s, Pack+Encode: 2.603s, Decode+Unpack: 2.517s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3100.1683 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02950826-ILSVRC2012_val_00000392.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n02950826-ILSVRC2012_val_00000392.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951358-ILSVRC2012_val_00000347.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951358-ILSVRC2012_val_00000347.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 187,408B, BPFP=0.3557 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 291,784B, BPFP=0.5538 +⌛️ [2/4] FRONTEND: Frontend time: 2.612s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.501s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12200860 109.70834852 + layer.39.0 237.66299198 4968.02526725 + ------------------------------------------------------------------------------------- + TOTAL 118.89250029 2538.86680788 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 479192 +BPFP 0.4548 bits/point +EBPFP 0.4548 equivalent bits/point +MSE 2538.866808 +---------------------- -------------------------------------------------------- +Time: 5.163s Load: 0.050s, Pack+Encode: 2.612s, Decode+Unpack: 2.501s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2538.8668 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951358-ILSVRC2012_val_00000347.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n02951358-ILSVRC2012_val_00000347.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951585-ILSVRC2012_val_00000101.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951585-ILSVRC2012_val_00000101.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.053s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 138,912B, BPFP=0.2637 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 314,096B, BPFP=0.5962 +⌛️ [2/4] FRONTEND: Frontend time: 2.609s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.493s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.07385432 12.77427930 + layer.39.0 181.90962099 3831.59037901 + ------------------------------------------------------------------------------------- + TOTAL 94.99173765 1922.18232915 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 453008 +BPFP 0.4299 bits/point +EBPFP 0.4299 equivalent bits/point +MSE 1922.182329 +---------------------- -------------------------------------------------------- +Time: 5.154s Load: 0.053s, Pack+Encode: 2.609s, Decode+Unpack: 2.493s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1922.1823 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951585-ILSVRC2012_val_00000101.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n02951585-ILSVRC2012_val_00000101.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02963159-ILSVRC2012_val_00000061.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02963159-ILSVRC2012_val_00000061.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 189,720B, BPFP=0.3601 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 312,828B, BPFP=0.5938 +⌛️ [2/4] FRONTEND: Frontend time: 2.629s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.497s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11232698 13.28136483 + layer.39.0 24.77479842 2098.90719145 + ------------------------------------------------------------------------------------- + TOTAL 12.44356270 1056.09427814 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 502548 +BPFP 0.4769 bits/point +EBPFP 0.4769 equivalent bits/point +MSE 1056.094278 +---------------------- -------------------------------------------------------- +Time: 5.176s Load: 0.050s, Pack+Encode: 2.629s, Decode+Unpack: 2.497s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1056.0943 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02963159-ILSVRC2012_val_00000061.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n02963159-ILSVRC2012_val_00000061.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02965783-ILSVRC2012_val_00000213.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02965783-ILSVRC2012_val_00000213.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.090s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 157,152B, BPFP=0.2983 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 347,364B, BPFP=0.6593 +⌛️ [2/4] FRONTEND: Frontend time: 2.601s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.503s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09516161 12.18630326 + layer.39.0 223.32294704 6186.98104956 + ------------------------------------------------------------------------------------- + TOTAL 111.70905432 3099.58367641 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 504516 +BPFP 0.4788 bits/point +EBPFP 0.4788 equivalent bits/point +MSE 3099.583676 +---------------------- -------------------------------------------------------- +Time: 5.193s Load: 0.090s, Pack+Encode: 2.601s, Decode+Unpack: 2.503s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3099.5837 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02965783-ILSVRC2012_val_00000213.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n02965783-ILSVRC2012_val_00000213.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966193-ILSVRC2012_val_00000074.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966193-ILSVRC2012_val_00000074.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 249,792B, BPFP=0.4741 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 423,060B, BPFP=0.8030 +⌛️ [2/4] FRONTEND: Frontend time: 2.618s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.504s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12190965 75.72925777 + layer.39.0 378.75431244 10212.10981535 + ------------------------------------------------------------------------------------- + TOTAL 189.43811104 5143.91953656 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 672852 +BPFP 0.6386 bits/point +EBPFP 0.6386 equivalent bits/point +MSE 5143.919537 +---------------------- -------------------------------------------------------- +Time: 5.194s Load: 0.072s, Pack+Encode: 2.618s, Decode+Unpack: 2.504s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 5143.9195 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966193-ILSVRC2012_val_00000074.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n02966193-ILSVRC2012_val_00000074.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966687-ILSVRC2012_val_00001041.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966687-ILSVRC2012_val_00001041.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.091s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 195,636B, BPFP=0.3713 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 351,156B, BPFP=0.6665 +⌛️ [2/4] FRONTEND: Frontend time: 2.622s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.508s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12487827 49.96358342 + layer.39.0 254.07423773 4795.30174927 + ------------------------------------------------------------------------------------- + TOTAL 127.09955800 2422.63266635 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 546792 +BPFP 0.5189 bits/point +EBPFP 0.5189 equivalent bits/point +MSE 2422.632666 +---------------------- -------------------------------------------------------- +Time: 5.221s Load: 0.091s, Pack+Encode: 2.622s, Decode+Unpack: 2.508s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2422.6327 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966687-ILSVRC2012_val_00001041.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n02966687-ILSVRC2012_val_00001041.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02971356-ILSVRC2012_val_00000019.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02971356-ILSVRC2012_val_00000019.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.079s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 139,328B, BPFP=0.2645 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 254,640B, BPFP=0.4833 +⌛️ [2/4] FRONTEND: Frontend time: 2.621s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.496s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09754465 8.63949090 + layer.39.0 24.51746044 2054.00388727 + ------------------------------------------------------------------------------------- + TOTAL 12.30750255 1031.32168908 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 393968 +BPFP 0.3739 bits/point +EBPFP 0.3739 equivalent bits/point +MSE 1031.321689 +---------------------- -------------------------------------------------------- +Time: 5.197s Load: 0.079s, Pack+Encode: 2.621s, Decode+Unpack: 2.496s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1031.3217 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02971356-ILSVRC2012_val_00000019.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n02971356-ILSVRC2012_val_00000019.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02978881-ILSVRC2012_val_00000353.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02978881-ILSVRC2012_val_00000353.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 190,964B, BPFP=0.3625 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 353,576B, BPFP=0.6711 +⌛️ [2/4] FRONTEND: Frontend time: 2.621s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.495s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09975241 1.29623155 + layer.39.0 226.62124939 2873.11321672 + ------------------------------------------------------------------------------------- + TOTAL 113.36050090 1437.20472413 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 544540 +BPFP 0.5168 bits/point +EBPFP 0.5168 equivalent bits/point +MSE 1437.204724 +---------------------- -------------------------------------------------------- +Time: 5.166s Load: 0.051s, Pack+Encode: 2.621s, Decode+Unpack: 2.495s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1437.2047 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02978881-ILSVRC2012_val_00000353.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n02978881-ILSVRC2012_val_00000353.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02980441-ILSVRC2012_val_00000122.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02980441-ILSVRC2012_val_00000122.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 161,664B, BPFP=0.3069 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 259,372B, BPFP=0.4923 +⌛️ [2/4] FRONTEND: Frontend time: 2.595s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.497s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10186533 12.63879430 + layer.39.0 8.25151846 1354.29992711 + ------------------------------------------------------------------------------------- + TOTAL 4.17669190 683.46936071 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 421036 +BPFP 0.3996 bits/point +EBPFP 0.3996 equivalent bits/point +MSE 683.469361 +---------------------- -------------------------------------------------------- +Time: 5.152s Load: 0.060s, Pack+Encode: 2.595s, Decode+Unpack: 2.497s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 683.4694 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02980441-ILSVRC2012_val_00000122.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n02980441-ILSVRC2012_val_00000122.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02988304-ILSVRC2012_val_00003491.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02988304-ILSVRC2012_val_00003491.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 178,092B, BPFP=0.3380 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 317,160B, BPFP=0.6020 +⌛️ [2/4] FRONTEND: Frontend time: 2.623s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.493s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10176498 25.11734314 + layer.39.0 516.16180758 6527.70359572 + ------------------------------------------------------------------------------------- + TOTAL 258.13178628 3276.41046943 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 495252 +BPFP 0.4700 bits/point +EBPFP 0.4700 equivalent bits/point +MSE 3276.410469 +---------------------- -------------------------------------------------------- +Time: 5.166s Load: 0.051s, Pack+Encode: 2.623s, Decode+Unpack: 2.493s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3276.4105 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02988304-ILSVRC2012_val_00003491.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n02988304-ILSVRC2012_val_00003491.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992211-ILSVRC2012_val_00000108.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992211-ILSVRC2012_val_00000108.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 200,888B, BPFP=0.3813 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 347,448B, BPFP=0.6595 +⌛️ [2/4] FRONTEND: Frontend time: 2.614s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.499s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10107529 12.66608680 + layer.39.0 89.13089923 7702.62390671 + ------------------------------------------------------------------------------------- + TOTAL 44.61598726 3857.64499675 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 548336 +BPFP 0.5204 bits/point +EBPFP 0.5204 equivalent bits/point +MSE 3857.644997 +---------------------- -------------------------------------------------------- +Time: 5.163s Load: 0.051s, Pack+Encode: 2.614s, Decode+Unpack: 2.499s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3857.6450 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992211-ILSVRC2012_val_00000108.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n02992211-ILSVRC2012_val_00000108.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992529-ILSVRC2012_val_00000089.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992529-ILSVRC2012_val_00000089.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.058s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 190,424B, BPFP=0.3614 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 380,000B, BPFP=0.7213 +⌛️ [2/4] FRONTEND: Frontend time: 2.600s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.507s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11197385 12.86990745 + layer.39.0 964.25631681 7311.40767736 + ------------------------------------------------------------------------------------- + TOTAL 482.18414533 3662.13879240 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 570424 +BPFP 0.5414 bits/point +EBPFP 0.5414 equivalent bits/point +MSE 3662.138792 +---------------------- -------------------------------------------------------- +Time: 5.165s Load: 0.058s, Pack+Encode: 2.600s, Decode+Unpack: 2.507s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3662.1388 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992529-ILSVRC2012_val_00000089.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n02992529-ILSVRC2012_val_00000089.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02999410-ILSVRC2012_val_00000376.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02999410-ILSVRC2012_val_00000376.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 196,512B, BPFP=0.3730 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 309,464B, BPFP=0.5874 +⌛️ [2/4] FRONTEND: Frontend time: 2.633s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.509s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10398186 14.13920049 + layer.39.0 145.78410471 2084.06049563 + ------------------------------------------------------------------------------------- + TOTAL 72.94404329 1049.09984806 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 505976 +BPFP 0.4802 bits/point +EBPFP 0.4802 equivalent bits/point +MSE 1049.099848 +---------------------- -------------------------------------------------------- +Time: 5.192s Load: 0.050s, Pack+Encode: 2.633s, Decode+Unpack: 2.509s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1049.0998 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02999410-ILSVRC2012_val_00000376.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n02999410-ILSVRC2012_val_00000376.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000134-ILSVRC2012_val_00001094.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000134-ILSVRC2012_val_00001094.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 164,600B, BPFP=0.3124 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 393,160B, BPFP=0.7462 +⌛️ [2/4] FRONTEND: Frontend time: 2.598s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.500s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09696872 12.66794882 + layer.39.0 22.81329530 6311.30952381 + ------------------------------------------------------------------------------------- + TOTAL 11.45513201 3161.98873631 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 557760 +BPFP 0.5293 bits/point +EBPFP 0.5293 equivalent bits/point +MSE 3161.988736 +---------------------- -------------------------------------------------------- +Time: 5.149s Load: 0.051s, Pack+Encode: 2.598s, Decode+Unpack: 2.500s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3161.9887 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000134-ILSVRC2012_val_00001094.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03000134-ILSVRC2012_val_00001094.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000247-ILSVRC2012_val_00002280.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000247-ILSVRC2012_val_00002280.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 367,624B, BPFP=0.6978 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 282,356B, BPFP=0.5359 +⌛️ [2/4] FRONTEND: Frontend time: 2.605s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.500s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.29135144 479.67893586 + layer.39.0 428.26293732 4520.17881438 + ------------------------------------------------------------------------------------- + TOTAL 214.27714438 2499.92887512 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 649980 +BPFP 0.6169 bits/point +EBPFP 0.6169 equivalent bits/point +MSE 2499.928875 +---------------------- -------------------------------------------------------- +Time: 5.156s Load: 0.052s, Pack+Encode: 2.605s, Decode+Unpack: 2.500s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2499.9289 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000247-ILSVRC2012_val_00002280.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03000247-ILSVRC2012_val_00002280.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000684-ILSVRC2012_val_00000537.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000684-ILSVRC2012_val_00000537.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 250,908B, BPFP=0.4762 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 367,288B, BPFP=0.6971 +⌛️ [2/4] FRONTEND: Frontend time: 2.605s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.517s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13150742 114.94348275 + layer.39.0 55.24585459 4382.18707483 + ------------------------------------------------------------------------------------- + TOTAL 27.68868101 2248.56527879 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 618196 +BPFP 0.5867 bits/point +EBPFP 0.5867 equivalent bits/point +MSE 2248.565279 +---------------------- -------------------------------------------------------- +Time: 5.172s Load: 0.050s, Pack+Encode: 2.605s, Decode+Unpack: 2.517s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2248.5653 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000684-ILSVRC2012_val_00000537.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03000684-ILSVRC2012_val_00000537.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03014705-ILSVRC2012_val_00001168.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03014705-ILSVRC2012_val_00001168.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.056s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 142,544B, BPFP=0.2706 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 324,952B, BPFP=0.6168 +⌛️ [2/4] FRONTEND: Frontend time: 2.600s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.513s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09787338 12.95610594 + layer.39.0 322.89622813 3357.42711370 + ------------------------------------------------------------------------------------- + TOTAL 161.49705076 1685.19160982 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 467496 +BPFP 0.4437 bits/point +EBPFP 0.4437 equivalent bits/point +MSE 1685.191610 +---------------------- -------------------------------------------------------- +Time: 5.168s Load: 0.056s, Pack+Encode: 2.600s, Decode+Unpack: 2.513s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1685.1916 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03014705-ILSVRC2012_val_00001168.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03014705-ILSVRC2012_val_00001168.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03017168-ILSVRC2012_val_00001601.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03017168-ILSVRC2012_val_00001601.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.054s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 173,568B, BPFP=0.3294 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 358,548B, BPFP=0.6806 +⌛️ [2/4] FRONTEND: Frontend time: 2.604s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.510s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10213913 24.33737625 + layer.39.0 475.40952988 8967.33916424 + ------------------------------------------------------------------------------------- + TOTAL 237.75583451 4495.83827024 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 532116 +BPFP 0.5050 bits/point +EBPFP 0.5050 equivalent bits/point +MSE 4495.838270 +---------------------- -------------------------------------------------------- +Time: 5.168s Load: 0.054s, Pack+Encode: 2.604s, Decode+Unpack: 2.510s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4495.8383 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03017168-ILSVRC2012_val_00001601.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03017168-ILSVRC2012_val_00001601.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03018349-ILSVRC2012_val_00000346.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03018349-ILSVRC2012_val_00000346.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 184,744B, BPFP=0.3507 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 416,308B, BPFP=0.7902 +⌛️ [2/4] FRONTEND: Frontend time: 2.602s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.494s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09959339 12.38266160 + layer.39.0 56.59841169 6563.20553936 + ------------------------------------------------------------------------------------- + TOTAL 28.34900254 3287.79410048 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 601052 +BPFP 0.5704 bits/point +EBPFP 0.5704 equivalent bits/point +MSE 3287.794100 +---------------------- -------------------------------------------------------- +Time: 5.145s Load: 0.050s, Pack+Encode: 2.602s, Decode+Unpack: 2.494s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3287.7941 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03018349-ILSVRC2012_val_00000346.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03018349-ILSVRC2012_val_00000346.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03026506-ILSVRC2012_val_00001908.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03026506-ILSVRC2012_val_00001908.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 194,020B, BPFP=0.3683 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 389,012B, BPFP=0.7384 +⌛️ [2/4] FRONTEND: Frontend time: 2.618s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.511s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10977067 45.86590819 + layer.39.0 668.54063411 7721.56608358 + ------------------------------------------------------------------------------------- + TOTAL 334.32520239 3883.71599588 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 583032 +BPFP 0.5533 bits/point +EBPFP 0.5533 equivalent bits/point +MSE 3883.715996 +---------------------- -------------------------------------------------------- +Time: 5.198s Load: 0.070s, Pack+Encode: 2.618s, Decode+Unpack: 2.511s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3883.7160 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03026506-ILSVRC2012_val_00001908.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03026506-ILSVRC2012_val_00001908.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03028079-ILSVRC2012_val_00003351.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03028079-ILSVRC2012_val_00003351.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 168,592B, BPFP=0.3200 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 292,520B, BPFP=0.5552 +⌛️ [2/4] FRONTEND: Frontend time: 2.614s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.502s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10934904 25.57494571 + layer.39.0 15.31112010 1823.47862002 + ------------------------------------------------------------------------------------- + TOTAL 7.71023457 924.52678287 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 461112 +BPFP 0.4376 bits/point +EBPFP 0.4376 equivalent bits/point +MSE 924.526783 +---------------------- -------------------------------------------------------- +Time: 5.187s Load: 0.071s, Pack+Encode: 2.614s, Decode+Unpack: 2.502s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 924.5268 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03028079-ILSVRC2012_val_00003351.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03028079-ILSVRC2012_val_00003351.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03032252-ILSVRC2012_val_00000086.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03032252-ILSVRC2012_val_00000086.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.053s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 242,316B, BPFP=0.4599 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 359,368B, BPFP=0.6821 +⌛️ [2/4] FRONTEND: Frontend time: 2.630s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.494s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13507480 1.46824440 + layer.39.0 103.55165816 3806.93075802 + ------------------------------------------------------------------------------------- + TOTAL 51.84336648 1904.19950121 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 601684 +BPFP 0.5710 bits/point +EBPFP 0.5710 equivalent bits/point +MSE 1904.199501 +---------------------- -------------------------------------------------------- +Time: 5.177s Load: 0.053s, Pack+Encode: 2.630s, Decode+Unpack: 2.494s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1904.1995 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03032252-ILSVRC2012_val_00000086.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03032252-ILSVRC2012_val_00000086.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03041632-ILSVRC2012_val_00000564.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03041632-ILSVRC2012_val_00000564.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 181,492B, BPFP=0.3445 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 311,544B, BPFP=0.5913 +⌛️ [2/4] FRONTEND: Frontend time: 2.602s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.517s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10123130 50.84713238 + layer.39.0 371.34277818 6716.27113703 + ------------------------------------------------------------------------------------- + TOTAL 185.72200474 3383.55913470 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 493036 +BPFP 0.4679 bits/point +EBPFP 0.4679 equivalent bits/point +MSE 3383.559135 +---------------------- -------------------------------------------------------- +Time: 5.170s Load: 0.051s, Pack+Encode: 2.602s, Decode+Unpack: 2.517s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3383.5591 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03041632-ILSVRC2012_val_00000564.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03041632-ILSVRC2012_val_00000564.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03042490-ILSVRC2012_val_00001426.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03042490-ILSVRC2012_val_00001426.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 206,952B, BPFP=0.3928 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 323,916B, BPFP=0.6148 +⌛️ [2/4] FRONTEND: Frontend time: 2.601s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.521s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10706725 22.16193665 + layer.39.0 141.71039845 4243.57240039 + ------------------------------------------------------------------------------------- + TOTAL 70.90873285 2132.86716852 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 530868 +BPFP 0.5038 bits/point +EBPFP 0.5038 equivalent bits/point +MSE 2132.867169 +---------------------- -------------------------------------------------------- +Time: 5.183s Load: 0.061s, Pack+Encode: 2.601s, Decode+Unpack: 2.521s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2132.8672 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03042490-ILSVRC2012_val_00001426.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03042490-ILSVRC2012_val_00001426.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03047690-ILSVRC2012_val_00001500.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03047690-ILSVRC2012_val_00001500.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 160,692B, BPFP=0.3050 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 369,104B, BPFP=0.7006 +⌛️ [2/4] FRONTEND: Frontend time: 2.629s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.504s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09570478 8.54478711 + layer.39.0 226.76483540 6723.21331390 + ------------------------------------------------------------------------------------- + TOTAL 113.43027009 3365.87905050 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 529796 +BPFP 0.5028 bits/point +EBPFP 0.5028 equivalent bits/point +MSE 3365.879051 +---------------------- -------------------------------------------------------- +Time: 5.204s Load: 0.070s, Pack+Encode: 2.629s, Decode+Unpack: 2.504s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3365.8791 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03047690-ILSVRC2012_val_00001500.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03047690-ILSVRC2012_val_00001500.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03062245-ILSVRC2012_val_00000344.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03062245-ILSVRC2012_val_00000344.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.091s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 137,004B, BPFP=0.2600 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 272,236B, BPFP=0.5167 +⌛️ [2/4] FRONTEND: Frontend time: 2.611s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.509s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09619164 0.86204714 + layer.39.0 46.71096787 1838.26554908 + ------------------------------------------------------------------------------------- + TOTAL 23.40357976 919.56379811 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 409240 +BPFP 0.3884 bits/point +EBPFP 0.3884 equivalent bits/point +MSE 919.563798 +---------------------- -------------------------------------------------------- +Time: 5.211s Load: 0.091s, Pack+Encode: 2.611s, Decode+Unpack: 2.509s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 919.5638 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03062245-ILSVRC2012_val_00000344.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03062245-ILSVRC2012_val_00000344.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063599-ILSVRC2012_val_00000164.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063599-ILSVRC2012_val_00000164.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 180,940B, BPFP=0.3434 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 381,296B, BPFP=0.7237 +⌛️ [2/4] FRONTEND: Frontend time: 2.612s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.494s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10111790 13.17849645 + layer.39.0 9.80528160 3668.34961127 + ------------------------------------------------------------------------------------- + TOTAL 4.95319975 1840.76405386 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 562236 +BPFP 0.5336 bits/point +EBPFP 0.5336 equivalent bits/point +MSE 1840.764054 +---------------------- -------------------------------------------------------- +Time: 5.156s Load: 0.050s, Pack+Encode: 2.612s, Decode+Unpack: 2.494s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1840.7641 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063599-ILSVRC2012_val_00000164.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03063599-ILSVRC2012_val_00000164.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063689-ILSVRC2012_val_00001940.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063689-ILSVRC2012_val_00001940.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 160,952B, BPFP=0.3055 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 357,064B, BPFP=0.6777 +⌛️ [2/4] FRONTEND: Frontend time: 2.593s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.507s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09645106 12.31169332 + layer.39.0 18.48014797 5314.69873664 + ------------------------------------------------------------------------------------- + TOTAL 9.28829952 2663.50521498 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 518016 +BPFP 0.4916 bits/point +EBPFP 0.4916 equivalent bits/point +MSE 2663.505215 +---------------------- -------------------------------------------------------- +Time: 5.151s Load: 0.052s, Pack+Encode: 2.593s, Decode+Unpack: 2.507s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2663.5052 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063689-ILSVRC2012_val_00001940.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03063689-ILSVRC2012_val_00001940.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03065424-ILSVRC2012_val_00000915.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03065424-ILSVRC2012_val_00000915.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 248,812B, BPFP=0.4723 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 386,796B, BPFP=0.7342 +⌛️ [2/4] FRONTEND: Frontend time: 2.612s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.498s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12384982 12.63772283 + layer.39.0 2154.15986395 9429.38483965 + ------------------------------------------------------------------------------------- + TOTAL 1077.14185688 4721.01128124 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 635608 +BPFP 0.6032 bits/point +EBPFP 0.6032 equivalent bits/point +MSE 4721.011281 +---------------------- -------------------------------------------------------- +Time: 5.171s Load: 0.061s, Pack+Encode: 2.612s, Decode+Unpack: 2.498s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4721.0113 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03065424-ILSVRC2012_val_00000915.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03065424-ILSVRC2012_val_00000915.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03075370-ILSVRC2012_val_00004971.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03075370-ILSVRC2012_val_00004971.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 156,584B, BPFP=0.2972 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 292,592B, BPFP=0.5554 +⌛️ [2/4] FRONTEND: Frontend time: 2.608s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.505s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10672879 12.97497950 + layer.39.0 301.29020894 2091.38629738 + ------------------------------------------------------------------------------------- + TOTAL 150.69846886 1052.18063844 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 449176 +BPFP 0.4263 bits/point +EBPFP 0.4263 equivalent bits/point +MSE 1052.180638 +---------------------- -------------------------------------------------------- +Time: 5.164s Load: 0.051s, Pack+Encode: 2.608s, Decode+Unpack: 2.505s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1052.1806 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03075370-ILSVRC2012_val_00004971.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03075370-ILSVRC2012_val_00004971.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03089624-ILSVRC2012_val_00001190.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03089624-ILSVRC2012_val_00001190.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 195,836B, BPFP=0.3717 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 358,184B, BPFP=0.6799 +⌛️ [2/4] FRONTEND: Frontend time: 2.606s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.515s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10385029 74.64131590 + layer.39.0 606.38896987 7363.96404276 + ------------------------------------------------------------------------------------- + TOTAL 303.24641008 3719.30267933 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 554020 +BPFP 0.5258 bits/point +EBPFP 0.5258 equivalent bits/point +MSE 3719.302679 +---------------------- -------------------------------------------------------- +Time: 5.181s Load: 0.060s, Pack+Encode: 2.606s, Decode+Unpack: 2.515s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3719.3027 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03089624-ILSVRC2012_val_00001190.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03089624-ILSVRC2012_val_00001190.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03095699-ILSVRC2012_val_00000403.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03095699-ILSVRC2012_val_00000403.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 237,380B, BPFP=0.4506 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 353,720B, BPFP=0.6714 +⌛️ [2/4] FRONTEND: Frontend time: 2.617s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.499s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12139760 119.10224581 + layer.39.0 62.59250486 5737.06754130 + ------------------------------------------------------------------------------------- + TOTAL 31.35695123 2928.08489356 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 591100 +BPFP 0.5610 bits/point +EBPFP 0.5610 equivalent bits/point +MSE 2928.084894 +---------------------- -------------------------------------------------------- +Time: 5.186s Load: 0.070s, Pack+Encode: 2.617s, Decode+Unpack: 2.499s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2928.0849 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03095699-ILSVRC2012_val_00000403.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03095699-ILSVRC2012_val_00000403.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03100240-ILSVRC2012_val_00001201.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03100240-ILSVRC2012_val_00001201.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.082s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 190,984B, BPFP=0.3625 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 273,656B, BPFP=0.5194 +⌛️ [2/4] FRONTEND: Frontend time: 2.612s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.504s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10258218 12.40510736 + layer.39.0 42.98202138 3481.74684159 + ------------------------------------------------------------------------------------- + TOTAL 21.54230178 1747.07597447 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 464640 +BPFP 0.4410 bits/point +EBPFP 0.4410 equivalent bits/point +MSE 1747.075974 +---------------------- -------------------------------------------------------- +Time: 5.198s Load: 0.082s, Pack+Encode: 2.612s, Decode+Unpack: 2.504s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1747.0760 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03100240-ILSVRC2012_val_00001201.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03100240-ILSVRC2012_val_00001201.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03109150-ILSVRC2012_val_00000678.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03109150-ILSVRC2012_val_00000678.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 194,708B, BPFP=0.3696 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 355,328B, BPFP=0.6744 +⌛️ [2/4] FRONTEND: Frontend time: 2.609s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.498s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09720685 12.27146919 + layer.39.0 496.21158285 6220.90962099 + ------------------------------------------------------------------------------------- + TOTAL 248.15439485 3116.59054509 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 550036 +BPFP 0.5220 bits/point +EBPFP 0.5220 equivalent bits/point +MSE 3116.590545 +---------------------- -------------------------------------------------------- +Time: 5.160s Load: 0.052s, Pack+Encode: 2.609s, Decode+Unpack: 2.498s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3116.5905 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03109150-ILSVRC2012_val_00000678.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03109150-ILSVRC2012_val_00000678.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03110669-ILSVRC2012_val_00002171.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03110669-ILSVRC2012_val_00002171.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 256,652B, BPFP=0.4871 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 405,708B, BPFP=0.7701 +⌛️ [2/4] FRONTEND: Frontend time: 2.620s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.505s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.15128201 52.51395849 + layer.39.0 15.00769387 5478.38775510 + ------------------------------------------------------------------------------------- + TOTAL 7.57948794 2765.45085679 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 662360 +BPFP 0.6286 bits/point +EBPFP 0.6286 equivalent bits/point +MSE 2765.450857 +---------------------- -------------------------------------------------------- +Time: 5.175s Load: 0.050s, Pack+Encode: 2.620s, Decode+Unpack: 2.505s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2765.4509 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03110669-ILSVRC2012_val_00002171.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03110669-ILSVRC2012_val_00002171.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124043-ILSVRC2012_val_00000766.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124043-ILSVRC2012_val_00000766.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 181,232B, BPFP=0.3440 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 354,796B, BPFP=0.6734 +⌛️ [2/4] FRONTEND: Frontend time: 2.609s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.499s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11473456 12.81277143 + layer.39.0 54.83309418 6829.75607386 + ------------------------------------------------------------------------------------- + TOTAL 27.47391437 3421.28442264 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 536028 +BPFP 0.5087 bits/point +EBPFP 0.5087 equivalent bits/point +MSE 3421.284423 +---------------------- -------------------------------------------------------- +Time: 5.178s Load: 0.070s, Pack+Encode: 2.609s, Decode+Unpack: 2.499s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3421.2844 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124043-ILSVRC2012_val_00000766.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03124043-ILSVRC2012_val_00000766.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124170-ILSVRC2012_val_00001875.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124170-ILSVRC2012_val_00001875.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 170,320B, BPFP=0.3233 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 266,456B, BPFP=0.5058 +⌛️ [2/4] FRONTEND: Frontend time: 2.613s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.512s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11393612 26.30128614 + layer.39.0 9.06747107 1803.33309038 + ------------------------------------------------------------------------------------- + TOTAL 4.59070360 914.81718826 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 436776 +BPFP 0.4145 bits/point +EBPFP 0.4145 equivalent bits/point +MSE 914.817188 +---------------------- -------------------------------------------------------- +Time: 5.196s Load: 0.072s, Pack+Encode: 2.613s, Decode+Unpack: 2.512s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 914.8172 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124170-ILSVRC2012_val_00001875.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03124170-ILSVRC2012_val_00001875.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03126707-ILSVRC2012_val_00000020.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03126707-ILSVRC2012_val_00000020.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 198,392B, BPFP=0.3766 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 282,748B, BPFP=0.5367 +⌛️ [2/4] FRONTEND: Frontend time: 2.623s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.485s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.15273996 157.94082544 + layer.39.0 1033.15269679 5463.23858115 + ------------------------------------------------------------------------------------- + TOTAL 516.65271838 2810.58970329 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 481140 +BPFP 0.4566 bits/point +EBPFP 0.4566 equivalent bits/point +MSE 2810.589703 +---------------------- -------------------------------------------------------- +Time: 5.179s Load: 0.071s, Pack+Encode: 2.623s, Decode+Unpack: 2.485s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2810.5897 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03126707-ILSVRC2012_val_00000020.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03126707-ILSVRC2012_val_00000020.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03127747-ILSVRC2012_val_00001689.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03127747-ILSVRC2012_val_00001689.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.058s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 156,096B, BPFP=0.2963 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 308,648B, BPFP=0.5858 +⌛️ [2/4] FRONTEND: Frontend time: 2.634s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.501s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10152024 0.89850180 + layer.39.0 322.92343902 2441.68634597 + ------------------------------------------------------------------------------------- + TOTAL 161.51247963 1221.29242388 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 464744 +BPFP 0.4411 bits/point +EBPFP 0.4411 equivalent bits/point +MSE 1221.292424 +---------------------- -------------------------------------------------------- +Time: 5.192s Load: 0.058s, Pack+Encode: 2.634s, Decode+Unpack: 2.501s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1221.2924 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03127747-ILSVRC2012_val_00001689.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03127747-ILSVRC2012_val_00001689.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03131574-ILSVRC2012_val_00003036.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03131574-ILSVRC2012_val_00003036.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 159,780B, BPFP=0.3033 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 342,528B, BPFP=0.6501 +⌛️ [2/4] FRONTEND: Frontend time: 2.608s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.488s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09568423 8.62752919 + layer.39.0 163.24681122 7127.04373178 + ------------------------------------------------------------------------------------- + TOTAL 81.67124773 3567.83563049 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 502308 +BPFP 0.4767 bits/point +EBPFP 0.4767 equivalent bits/point +MSE 3567.835630 +---------------------- -------------------------------------------------------- +Time: 5.166s Load: 0.070s, Pack+Encode: 2.608s, Decode+Unpack: 2.488s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3567.8356 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03131574-ILSVRC2012_val_00003036.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03131574-ILSVRC2012_val_00003036.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03133878-ILSVRC2012_val_00000534.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03133878-ILSVRC2012_val_00000534.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 236,004B, BPFP=0.4480 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 367,788B, BPFP=0.6981 +⌛️ [2/4] FRONTEND: Frontend time: 2.619s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.505s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11186348 27.67458926 + layer.39.0 28.46096218 6731.29348882 + ------------------------------------------------------------------------------------- + TOTAL 14.28641283 3379.48403904 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 603792 +BPFP 0.5730 bits/point +EBPFP 0.5730 equivalent bits/point +MSE 3379.484039 +---------------------- -------------------------------------------------------- +Time: 5.185s Load: 0.061s, Pack+Encode: 2.619s, Decode+Unpack: 2.505s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3379.4840 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03133878-ILSVRC2012_val_00000534.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03133878-ILSVRC2012_val_00000534.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03134739-ILSVRC2012_val_00000249.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03134739-ILSVRC2012_val_00000249.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 163,908B, BPFP=0.3111 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 400,428B, BPFP=0.7600 +⌛️ [2/4] FRONTEND: Frontend time: 2.599s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.497s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09967384 12.64324530 + layer.39.0 372.24465500 7113.50145773 + ------------------------------------------------------------------------------------- + TOTAL 186.17216442 3563.07235151 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 564336 +BPFP 0.5356 bits/point +EBPFP 0.5356 equivalent bits/point +MSE 3563.072352 +---------------------- -------------------------------------------------------- +Time: 5.156s Load: 0.060s, Pack+Encode: 2.599s, Decode+Unpack: 2.497s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3563.0724 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03134739-ILSVRC2012_val_00000249.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03134739-ILSVRC2012_val_00000249.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03141823-ILSVRC2012_val_00001337.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03141823-ILSVRC2012_val_00001337.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 203,752B, BPFP=0.3867 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 411,488B, BPFP=0.7810 +⌛️ [2/4] FRONTEND: Frontend time: 2.610s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.493s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10422104 25.00773468 + layer.39.0 29.45558301 3188.93294461 + ------------------------------------------------------------------------------------- + TOTAL 14.77990203 1606.97033964 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 615240 +BPFP 0.5839 bits/point +EBPFP 0.5839 equivalent bits/point +MSE 1606.970340 +---------------------- -------------------------------------------------------- +Time: 5.153s Load: 0.051s, Pack+Encode: 2.610s, Decode+Unpack: 2.493s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1606.9703 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03141823-ILSVRC2012_val_00001337.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03141823-ILSVRC2012_val_00001337.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03160309-ILSVRC2012_val_00000330.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03160309-ILSVRC2012_val_00000330.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.090s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 163,304B, BPFP=0.3100 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 245,792B, BPFP=0.4665 +⌛️ [2/4] FRONTEND: Frontend time: 2.646s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.503s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09980877 12.98171484 + layer.39.0 30.04123011 1815.74611273 + ------------------------------------------------------------------------------------- + TOTAL 15.07051944 914.36391379 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 409096 +BPFP 0.3882 bits/point +EBPFP 0.3882 equivalent bits/point +MSE 914.363914 +---------------------- -------------------------------------------------------- +Time: 5.239s Load: 0.090s, Pack+Encode: 2.646s, Decode+Unpack: 2.503s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 914.3639 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03160309-ILSVRC2012_val_00000330.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03160309-ILSVRC2012_val_00000330.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03187595-ILSVRC2012_val_00000137.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03187595-ILSVRC2012_val_00000137.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 176,640B, BPFP=0.3353 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 367,888B, BPFP=0.6983 +⌛️ [2/4] FRONTEND: Frontend time: 2.619s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.493s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10716813 0.89749398 + layer.39.0 12.39187394 3823.55053450 + ------------------------------------------------------------------------------------- + TOTAL 6.24952103 1912.22401424 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 544528 +BPFP 0.5168 bits/point +EBPFP 0.5168 equivalent bits/point +MSE 1912.224014 +---------------------- -------------------------------------------------------- +Time: 5.162s Load: 0.051s, Pack+Encode: 2.619s, Decode+Unpack: 2.493s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1912.2240 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03187595-ILSVRC2012_val_00000137.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03187595-ILSVRC2012_val_00000137.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03188531-ILSVRC2012_val_00000493.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03188531-ILSVRC2012_val_00000493.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 141,120B, BPFP=0.2679 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 329,752B, BPFP=0.6259 +⌛️ [2/4] FRONTEND: Frontend time: 2.644s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.524s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09509044 12.60879419 + layer.39.0 10.77256154 3036.08066084 + ------------------------------------------------------------------------------------- + TOTAL 5.43382599 1524.34472751 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 470872 +BPFP 0.4469 bits/point +EBPFP 0.4469 equivalent bits/point +MSE 1524.344728 +---------------------- -------------------------------------------------------- +Time: 5.248s Load: 0.080s, Pack+Encode: 2.644s, Decode+Unpack: 2.524s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1524.3447 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03188531-ILSVRC2012_val_00000493.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03188531-ILSVRC2012_val_00000493.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03196217-ILSVRC2012_val_00003643.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03196217-ILSVRC2012_val_00003643.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 144,880B, BPFP=0.2750 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 338,852B, BPFP=0.6432 +⌛️ [2/4] FRONTEND: Frontend time: 2.619s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.533s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09478207 12.55959499 + layer.39.0 65.57403274 3208.96817298 + ------------------------------------------------------------------------------------- + TOTAL 32.83440740 1610.76388399 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 483732 +BPFP 0.4591 bits/point +EBPFP 0.4591 equivalent bits/point +MSE 1610.763884 +---------------------- -------------------------------------------------------- +Time: 5.223s Load: 0.070s, Pack+Encode: 2.619s, Decode+Unpack: 2.533s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1610.7639 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03196217-ILSVRC2012_val_00003643.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03196217-ILSVRC2012_val_00003643.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03201208-ILSVRC2012_val_00000241.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03201208-ILSVRC2012_val_00000241.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 194,324B, BPFP=0.3688 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 296,112B, BPFP=0.5620 +⌛️ [2/4] FRONTEND: Frontend time: 2.650s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.499s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10331685 36.54268024 + layer.39.0 136.59314261 2243.48760933 + ------------------------------------------------------------------------------------- + TOTAL 68.34822973 1140.01514479 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 490436 +BPFP 0.4654 bits/point +EBPFP 0.4654 equivalent bits/point +MSE 1140.015145 +---------------------- -------------------------------------------------------- +Time: 5.208s Load: 0.059s, Pack+Encode: 2.650s, Decode+Unpack: 2.499s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1140.0151 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03201208-ILSVRC2012_val_00000241.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03201208-ILSVRC2012_val_00000241.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03207743-ILSVRC2012_val_00000256.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03207743-ILSVRC2012_val_00000256.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.055s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 259,448B, BPFP=0.4925 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 305,712B, BPFP=0.5803 +⌛️ [2/4] FRONTEND: Frontend time: 2.609s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.504s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09674843 2.20102762 + layer.39.0 189.63590258 2628.89990282 + ------------------------------------------------------------------------------------- + TOTAL 94.86632550 1315.55046522 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 565160 +BPFP 0.5364 bits/point +EBPFP 0.5364 equivalent bits/point +MSE 1315.550465 +---------------------- -------------------------------------------------------- +Time: 5.168s Load: 0.055s, Pack+Encode: 2.609s, Decode+Unpack: 2.504s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1315.5505 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03207743-ILSVRC2012_val_00000256.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03207743-ILSVRC2012_val_00000256.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03216828-ILSVRC2012_val_00001729.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03216828-ILSVRC2012_val_00001729.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 201,456B, BPFP=0.3824 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 305,052B, BPFP=0.5790 +⌛️ [2/4] FRONTEND: Frontend time: 2.608s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.498s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10800209 51.81654291 + layer.39.0 31.30713223 3605.82847425 + ------------------------------------------------------------------------------------- + TOTAL 15.70756716 1828.82250858 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 506508 +BPFP 0.4807 bits/point +EBPFP 0.4807 equivalent bits/point +MSE 1828.822509 +---------------------- -------------------------------------------------------- +Time: 5.156s Load: 0.051s, Pack+Encode: 2.608s, Decode+Unpack: 2.498s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1828.8225 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03216828-ILSVRC2012_val_00001729.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03216828-ILSVRC2012_val_00001729.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03218198-ILSVRC2012_val_00002266.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03218198-ILSVRC2012_val_00002266.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 225,724B, BPFP=0.4284 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 386,520B, BPFP=0.7336 +⌛️ [2/4] FRONTEND: Frontend time: 2.610s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.515s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11617067 47.41251746 + layer.39.0 195.83184524 6478.78036929 + ------------------------------------------------------------------------------------- + TOTAL 97.97400795 3263.09644338 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 612244 +BPFP 0.5810 bits/point +EBPFP 0.5810 equivalent bits/point +MSE 3263.096443 +---------------------- -------------------------------------------------------- +Time: 5.195s Load: 0.070s, Pack+Encode: 2.610s, Decode+Unpack: 2.515s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3263.0964 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03218198-ILSVRC2012_val_00002266.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03218198-ILSVRC2012_val_00002266.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03220513-ILSVRC2012_val_00001868.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03220513-ILSVRC2012_val_00001868.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.057s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 357,820B, BPFP=0.6792 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 349,064B, BPFP=0.6626 +⌛️ [2/4] FRONTEND: Frontend time: 2.640s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.507s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.20032125 382.64504373 + layer.39.0 377.00176142 6241.93683188 + ------------------------------------------------------------------------------------- + TOTAL 188.60104134 3312.29093780 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 706884 +BPFP 0.6709 bits/point +EBPFP 0.6709 equivalent bits/point +MSE 3312.290938 +---------------------- -------------------------------------------------------- +Time: 5.203s Load: 0.057s, Pack+Encode: 2.640s, Decode+Unpack: 2.507s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3312.2909 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03220513-ILSVRC2012_val_00001868.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03220513-ILSVRC2012_val_00001868.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03223299-ILSVRC2012_val_00001893.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03223299-ILSVRC2012_val_00001893.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 155,840B, BPFP=0.2958 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 279,988B, BPFP=0.5314 +⌛️ [2/4] FRONTEND: Frontend time: 2.618s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.517s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10735053 12.49873493 + layer.39.0 354.51621720 3076.76846453 + ------------------------------------------------------------------------------------- + TOTAL 177.31178386 1544.63359973 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 435828 +BPFP 0.4136 bits/point +EBPFP 0.4136 equivalent bits/point +MSE 1544.633600 +---------------------- -------------------------------------------------------- +Time: 5.206s Load: 0.071s, Pack+Encode: 2.618s, Decode+Unpack: 2.517s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1544.6336 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03223299-ILSVRC2012_val_00001893.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03223299-ILSVRC2012_val_00001893.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03240683-ILSVRC2012_val_00000504.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03240683-ILSVRC2012_val_00000504.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.089s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 171,056B, BPFP=0.3247 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 318,668B, BPFP=0.6049 +⌛️ [2/4] FRONTEND: Frontend time: 2.616s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.506s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10065408 12.44413664 + layer.39.0 443.53838678 4652.94460641 + ------------------------------------------------------------------------------------- + TOTAL 221.81952043 2332.69437153 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 489724 +BPFP 0.4648 bits/point +EBPFP 0.4648 equivalent bits/point +MSE 2332.694372 +---------------------- -------------------------------------------------------- +Time: 5.211s Load: 0.089s, Pack+Encode: 2.616s, Decode+Unpack: 2.506s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2332.6944 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03240683-ILSVRC2012_val_00000504.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03240683-ILSVRC2012_val_00000504.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03250847-ILSVRC2012_val_00000542.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03250847-ILSVRC2012_val_00000542.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.082s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 192,660B, BPFP=0.3657 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 396,088B, BPFP=0.7518 +⌛️ [2/4] FRONTEND: Frontend time: 2.626s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.507s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10136319 33.05153289 + layer.39.0 140.24735787 5619.20359572 + ------------------------------------------------------------------------------------- + TOTAL 70.17436053 2826.12756431 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 588748 +BPFP 0.5587 bits/point +EBPFP 0.5587 equivalent bits/point +MSE 2826.127564 +---------------------- -------------------------------------------------------- +Time: 5.214s Load: 0.082s, Pack+Encode: 2.626s, Decode+Unpack: 2.507s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2826.1276 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03250847-ILSVRC2012_val_00000542.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03250847-ILSVRC2012_val_00000542.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03255030-ILSVRC2012_val_00001045.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03255030-ILSVRC2012_val_00001045.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 162,480B, BPFP=0.3084 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 320,244B, BPFP=0.6078 +⌛️ [2/4] FRONTEND: Frontend time: 2.625s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.505s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10050351 12.54068346 + layer.39.0 12.06722622 4375.75996113 + ------------------------------------------------------------------------------------- + TOTAL 6.08386487 2194.15032229 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 482724 +BPFP 0.4581 bits/point +EBPFP 0.4581 equivalent bits/point +MSE 2194.150322 +---------------------- -------------------------------------------------------- +Time: 5.180s Load: 0.050s, Pack+Encode: 2.625s, Decode+Unpack: 2.505s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2194.1503 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03255030-ILSVRC2012_val_00001045.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03255030-ILSVRC2012_val_00001045.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03271574-ILSVRC2012_val_00000942.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03271574-ILSVRC2012_val_00000942.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.091s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 166,604B, BPFP=0.3162 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 332,416B, BPFP=0.6310 +⌛️ [2/4] FRONTEND: Frontend time: 2.624s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.501s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10164264 24.95671617 + layer.39.0 660.63544704 4757.52137998 + ------------------------------------------------------------------------------------- + TOTAL 330.36854484 2391.23904807 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 499020 +BPFP 0.4736 bits/point +EBPFP 0.4736 equivalent bits/point +MSE 2391.239048 +---------------------- -------------------------------------------------------- +Time: 5.216s Load: 0.091s, Pack+Encode: 2.624s, Decode+Unpack: 2.501s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2391.2390 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03271574-ILSVRC2012_val_00000942.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03271574-ILSVRC2012_val_00000942.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272010-ILSVRC2012_val_00000374.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272010-ILSVRC2012_val_00000374.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 168,276B, BPFP=0.3194 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 349,744B, BPFP=0.6638 +⌛️ [2/4] FRONTEND: Frontend time: 2.601s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.498s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10420663 12.72693452 + layer.39.0 9.63653369 2388.76967930 + ------------------------------------------------------------------------------------- + TOTAL 4.87037016 1200.74830691 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 518020 +BPFP 0.4916 bits/point +EBPFP 0.4916 equivalent bits/point +MSE 1200.748307 +---------------------- -------------------------------------------------------- +Time: 5.152s Load: 0.052s, Pack+Encode: 2.601s, Decode+Unpack: 2.498s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1200.7483 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272010-ILSVRC2012_val_00000374.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03272010-ILSVRC2012_val_00000374.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272562-ILSVRC2012_val_00001699.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272562-ILSVRC2012_val_00001699.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 213,704B, BPFP=0.4056 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 293,676B, BPFP=0.5574 +⌛️ [2/4] FRONTEND: Frontend time: 2.641s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.495s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11399285 35.31617468 + layer.39.0 12.79457642 6058.66763848 + ------------------------------------------------------------------------------------- + TOTAL 6.45428464 3046.99190658 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 507380 +BPFP 0.4815 bits/point +EBPFP 0.4815 equivalent bits/point +MSE 3046.991907 +---------------------- -------------------------------------------------------- +Time: 5.187s Load: 0.051s, Pack+Encode: 2.641s, Decode+Unpack: 2.495s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3046.9919 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272562-ILSVRC2012_val_00001699.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03272562-ILSVRC2012_val_00001699.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03290653-ILSVRC2012_val_00000199.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03290653-ILSVRC2012_val_00000199.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 161,184B, BPFP=0.3059 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 327,044B, BPFP=0.6208 +⌛️ [2/4] FRONTEND: Frontend time: 2.603s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.510s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09581849 0.85248147 + layer.39.0 9.30266794 2166.90500486 + ------------------------------------------------------------------------------------- + TOTAL 4.69924322 1083.87874316 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 488228 +BPFP 0.4633 bits/point +EBPFP 0.4633 equivalent bits/point +MSE 1083.878743 +---------------------- -------------------------------------------------------- +Time: 5.185s Load: 0.071s, Pack+Encode: 2.603s, Decode+Unpack: 2.510s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1083.8787 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03290653-ILSVRC2012_val_00000199.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03290653-ILSVRC2012_val_00000199.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03291819-ILSVRC2012_val_00000419.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03291819-ILSVRC2012_val_00000419.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 139,172B, BPFP=0.2642 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 299,592B, BPFP=0.5686 +⌛️ [2/4] FRONTEND: Frontend time: 2.596s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.496s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10621172 37.63018935 + layer.39.0 31.36357166 3386.43124393 + ------------------------------------------------------------------------------------- + TOTAL 15.73489169 1712.03071664 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 438764 +BPFP 0.4164 bits/point +EBPFP 0.4164 equivalent bits/point +MSE 1712.030717 +---------------------- -------------------------------------------------------- +Time: 5.154s Load: 0.061s, Pack+Encode: 2.596s, Decode+Unpack: 2.496s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1712.0307 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03291819-ILSVRC2012_val_00000419.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03291819-ILSVRC2012_val_00000419.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03314780-ILSVRC2012_val_00000624.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03314780-ILSVRC2012_val_00000624.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 178,456B, BPFP=0.3387 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 386,420B, BPFP=0.7335 +⌛️ [2/4] FRONTEND: Frontend time: 2.586s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.493s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10172509 32.78530430 + layer.39.0 35.60390853 7112.11661808 + ------------------------------------------------------------------------------------- + TOTAL 17.85281681 3572.45096119 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 564876 +BPFP 0.5361 bits/point +EBPFP 0.5361 equivalent bits/point +MSE 3572.450961 +---------------------- -------------------------------------------------------- +Time: 5.130s Load: 0.051s, Pack+Encode: 2.586s, Decode+Unpack: 2.493s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3572.4510 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03314780-ILSVRC2012_val_00000624.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03314780-ILSVRC2012_val_00000624.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03325584-ILSVRC2012_val_00001256.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03325584-ILSVRC2012_val_00001256.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.081s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 221,572B, BPFP=0.4206 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 362,784B, BPFP=0.6886 +⌛️ [2/4] FRONTEND: Frontend time: 2.610s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.507s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11348933 37.53268495 + layer.39.0 26.85401292 4011.37026239 + ------------------------------------------------------------------------------------- + TOTAL 13.48375113 2024.45147367 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 584356 +BPFP 0.5546 bits/point +EBPFP 0.5546 equivalent bits/point +MSE 2024.451474 +---------------------- -------------------------------------------------------- +Time: 5.198s Load: 0.081s, Pack+Encode: 2.610s, Decode+Unpack: 2.507s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2024.4515 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03325584-ILSVRC2012_val_00001256.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03325584-ILSVRC2012_val_00001256.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03337140-ILSVRC2012_val_00000132.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03337140-ILSVRC2012_val_00000132.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.079s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 151,848B, BPFP=0.2882 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 300,512B, BPFP=0.5704 +⌛️ [2/4] FRONTEND: Frontend time: 2.661s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.513s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09852950 12.22661470 + layer.39.0 10.39905343 2110.51384840 + ------------------------------------------------------------------------------------- + TOTAL 5.24879146 1061.37023155 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 452360 +BPFP 0.4293 bits/point +EBPFP 0.4293 equivalent bits/point +MSE 1061.370232 +---------------------- -------------------------------------------------------- +Time: 5.254s Load: 0.079s, Pack+Encode: 2.661s, Decode+Unpack: 2.513s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1061.3702 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03337140-ILSVRC2012_val_00000132.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03337140-ILSVRC2012_val_00000132.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03344393-ILSVRC2012_val_00000288.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03344393-ILSVRC2012_val_00000288.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.081s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 160,484B, BPFP=0.3046 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 329,476B, BPFP=0.6254 +⌛️ [2/4] FRONTEND: Frontend time: 2.628s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.485s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09830858 12.40976335 + layer.39.0 109.00505649 3477.48177843 + ------------------------------------------------------------------------------------- + TOTAL 54.55168253 1744.94577089 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 489960 +BPFP 0.4650 bits/point +EBPFP 0.4650 equivalent bits/point +MSE 1744.945771 +---------------------- -------------------------------------------------------- +Time: 5.194s Load: 0.081s, Pack+Encode: 2.628s, Decode+Unpack: 2.485s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1744.9458 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03344393-ILSVRC2012_val_00000288.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03344393-ILSVRC2012_val_00000288.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03345487-ILSVRC2012_val_00000764.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03345487-ILSVRC2012_val_00000764.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.090s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 190,692B, BPFP=0.3619 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 334,820B, BPFP=0.6355 +⌛️ [2/4] FRONTEND: Frontend time: 2.606s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.515s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10639974 0.94782686 + layer.39.0 14.55993569 4949.74732750 + ------------------------------------------------------------------------------------- + TOTAL 7.33316771 2475.34757718 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 525512 +BPFP 0.4987 bits/point +EBPFP 0.4987 equivalent bits/point +MSE 2475.347577 +---------------------- -------------------------------------------------------- +Time: 5.211s Load: 0.090s, Pack+Encode: 2.606s, Decode+Unpack: 2.515s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2475.3476 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03345487-ILSVRC2012_val_00000764.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03345487-ILSVRC2012_val_00000764.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03347037-ILSVRC2012_val_00000743.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03347037-ILSVRC2012_val_00000743.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 256,792B, BPFP=0.4874 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 362,520B, BPFP=0.6881 +⌛️ [2/4] FRONTEND: Frontend time: 2.624s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.520s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14351733 123.75252824 + layer.39.0 355.98426871 3344.69582119 + ------------------------------------------------------------------------------------- + TOTAL 178.06389302 1734.22417471 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 619312 +BPFP 0.5878 bits/point +EBPFP 0.5878 equivalent bits/point +MSE 1734.224175 +---------------------- -------------------------------------------------------- +Time: 5.194s Load: 0.050s, Pack+Encode: 2.624s, Decode+Unpack: 2.520s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1734.2242 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03347037-ILSVRC2012_val_00000743.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03347037-ILSVRC2012_val_00000743.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03355925-ILSVRC2012_val_00000445.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03355925-ILSVRC2012_val_00000445.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 139,496B, BPFP=0.2648 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 240,060B, BPFP=0.4557 +⌛️ [2/4] FRONTEND: Frontend time: 2.634s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.500s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09979894 13.05177015 + layer.39.0 9.06502540 1366.97120991 + ------------------------------------------------------------------------------------- + TOTAL 4.58241217 690.01149003 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 379556 +BPFP 0.3602 bits/point +EBPFP 0.3602 equivalent bits/point +MSE 690.011490 +---------------------- -------------------------------------------------------- +Time: 5.185s Load: 0.051s, Pack+Encode: 2.634s, Decode+Unpack: 2.500s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 690.0115 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03355925-ILSVRC2012_val_00000445.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03355925-ILSVRC2012_val_00000445.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03376595-ILSVRC2012_val_00001616.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03376595-ILSVRC2012_val_00001616.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.056s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 216,108B, BPFP=0.4102 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 387,048B, BPFP=0.7346 +⌛️ [2/4] FRONTEND: Frontend time: 2.642s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.498s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09988844 12.86904572 + layer.39.0 1408.20760447 9405.78814383 + ------------------------------------------------------------------------------------- + TOTAL 704.15374646 4709.32859477 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 603156 +BPFP 0.5724 bits/point +EBPFP 0.5724 equivalent bits/point +MSE 4709.328595 +---------------------- -------------------------------------------------------- +Time: 5.196s Load: 0.056s, Pack+Encode: 2.642s, Decode+Unpack: 2.498s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4709.3286 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03376595-ILSVRC2012_val_00001616.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03376595-ILSVRC2012_val_00001616.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03379051-ILSVRC2012_val_00002562.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03379051-ILSVRC2012_val_00002562.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 205,216B, BPFP=0.3895 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 378,992B, BPFP=0.7194 +⌛️ [2/4] FRONTEND: Frontend time: 2.595s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.505s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10889592 24.66656607 + layer.39.0 102.95462828 5877.67395530 + ------------------------------------------------------------------------------------- + TOTAL 51.53176210 2951.17026068 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 584208 +BPFP 0.5544 bits/point +EBPFP 0.5544 equivalent bits/point +MSE 2951.170261 +---------------------- -------------------------------------------------------- +Time: 5.171s Load: 0.071s, Pack+Encode: 2.595s, Decode+Unpack: 2.505s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2951.1703 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03379051-ILSVRC2012_val_00002562.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03379051-ILSVRC2012_val_00002562.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388043-ILSVRC2012_val_00001018.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388043-ILSVRC2012_val_00001018.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 165,776B, BPFP=0.3147 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 303,652B, BPFP=0.5764 +⌛️ [2/4] FRONTEND: Frontend time: 2.608s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.488s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09747427 13.34978779 + layer.39.0 21.12933142 2133.91982507 + ------------------------------------------------------------------------------------- + TOTAL 10.61340285 1073.63480643 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 469428 +BPFP 0.4455 bits/point +EBPFP 0.4455 equivalent bits/point +MSE 1073.634806 +---------------------- -------------------------------------------------------- +Time: 5.158s Load: 0.061s, Pack+Encode: 2.608s, Decode+Unpack: 2.488s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1073.6348 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388043-ILSVRC2012_val_00001018.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03388043-ILSVRC2012_val_00001018.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388183-ILSVRC2012_val_00002799.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388183-ILSVRC2012_val_00002799.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 203,344B, BPFP=0.3860 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 354,668B, BPFP=0.6732 +⌛️ [2/4] FRONTEND: Frontend time: 2.605s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.498s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10066175 13.02629411 + layer.39.0 786.68810739 8176.01846453 + ------------------------------------------------------------------------------------- + TOTAL 393.39438457 4094.52237932 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 558012 +BPFP 0.5296 bits/point +EBPFP 0.5296 equivalent bits/point +MSE 4094.522379 +---------------------- -------------------------------------------------------- +Time: 5.154s Load: 0.050s, Pack+Encode: 2.605s, Decode+Unpack: 2.498s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 4094.5224 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388183-ILSVRC2012_val_00002799.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03388183-ILSVRC2012_val_00002799.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388549-ILSVRC2012_val_00002945.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388549-ILSVRC2012_val_00002945.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.081s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 175,412B, BPFP=0.3329 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 323,384B, BPFP=0.6138 +⌛️ [2/4] FRONTEND: Frontend time: 2.627s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.504s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09849939 12.36161189 + layer.39.0 10.79426799 3755.54640428 + ------------------------------------------------------------------------------------- + TOTAL 5.44638369 1883.95400808 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 498796 +BPFP 0.4734 bits/point +EBPFP 0.4734 equivalent bits/point +MSE 1883.954008 +---------------------- -------------------------------------------------------- +Time: 5.212s Load: 0.081s, Pack+Encode: 2.627s, Decode+Unpack: 2.504s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1883.9540 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388549-ILSVRC2012_val_00002945.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03388549-ILSVRC2012_val_00002945.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03393912-ILSVRC2012_val_00000047.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03393912-ILSVRC2012_val_00000047.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 172,708B, BPFP=0.3278 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 319,116B, BPFP=0.6057 +⌛️ [2/4] FRONTEND: Frontend time: 2.610s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.502s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09729456 12.18304900 + layer.39.0 38.26720800 7289.14625850 + ------------------------------------------------------------------------------------- + TOTAL 19.18225128 3650.66465375 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 491824 +BPFP 0.4668 bits/point +EBPFP 0.4668 equivalent bits/point +MSE 3650.664654 +---------------------- -------------------------------------------------------- +Time: 5.184s Load: 0.072s, Pack+Encode: 2.610s, Decode+Unpack: 2.502s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3650.6647 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03393912-ILSVRC2012_val_00000047.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03393912-ILSVRC2012_val_00000047.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03394916-ILSVRC2012_val_00000957.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03394916-ILSVRC2012_val_00000957.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.058s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 159,992B, BPFP=0.3037 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 322,000B, BPFP=0.6112 +⌛️ [2/4] FRONTEND: Frontend time: 2.606s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.510s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10421823 37.52747662 + layer.39.0 9.72561820 2580.93658892 + ------------------------------------------------------------------------------------- + TOTAL 4.91491822 1309.23203277 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 481992 +BPFP 0.4574 bits/point +EBPFP 0.4574 equivalent bits/point +MSE 1309.232033 +---------------------- -------------------------------------------------------- +Time: 5.174s Load: 0.058s, Pack+Encode: 2.606s, Decode+Unpack: 2.510s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1309.2320 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03394916-ILSVRC2012_val_00000957.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03394916-ILSVRC2012_val_00000957.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03404251-ILSVRC2012_val_00000641.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03404251-ILSVRC2012_val_00000641.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.056s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 167,156B, BPFP=0.3173 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 368,728B, BPFP=0.6999 +⌛️ [2/4] FRONTEND: Frontend time: 2.596s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.498s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10764784 48.66396760 + layer.39.0 585.45553936 7242.99805637 + ------------------------------------------------------------------------------------- + TOTAL 292.78159360 3645.83101198 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 535884 +BPFP 0.5086 bits/point +EBPFP 0.5086 equivalent bits/point +MSE 3645.831012 +---------------------- -------------------------------------------------------- +Time: 5.150s Load: 0.056s, Pack+Encode: 2.596s, Decode+Unpack: 2.498s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3645.8310 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03404251-ILSVRC2012_val_00000641.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03404251-ILSVRC2012_val_00000641.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03417042-ILSVRC2012_val_00001144.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03417042-ILSVRC2012_val_00001144.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 176,416B, BPFP=0.3349 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 334,208B, BPFP=0.6344 +⌛️ [2/4] FRONTEND: Frontend time: 2.597s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.512s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10091509 13.58736581 + layer.39.0 202.93364310 5513.25170068 + ------------------------------------------------------------------------------------- + TOTAL 101.51727910 2763.41953324 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 510624 +BPFP 0.4846 bits/point +EBPFP 0.4846 equivalent bits/point +MSE 2763.419533 +---------------------- -------------------------------------------------------- +Time: 5.179s Load: 0.070s, Pack+Encode: 2.597s, Decode+Unpack: 2.512s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2763.4195 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03417042-ILSVRC2012_val_00001144.zst + to output-fixed/dinov3-total/lambda0.007/elic-featurecoding-8bit-layerwise/cls_in1kval/n03417042-ILSVRC2012_val_00001144.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 0.4969 bits/point +Avg EBPFP 0.4969 equivalent bits/point +Avg MSE 2314.681760 +Avg Time 5.184s +------------------------ ---------------------------- diff --git a/lambda0.007/hyperprior-featurecoding-8bit-individual/cls_in1ka/dtufc_hyperprior-featurecoding_dinov3-total_individual.log b/lambda0.007/hyperprior-featurecoding-8bit-individual/cls_in1ka/dtufc_hyperprior-featurecoding_dinov3-total_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..976df08cbdef7bf8bfbe7f3d7e86a2dc5cfdb5ba --- /dev/null +++ b/lambda0.007/hyperprior-featurecoding-8bit-individual/cls_in1ka/dtufc_hyperprior-featurecoding_dinov3-total_individual.log @@ -0,0 +1,9344 @@ +Experiment: dtufc_hyperprior-featurecoding_dinov3-total_individual +Log file: output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/dtufc_hyperprior-featurecoding_dinov3-total_individual.log +DTUFCCodecConfig: + arch: hyperprior-featurecoding + handler: dinov3-total + checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.007_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.007_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 599 +Loaded hyperprior-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.9' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.19' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.29' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.39' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json +Loaded per-key mappings: model=dinov3-total + Keys: ['layer.9', 'layer.19', 'layer.29', 'layer.39'] +---------------- ------------------------------------------------------------------------------------------------------------------------------ +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +Checkpoint codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.007_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-DINOv3/imagenet1k-a +Output output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka +---------------- ------------------------------------------------------------------------------------------------------------------------------ +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01498041-0.006639_koala _ American bullfrog_0.6658246.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01498041-0.006639_koala _ American bullfrog_0.6658246.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.091s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 239,032B, BPFP=0.4537 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 430,332B, BPFP=0.8168 +⌛️ [2/4] FRONTEND: Frontend time: 0.933s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.200s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09594801 13.10448649 + layer.39.0 58.94484178 2283.81438290 + ------------------------------------------------------------------------------------- + TOTAL 29.52039490 1148.45943469 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 669364 +BPFP 0.6353 bits/point +EBPFP 0.6353 equivalent bits/point +MSE 1148.459435 +---------------------- -------------------------------------------------------- +Time: 2.224s Load: 0.091s, Pack+Encode: 0.933s, Decode+Unpack: 1.200s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1148.4594 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01498041-0.006639_koala _ American bullfrog_0.6658246.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01498041-0.006639_koala _ American bullfrog_0.6658246.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01531178-0.000765_fox squirrel _ ant_0.9486805.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01531178-0.000765_fox squirrel _ ant_0.9486805.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 228,976B, BPFP=0.4346 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 498,824B, BPFP=0.9468 +⌛️ [2/4] FRONTEND: Frontend time: 0.593s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.083s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09773727 63.61153122 + layer.39.0 17.17825445 2152.96598639 + ------------------------------------------------------------------------------------- + TOTAL 8.63799586 1108.28875881 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 727800 +BPFP 0.6907 bits/point +EBPFP 0.6907 equivalent bits/point +MSE 1108.288759 +---------------------- -------------------------------------------------------- +Time: 1.756s Load: 0.080s, Pack+Encode: 0.593s, Decode+Unpack: 1.083s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1108.2888 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01531178-0.000765_fox squirrel _ ant_0.9486805.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01531178-0.000765_fox squirrel _ ant_0.9486805.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01534433-0.004573_stingray _ stingray_0.97124094.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01534433-0.004573_stingray _ stingray_0.97124094.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.085s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 179,692B, BPFP=0.3411 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 324,404B, BPFP=0.6157 +⌛️ [2/4] FRONTEND: Frontend time: 0.577s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.073s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09515371 12.78158786 + layer.39.0 6.87362484 1340.04324587 + ------------------------------------------------------------------------------------- + TOTAL 3.48438928 676.41241686 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 504096 +BPFP 0.4784 bits/point +EBPFP 0.4784 equivalent bits/point +MSE 676.412417 +---------------------- -------------------------------------------------------- +Time: 1.735s Load: 0.085s, Pack+Encode: 0.577s, Decode+Unpack: 1.073s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 676.4124 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01534433-0.004573_stingray _ stingray_0.97124094.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01534433-0.004573_stingray _ stingray_0.97124094.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01558993-0.000522_bow _ bow_0.9033333.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01558993-0.000522_bow _ bow_0.9033333.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 252,032B, BPFP=0.4784 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 365,240B, BPFP=0.6933 +⌛️ [2/4] FRONTEND: Frontend time: 0.582s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.048s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09874929 590.87402818 + layer.39.0 7.31778236 1268.53741497 + ------------------------------------------------------------------------------------- + TOTAL 3.70826583 929.70572157 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 617272 +BPFP 0.5858 bits/point +EBPFP 0.5858 equivalent bits/point +MSE 929.705722 +---------------------- -------------------------------------------------------- +Time: 1.689s Load: 0.059s, Pack+Encode: 0.582s, Decode+Unpack: 1.048s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 929.7057 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01558993-0.000522_bow _ bow_0.9033333.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01558993-0.000522_bow _ bow_0.9033333.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01580077-0.004583_African bush elephant _ African bush elephant_0.59939015.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01580077-0.004583_African bush elephant _ African bush elephant_0.59939015.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 237,516B, BPFP=0.4508 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 463,152B, BPFP=0.8791 +⌛️ [2/4] FRONTEND: Frontend time: 0.493s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.021s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10720986 260.12730807 + layer.39.0 24.46209533 1712.57288630 + ------------------------------------------------------------------------------------- + TOTAL 12.28465260 986.35009718 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 700668 +BPFP 0.6650 bits/point +EBPFP 0.6650 equivalent bits/point +MSE 986.350097 +---------------------- -------------------------------------------------------- +Time: 1.565s Load: 0.051s, Pack+Encode: 0.493s, Decode+Unpack: 1.021s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 986.3501 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01580077-0.004583_African bush elephant _ African bush elephant_0.59939015.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01580077-0.004583_African bush elephant _ African bush elephant_0.59939015.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01614925-0.049724_white-headed capuchin _ white-headed capuchin_0.9309422.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01614925-0.049724_white-headed capuchin _ white-headed capuchin_0.9309422.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 258,520B, BPFP=0.4907 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 401,284B, BPFP=0.7617 +⌛️ [2/4] FRONTEND: Frontend time: 0.588s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.063s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09739119 296.38887877 + layer.39.0 8.81423010 1431.98918853 + ------------------------------------------------------------------------------------- + TOTAL 4.45581065 864.18903365 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 659804 +BPFP 0.6262 bits/point +EBPFP 0.6262 equivalent bits/point +MSE 864.189034 +---------------------- -------------------------------------------------------- +Time: 1.721s Load: 0.069s, Pack+Encode: 0.588s, Decode+Unpack: 1.063s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 864.1890 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01614925-0.049724_white-headed capuchin _ white-headed capuchin_0.9309422.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01614925-0.049724_white-headed capuchin _ white-headed capuchin_0.9309422.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01631663-0.004606_American bullfrog _ American bullfrog_0.8789855.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01631663-0.004606_American bullfrog _ American bullfrog_0.8789855.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 213,720B, BPFP=0.4057 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 472,752B, BPFP=0.8973 +⌛️ [2/4] FRONTEND: Frontend time: 0.578s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.056s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09716670 13.17146217 + layer.39.0 20.45897868 1585.75182216 + ------------------------------------------------------------------------------------- + TOTAL 10.27807269 799.46164216 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 686472 +BPFP 0.6515 bits/point +EBPFP 0.6515 equivalent bits/point +MSE 799.461642 +---------------------- -------------------------------------------------------- +Time: 1.704s Load: 0.071s, Pack+Encode: 0.578s, Decode+Unpack: 1.056s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 799.4616 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01631663-0.004606_American bullfrog _ American bullfrog_0.8789855.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01631663-0.004606_American bullfrog _ American bullfrog_0.8789855.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01669191-0.029754_sandal _ sandal_0.38198605.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01669191-0.029754_sandal _ sandal_0.38198605.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 321,640B, BPFP=0.6105 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 351,872B, BPFP=0.6679 +⌛️ [2/4] FRONTEND: Frontend time: 0.531s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.014s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10877632 867.07640914 + layer.39.0 13.16500205 1403.26117590 + ------------------------------------------------------------------------------------- + TOTAL 6.63688918 1135.16879252 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 673512 +BPFP 0.6392 bits/point +EBPFP 0.6392 equivalent bits/point +MSE 1135.168793 +---------------------- -------------------------------------------------------- +Time: 1.597s Load: 0.052s, Pack+Encode: 0.531s, Decode+Unpack: 1.014s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1135.1688 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01669191-0.029754_sandal _ sandal_0.38198605.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01669191-0.029754_sandal _ sandal_0.38198605.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770081-0.000571_syringe _ syringe_0.7369336.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770081-0.000571_syringe _ syringe_0.7369336.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 192,832B, BPFP=0.3660 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 381,020B, BPFP=0.7232 +⌛️ [2/4] FRONTEND: Frontend time: 0.576s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.031s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09508557 13.00424221 + layer.39.0 60.03878538 1866.91047133 + ------------------------------------------------------------------------------------- + TOTAL 30.06693547 939.95735677 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 573852 +BPFP 0.5446 bits/point +EBPFP 0.5446 equivalent bits/point +MSE 939.957357 +---------------------- -------------------------------------------------------- +Time: 1.677s Load: 0.070s, Pack+Encode: 0.576s, Decode+Unpack: 1.031s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 939.9574 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770081-0.000571_syringe _ syringe_0.7369336.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01770081-0.000571_syringe _ syringe_0.7369336.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770393-0.000908_harvestman _ harvestman_0.8782497.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770393-0.000908_harvestman _ harvestman_0.8782497.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 14.546s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 235,992B, BPFP=0.4479 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 482,200B, BPFP=0.9153 +⌛️ [2/4] FRONTEND: Frontend time: 0.558s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.060s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11350316 25.41730442 + layer.39.0 19.73148992 1900.54421769 + ------------------------------------------------------------------------------------- + TOTAL 9.92249654 962.98076105 + (elements=8,429,568) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 8429568 +Total Bytes 718192 +BPFP 0.6816 bits/point +EBPFP 0.6816 equivalent bits/point +MSE 962.980761 +---------------------- --------------------------------------------------------- +Time: 16.165s Load: 14.546s, Pack+Encode: 0.558s, Decode+Unpack: 1.060s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 962.9808 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770393-0.000908_harvestman _ harvestman_0.8782497.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01770393-0.000908_harvestman _ harvestman_0.8782497.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01784675-0.027853_syringe _ syringe_0.9584382.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01784675-0.027853_syringe _ syringe_0.9584382.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 300,864B, BPFP=0.5711 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 299,004B, BPFP=0.5675 +⌛️ [2/4] FRONTEND: Frontend time: 0.573s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.038s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11002613 494.11330782 + layer.39.0 26.08665877 2100.78425656 + ------------------------------------------------------------------------------------- + TOTAL 13.09834245 1297.44878219 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 599868 +BPFP 0.5693 bits/point +EBPFP 0.5693 equivalent bits/point +MSE 1297.448782 +---------------------- -------------------------------------------------------- +Time: 1.671s Load: 0.059s, Pack+Encode: 0.573s, Decode+Unpack: 1.038s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1297.4488 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01784675-0.027853_syringe _ syringe_0.9584382.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01784675-0.027853_syringe _ syringe_0.9584382.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01819313-0.053742_koala _ koala_0.98647016.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01819313-0.053742_koala _ koala_0.98647016.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.081s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 294,984B, BPFP=0.5599 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 465,868B, BPFP=0.8843 +⌛️ [2/4] FRONTEND: Frontend time: 0.587s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.058s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14565475 642.93962585 + layer.39.0 25.01023445 2425.16763848 + ------------------------------------------------------------------------------------- + TOTAL 12.57794460 1534.05363217 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 760852 +BPFP 0.7221 bits/point +EBPFP 0.7221 equivalent bits/point +MSE 1534.053632 +---------------------- -------------------------------------------------------- +Time: 1.726s Load: 0.081s, Pack+Encode: 0.587s, Decode+Unpack: 1.058s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1534.0536 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01819313-0.053742_koala _ koala_0.98647016.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01819313-0.053742_koala _ koala_0.98647016.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01820546-0.012522_toucan _ toucan_0.63882655.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01820546-0.012522_toucan _ toucan_0.63882655.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 227,916B, BPFP=0.4326 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 476,624B, BPFP=0.9047 +⌛️ [2/4] FRONTEND: Frontend time: 0.544s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.996s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09696376 49.89074268 + layer.39.0 16.65489097 1994.96647230 + ------------------------------------------------------------------------------------- + TOTAL 8.37592737 1022.42860749 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 704540 +BPFP 0.6686 bits/point +EBPFP 0.6686 equivalent bits/point +MSE 1022.428607 +---------------------- -------------------------------------------------------- +Time: 1.591s Load: 0.051s, Pack+Encode: 0.544s, Decode+Unpack: 0.996s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1022.4286 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01820546-0.012522_toucan _ toucan_0.63882655.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01820546-0.012522_toucan _ toucan_0.63882655.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01833805-0.013248_toucan _ lorikeet_0.77773976.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01833805-0.013248_toucan _ lorikeet_0.77773976.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 218,304B, BPFP=0.4144 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 487,560B, BPFP=0.9254 +⌛️ [2/4] FRONTEND: Frontend time: 0.507s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.032s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09866240 12.49610799 + layer.39.0 7.67772963 1594.75947522 + ------------------------------------------------------------------------------------- + TOTAL 3.88819601 803.62779160 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 705864 +BPFP 0.6699 bits/point +EBPFP 0.6699 equivalent bits/point +MSE 803.627792 +---------------------- -------------------------------------------------------- +Time: 1.591s Load: 0.051s, Pack+Encode: 0.507s, Decode+Unpack: 1.032s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 803.6278 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01833805-0.013248_toucan _ lorikeet_0.77773976.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01833805-0.013248_toucan _ lorikeet_0.77773976.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01843383-0.013605_white-headed capuchin _ white-headed capuchin_0.9984768.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01843383-0.013605_white-headed capuchin _ white-headed capuchin_0.9984768.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 269,732B, BPFP=0.5120 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 430,940B, BPFP=0.8180 +⌛️ [2/4] FRONTEND: Frontend time: 0.568s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.055s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11910487 285.13657070 + layer.39.0 9.20068692 2115.01093294 + ------------------------------------------------------------------------------------- + TOTAL 4.65989589 1200.07375182 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 700672 +BPFP 0.6650 bits/point +EBPFP 0.6650 equivalent bits/point +MSE 1200.073752 +---------------------- -------------------------------------------------------- +Time: 1.674s Load: 0.051s, Pack+Encode: 0.568s, Decode+Unpack: 1.055s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1200.0738 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01843383-0.013605_white-headed capuchin _ white-headed capuchin_0.9984768.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01843383-0.013605_white-headed capuchin _ white-headed capuchin_0.9984768.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01924916-0.000644_jay _ jay_0.82223135.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01924916-0.000644_jay _ jay_0.82223135.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 284,296B, BPFP=0.5396 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 381,384B, BPFP=0.7239 +⌛️ [2/4] FRONTEND: Frontend time: 0.512s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.009s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11488669 506.94764334 + layer.39.0 141.08750911 1561.08345481 + ------------------------------------------------------------------------------------- + TOTAL 70.60119790 1034.01554908 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 665680 +BPFP 0.6318 bits/point +EBPFP 0.6318 equivalent bits/point +MSE 1034.015549 +---------------------- -------------------------------------------------------- +Time: 1.572s Load: 0.050s, Pack+Encode: 0.512s, Decode+Unpack: 1.009s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1034.0155 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01924916-0.000644_jay _ jay_0.82223135.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01924916-0.000644_jay _ jay_0.82223135.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01944390-0.002567_American robin _ American robin_0.5629079.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01944390-0.002567_American robin _ American robin_0.5629079.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 241,904B, BPFP=0.4592 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 502,888B, BPFP=0.9545 +⌛️ [2/4] FRONTEND: Frontend time: 0.582s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.084s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10732387 198.81620505 + layer.39.0 16.74672581 1742.28255588 + ------------------------------------------------------------------------------------- + TOTAL 8.42702484 970.54938047 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 744792 +BPFP 0.7068 bits/point +EBPFP 0.7068 equivalent bits/point +MSE 970.549380 +---------------------- -------------------------------------------------------- +Time: 1.736s Load: 0.071s, Pack+Encode: 0.582s, Decode+Unpack: 1.084s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 970.5494 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01944390-0.002567_American robin _ American robin_0.5629079.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01944390-0.002567_American robin _ American robin_0.5629079.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01985128-0.001579_centipede _ centipede_0.85936093.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01985128-0.001579_centipede _ centipede_0.85936093.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.079s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 222,632B, BPFP=0.4226 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 383,420B, BPFP=0.7278 +⌛️ [2/4] FRONTEND: Frontend time: 0.587s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.048s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09645609 26.23759414 + layer.39.0 23.47999613 1641.12038387 + ------------------------------------------------------------------------------------- + TOTAL 11.78822611 833.67898901 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 606052 +BPFP 0.5752 bits/point +EBPFP 0.5752 equivalent bits/point +MSE 833.678989 +---------------------- -------------------------------------------------------- +Time: 1.715s Load: 0.079s, Pack+Encode: 0.587s, Decode+Unpack: 1.048s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 833.6790 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01985128-0.001579_centipede _ centipede_0.85936093.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01985128-0.001579_centipede _ centipede_0.85936093.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02037110-0.003503_snowmobile _ snowmobile_0.5200631.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02037110-0.003503_snowmobile _ snowmobile_0.5200631.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 157,912B, BPFP=0.2997 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 294,376B, BPFP=0.5587 +⌛️ [2/4] FRONTEND: Frontend time: 0.532s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.023s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09471867 24.96093940 + layer.39.0 17.04498261 1515.53875121 + ------------------------------------------------------------------------------------- + TOTAL 8.56985064 770.24984531 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 452288 +BPFP 0.4292 bits/point +EBPFP 0.4292 equivalent bits/point +MSE 770.249845 +---------------------- -------------------------------------------------------- +Time: 1.623s Load: 0.069s, Pack+Encode: 0.532s, Decode+Unpack: 1.023s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 770.2498 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02037110-0.003503_snowmobile _ snowmobile_0.5200631.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02037110-0.003503_snowmobile _ snowmobile_0.5200631.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02123394-0.015363_marmot _ marmot_0.82052565.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02123394-0.015363_marmot _ marmot_0.82052565.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 216,004B, BPFP=0.4100 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 458,464B, BPFP=0.8702 +⌛️ [2/4] FRONTEND: Frontend time: 0.593s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.060s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10209646 39.17798302 + layer.39.0 11.38238543 1846.43100097 + ------------------------------------------------------------------------------------- + TOTAL 5.74224095 942.80449200 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 674468 +BPFP 0.6401 bits/point +EBPFP 0.6401 equivalent bits/point +MSE 942.804492 +---------------------- -------------------------------------------------------- +Time: 1.733s Load: 0.080s, Pack+Encode: 0.593s, Decode+Unpack: 1.060s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 942.8045 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02123394-0.015363_marmot _ marmot_0.82052565.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02123394-0.015363_marmot _ marmot_0.82052565.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02165456-0.000157_corn _ corn_0.9868978.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02165456-0.000157_corn _ corn_0.9868978.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 251,896B, BPFP=0.4781 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 454,848B, BPFP=0.8633 +⌛️ [2/4] FRONTEND: Frontend time: 0.601s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.018s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10346756 259.94545675 + layer.39.0 776.17699223 2817.98032070 + ------------------------------------------------------------------------------------- + TOTAL 388.14022989 1538.96288873 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 706744 +BPFP 0.6707 bits/point +EBPFP 0.6707 equivalent bits/point +MSE 1538.962889 +---------------------- -------------------------------------------------------- +Time: 1.689s Load: 0.069s, Pack+Encode: 0.601s, Decode+Unpack: 1.018s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1538.9629 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02165456-0.000157_corn _ corn_0.9868978.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02165456-0.000157_corn _ corn_0.9868978.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02219486-0.000060_cliff _ cliff_0.99684334.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02219486-0.000060_cliff _ cliff_0.99684334.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 211,260B, BPFP=0.4010 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 330,252B, BPFP=0.6268 +⌛️ [2/4] FRONTEND: Frontend time: 0.505s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.004s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09584527 12.53152712 + layer.39.0 31.94620460 1404.98238581 + ------------------------------------------------------------------------------------- + TOTAL 16.02102494 708.75695647 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 541512 +BPFP 0.5139 bits/point +EBPFP 0.5139 equivalent bits/point +MSE 708.756956 +---------------------- -------------------------------------------------------- +Time: 1.571s Load: 0.062s, Pack+Encode: 0.505s, Decode+Unpack: 1.004s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 708.7570 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02219486-0.000060_cliff _ cliff_0.99684334.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02219486-0.000060_cliff _ cliff_0.99684334.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02226429-0.000085_stick insect _ stick insect_0.99900275.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02226429-0.000085_stick insect _ stick insect_0.99900275.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.079s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 223,496B, BPFP=0.4242 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 456,192B, BPFP=0.8659 +⌛️ [2/4] FRONTEND: Frontend time: 0.579s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.044s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09547379 25.25525389 + layer.39.0 19.16722850 2369.96938776 + ------------------------------------------------------------------------------------- + TOTAL 9.63135114 1197.61232082 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 679688 +BPFP 0.6451 bits/point +EBPFP 0.6451 equivalent bits/point +MSE 1197.612321 +---------------------- -------------------------------------------------------- +Time: 1.702s Load: 0.079s, Pack+Encode: 0.579s, Decode+Unpack: 1.044s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1197.6123 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02226429-0.000085_stick insect _ stick insect_0.99900275.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02226429-0.000085_stick insect _ stick insect_0.99900275.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02231487-0.000080_manhole cover _ manhole cover_0.7554716.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02231487-0.000080_manhole cover _ manhole cover_0.7554716.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.058s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 211,604B, BPFP=0.4016 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 492,376B, BPFP=0.9346 +⌛️ [2/4] FRONTEND: Frontend time: 0.595s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.063s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09512618 12.79666621 + layer.39.0 210.79875790 2265.84256560 + ------------------------------------------------------------------------------------- + TOTAL 105.44694204 1139.31961590 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 703980 +BPFP 0.6681 bits/point +EBPFP 0.6681 equivalent bits/point +MSE 1139.319616 +---------------------- -------------------------------------------------------- +Time: 1.716s Load: 0.058s, Pack+Encode: 0.595s, Decode+Unpack: 1.063s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1139.3196 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02231487-0.000080_manhole cover _ manhole cover_0.7554716.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02231487-0.000080_manhole cover _ manhole cover_0.7554716.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02233338-0.002901_manhole cover _ manhole cover_0.69458175.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02233338-0.002901_manhole cover _ manhole cover_0.69458175.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 199,392B, BPFP=0.3785 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 438,056B, BPFP=0.8315 +⌛️ [2/4] FRONTEND: Frontend time: 0.567s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.059s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09539769 12.75275506 + layer.39.0 58.97704841 1552.93828960 + ------------------------------------------------------------------------------------- + TOTAL 29.53622305 782.84552233 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 637448 +BPFP 0.6050 bits/point +EBPFP 0.6050 equivalent bits/point +MSE 782.845522 +---------------------- -------------------------------------------------------- +Time: 1.697s Load: 0.071s, Pack+Encode: 0.567s, Decode+Unpack: 1.059s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 782.8455 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02233338-0.002901_manhole cover _ manhole cover_0.69458175.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02233338-0.002901_manhole cover _ manhole cover_0.69458175.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02236044-0.000522_sundial _ sundial_0.96381366.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02236044-0.000522_sundial _ sundial_0.96381366.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 250,308B, BPFP=0.4751 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 461,896B, BPFP=0.8767 +⌛️ [2/4] FRONTEND: Frontend time: 0.567s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.067s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09795647 113.29771775 + layer.39.0 53.12385356 2217.67711370 + ------------------------------------------------------------------------------------- + TOTAL 26.61090502 1165.48741573 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 712204 +BPFP 0.6759 bits/point +EBPFP 0.6759 equivalent bits/point +MSE 1165.487416 +---------------------- -------------------------------------------------------- +Time: 1.705s Load: 0.071s, Pack+Encode: 0.567s, Decode+Unpack: 1.067s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1165.4874 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02236044-0.000522_sundial _ sundial_0.96381366.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02236044-0.000522_sundial _ sundial_0.96381366.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02259212-0.000032_chain _ chain_0.6590295.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02259212-0.000032_chain _ chain_0.6590295.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 220,728B, BPFP=0.4190 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 427,400B, BPFP=0.8112 +⌛️ [2/4] FRONTEND: Frontend time: 0.527s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.996s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09523673 13.02478609 + layer.39.0 80.66082058 2520.10228377 + ------------------------------------------------------------------------------------- + TOTAL 40.37802865 1266.56353493 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 648128 +BPFP 0.6151 bits/point +EBPFP 0.6151 equivalent bits/point +MSE 1266.563535 +---------------------- -------------------------------------------------------- +Time: 1.575s Load: 0.052s, Pack+Encode: 0.527s, Decode+Unpack: 0.996s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1266.5635 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02259212-0.000032_chain _ chain_0.6590295.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02259212-0.000032_chain _ chain_0.6590295.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02279972-0.000576_apron _ apron_0.7661352.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02279972-0.000576_apron _ apron_0.7661352.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 244,436B, BPFP=0.4640 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 354,080B, BPFP=0.6721 +⌛️ [2/4] FRONTEND: Frontend time: 0.527s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.014s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12772729 341.71993440 + layer.39.0 1038.59135083 2738.81729835 + ------------------------------------------------------------------------------------- + TOTAL 519.35953906 1540.26861638 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 598516 +BPFP 0.5680 bits/point +EBPFP 0.5680 equivalent bits/point +MSE 1540.268616 +---------------------- -------------------------------------------------------- +Time: 1.603s Load: 0.061s, Pack+Encode: 0.527s, Decode+Unpack: 1.014s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1540.2686 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02279972-0.000576_apron _ apron_0.7661352.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02279972-0.000576_apron _ apron_0.7661352.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02280649-0.001599_custard apple _ leafhopper_0.9128452.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02280649-0.001599_custard apple _ leafhopper_0.9128452.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 201,184B, BPFP=0.3819 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 400,824B, BPFP=0.7608 +⌛️ [2/4] FRONTEND: Frontend time: 0.552s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.064s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09488542 13.17074944 + layer.39.0 1031.59973275 3369.33819242 + ------------------------------------------------------------------------------------- + TOTAL 515.84730909 1691.25447093 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 602008 +BPFP 0.5713 bits/point +EBPFP 0.5713 equivalent bits/point +MSE 1691.254471 +---------------------- -------------------------------------------------------- +Time: 1.667s Load: 0.051s, Pack+Encode: 0.552s, Decode+Unpack: 1.064s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1691.2545 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02280649-0.001599_custard apple _ leafhopper_0.9128452.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02280649-0.001599_custard apple _ leafhopper_0.9128452.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02317335-0.033681_flatworm _ flatworm_0.6070924.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02317335-0.033681_flatworm _ flatworm_0.6070924.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 204,132B, BPFP=0.3875 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 337,464B, BPFP=0.6405 +⌛️ [2/4] FRONTEND: Frontend time: 0.588s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.056s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09575805 13.19293895 + layer.39.0 62.35741238 1998.33381924 + ------------------------------------------------------------------------------------- + TOTAL 31.22658522 1005.76337910 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 541596 +BPFP 0.5140 bits/point +EBPFP 0.5140 equivalent bits/point +MSE 1005.763379 +---------------------- -------------------------------------------------------- +Time: 1.715s Load: 0.071s, Pack+Encode: 0.588s, Decode+Unpack: 1.056s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1005.7634 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02317335-0.033681_flatworm _ flatworm_0.6070924.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02317335-0.033681_flatworm _ flatworm_0.6070924.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02325366-0.000350_flatworm _ flatworm_0.87634724.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02325366-0.000350_flatworm _ flatworm_0.87634724.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 167,516B, BPFP=0.3180 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 350,632B, BPFP=0.6655 +⌛️ [2/4] FRONTEND: Frontend time: 0.589s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.048s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09712043 12.93152674 + layer.39.0 30.59439155 1561.38083090 + ------------------------------------------------------------------------------------- + TOTAL 15.34575599 787.15617882 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 518148 +BPFP 0.4917 bits/point +EBPFP 0.4917 equivalent bits/point +MSE 787.156179 +---------------------- -------------------------------------------------------- +Time: 1.696s Load: 0.059s, Pack+Encode: 0.589s, Decode+Unpack: 1.048s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 787.1562 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02325366-0.000350_flatworm _ flatworm_0.87634724.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02325366-0.000350_flatworm _ flatworm_0.87634724.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02346627-0.011107_fountain _ skunk_0.28641737.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02346627-0.011107_fountain _ skunk_0.28641737.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.079s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 166,000B, BPFP=0.3151 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 264,544B, BPFP=0.5021 +⌛️ [2/4] FRONTEND: Frontend time: 0.582s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.056s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09705289 12.69905552 + layer.39.0 9.52721088 1323.63714772 + ------------------------------------------------------------------------------------- + TOTAL 4.81213189 668.16810162 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 430544 +BPFP 0.4086 bits/point +EBPFP 0.4086 equivalent bits/point +MSE 668.168102 +---------------------- -------------------------------------------------------- +Time: 1.718s Load: 0.079s, Pack+Encode: 0.582s, Decode+Unpack: 1.056s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 668.1681 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02346627-0.011107_fountain _ skunk_0.28641737.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02346627-0.011107_fountain _ skunk_0.28641737.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02445715-0.036432_shovel _ tarantula_0.26785344.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02445715-0.036432_shovel _ tarantula_0.26785344.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 240,300B, BPFP=0.4561 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 303,244B, BPFP=0.5756 +⌛️ [2/4] FRONTEND: Frontend time: 0.557s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.057s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09708806 137.12182641 + layer.39.0 8.00606437 1290.72776968 + ------------------------------------------------------------------------------------- + TOTAL 4.05157622 713.92479804 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 543544 +BPFP 0.5158 bits/point +EBPFP 0.5158 equivalent bits/point +MSE 713.924798 +---------------------- -------------------------------------------------------- +Time: 1.665s Load: 0.052s, Pack+Encode: 0.557s, Decode+Unpack: 1.057s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 713.9248 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02445715-0.036432_shovel _ tarantula_0.26785344.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02445715-0.036432_shovel _ tarantula_0.26785344.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02454379-0.082010_koala _ koala_0.7052893.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02454379-0.082010_koala _ koala_0.7052893.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.079s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 337,044B, BPFP=0.6397 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 402,792B, BPFP=0.7645 +⌛️ [2/4] FRONTEND: Frontend time: 0.583s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.064s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14585212 764.23748785 + layer.39.0 44.19989826 1680.42638484 + ------------------------------------------------------------------------------------- + TOTAL 22.17287519 1222.33193635 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 739836 +BPFP 0.7021 bits/point +EBPFP 0.7021 equivalent bits/point +MSE 1222.331936 +---------------------- -------------------------------------------------------- +Time: 1.726s Load: 0.079s, Pack+Encode: 0.583s, Decode+Unpack: 1.064s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1222.3319 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02454379-0.082010_koala _ koala_0.7052893.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02454379-0.082010_koala _ koala_0.7052893.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02782093-0.007542_American alligator _ American alligator_0.49872985.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02782093-0.007542_American alligator _ American alligator_0.49872985.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 185,700B, BPFP=0.3525 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 421,392B, BPFP=0.7998 +⌛️ [2/4] FRONTEND: Frontend time: 0.548s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.021s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09848133 12.79321930 + layer.39.0 9.18780844 1536.48347911 + ------------------------------------------------------------------------------------- + TOTAL 4.64314488 774.63834920 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 607092 +BPFP 0.5762 bits/point +EBPFP 0.5762 equivalent bits/point +MSE 774.638349 +---------------------- -------------------------------------------------------- +Time: 1.629s Load: 0.060s, Pack+Encode: 0.548s, Decode+Unpack: 1.021s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 774.6383 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02782093-0.007542_American alligator _ American alligator_0.49872985.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02782093-0.007542_American alligator _ American alligator_0.49872985.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02787622-0.004599_marimba _ accordion_0.25991488.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02787622-0.004599_marimba _ accordion_0.25991488.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 254,708B, BPFP=0.4835 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 409,652B, BPFP=0.7776 +⌛️ [2/4] FRONTEND: Frontend time: 0.542s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.066s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12856446 235.82935496 + layer.39.0 1004.59450923 3605.12390671 + ------------------------------------------------------------------------------------- + TOTAL 502.36153685 1920.47663083 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 664360 +BPFP 0.6305 bits/point +EBPFP 0.6305 equivalent bits/point +MSE 1920.476631 +---------------------- -------------------------------------------------------- +Time: 1.660s Load: 0.051s, Pack+Encode: 0.542s, Decode+Unpack: 1.066s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1920.4766 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02787622-0.004599_marimba _ accordion_0.25991488.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02787622-0.004599_marimba _ accordion_0.25991488.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02793495-0.000130_flatworm _ stingray_0.5528234.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02793495-0.000130_flatworm _ stingray_0.5528234.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 179,472B, BPFP=0.3407 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 263,224B, BPFP=0.4996 +⌛️ [2/4] FRONTEND: Frontend time: 0.565s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.048s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09706621 38.41518616 + layer.39.0 8.05872662 1453.55102041 + ------------------------------------------------------------------------------------- + TOTAL 4.07789641 745.98310329 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 442696 +BPFP 0.4201 bits/point +EBPFP 0.4201 equivalent bits/point +MSE 745.983103 +---------------------- -------------------------------------------------------- +Time: 1.684s Load: 0.071s, Pack+Encode: 0.565s, Decode+Unpack: 1.048s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 745.9831 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02793495-0.000130_flatworm _ stingray_0.5528234.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02793495-0.000130_flatworm _ stingray_0.5528234.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02797295-0.002775_hair dryer _ box turtle_0.2407761.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02797295-0.002775_hair dryer _ box turtle_0.2407761.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 256,488B, BPFP=0.4868 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 477,932B, BPFP=0.9072 +⌛️ [2/4] FRONTEND: Frontend time: 0.504s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.003s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11161610 171.65922619 + layer.39.0 373.09438776 2813.13265306 + ------------------------------------------------------------------------------------- + TOTAL 186.60300193 1492.39593963 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 734420 +BPFP 0.6970 bits/point +EBPFP 0.6970 equivalent bits/point +MSE 1492.395940 +---------------------- -------------------------------------------------------- +Time: 1.558s Load: 0.052s, Pack+Encode: 0.504s, Decode+Unpack: 1.003s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1492.3959 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02797295-0.002775_hair dryer _ box turtle_0.2407761.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02797295-0.002775_hair dryer _ box turtle_0.2407761.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02814860-0.006340_fountain _ fountain_0.7891514.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02814860-0.006340_fountain _ fountain_0.7891514.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 160,668B, BPFP=0.3050 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 342,112B, BPFP=0.6494 +⌛️ [2/4] FRONTEND: Frontend time: 0.520s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.016s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.04615183 13.01203288 + layer.39.0 7.48662090 1526.97521866 + ------------------------------------------------------------------------------------- + TOTAL 7.76638637 769.99362577 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 502780 +BPFP 0.4772 bits/point +EBPFP 0.4772 equivalent bits/point +MSE 769.993626 +---------------------- -------------------------------------------------------- +Time: 1.586s Load: 0.050s, Pack+Encode: 0.520s, Decode+Unpack: 1.016s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 769.9936 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02814860-0.006340_fountain _ fountain_0.7891514.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02814860-0.006340_fountain _ fountain_0.7891514.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02879718-0.003578_maraca _ maraca_0.6809677.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02879718-0.003578_maraca _ maraca_0.6809677.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.081s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 226,792B, BPFP=0.4305 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 441,676B, BPFP=0.8383 +⌛️ [2/4] FRONTEND: Frontend time: 0.586s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.060s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10989876 137.11273081 + layer.39.0 33.03751367 2317.18780369 + ------------------------------------------------------------------------------------- + TOTAL 16.57370621 1227.15026725 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 668468 +BPFP 0.6344 bits/point +EBPFP 0.6344 equivalent bits/point +MSE 1227.150267 +---------------------- -------------------------------------------------------- +Time: 1.727s Load: 0.081s, Pack+Encode: 0.586s, Decode+Unpack: 1.060s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1227.1503 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02879718-0.003578_maraca _ maraca_0.6809677.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02879718-0.003578_maraca _ maraca_0.6809677.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02883205-0.000262_syringe _ syringe_0.7098205.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02883205-0.000262_syringe _ syringe_0.7098205.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.081s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 199,432B, BPFP=0.3785 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 415,472B, BPFP=0.7886 +⌛️ [2/4] FRONTEND: Frontend time: 0.598s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.049s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09610580 12.61536724 + layer.39.0 8.14318931 1516.65051020 + ------------------------------------------------------------------------------------- + TOTAL 4.11964755 764.63293872 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 614904 +BPFP 0.5836 bits/point +EBPFP 0.5836 equivalent bits/point +MSE 764.632939 +---------------------- -------------------------------------------------------- +Time: 1.728s Load: 0.081s, Pack+Encode: 0.598s, Decode+Unpack: 1.049s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 764.6329 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02883205-0.000262_syringe _ syringe_0.7098205.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02883205-0.000262_syringe _ syringe_0.7098205.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02906734-0.000163_stethoscope _ stethoscope_0.9290994.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02906734-0.000163_stethoscope _ stethoscope_0.9290994.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 237,428B, BPFP=0.4507 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 435,624B, BPFP=0.8268 +⌛️ [2/4] FRONTEND: Frontend time: 0.520s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.052s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12024398 209.10339225 + layer.39.0 47.23105336 2279.63508260 + ------------------------------------------------------------------------------------- + TOTAL 23.67564867 1244.36923743 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 673052 +BPFP 0.6388 bits/point +EBPFP 0.6388 equivalent bits/point +MSE 1244.369237 +---------------------- -------------------------------------------------------- +Time: 1.622s Load: 0.050s, Pack+Encode: 0.520s, Decode+Unpack: 1.052s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1244.3692 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02906734-0.000163_stethoscope _ stethoscope_0.9290994.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02906734-0.000163_stethoscope _ stethoscope_0.9290994.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02948072-0.002303_digital clock _ digital clock_0.928374.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02948072-0.002303_digital clock _ digital clock_0.928374.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 203,588B, BPFP=0.3864 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 480,980B, BPFP=0.9129 +⌛️ [2/4] FRONTEND: Frontend time: 0.527s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.055s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09670976 12.94789863 + layer.39.0 81.62974520 2819.20238095 + ------------------------------------------------------------------------------------- + TOTAL 40.86322748 1416.07513979 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 684568 +BPFP 0.6497 bits/point +EBPFP 0.6497 equivalent bits/point +MSE 1416.075140 +---------------------- -------------------------------------------------------- +Time: 1.644s Load: 0.062s, Pack+Encode: 0.527s, Decode+Unpack: 1.055s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1416.0751 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02948072-0.002303_digital clock _ digital clock_0.928374.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02948072-0.002303_digital clock _ digital clock_0.928374.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02999410-0.000148_chest _ chest_0.9948565.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02999410-0.000148_chest _ chest_0.9948565.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 201,300B, BPFP=0.3821 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 380,040B, BPFP=0.7213 +⌛️ [2/4] FRONTEND: Frontend time: 0.509s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.075s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10256943 37.73502794 + layer.39.0 13.72598738 1471.62670068 + ------------------------------------------------------------------------------------- + TOTAL 6.91427841 754.68086431 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 581340 +BPFP 0.5517 bits/point +EBPFP 0.5517 equivalent bits/point +MSE 754.680864 +---------------------- -------------------------------------------------------- +Time: 1.635s Load: 0.052s, Pack+Encode: 0.509s, Decode+Unpack: 1.075s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 754.6809 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02999410-0.000148_chest _ chest_0.9948565.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02999410-0.000148_chest _ chest_0.9948565.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03026506-0.001828_basketball _ basketball_0.6904969.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03026506-0.001828_basketball _ basketball_0.6904969.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 194,328B, BPFP=0.3689 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 449,980B, BPFP=0.8541 +⌛️ [2/4] FRONTEND: Frontend time: 0.579s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.063s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09484169 13.09758412 + layer.39.0 87.31533194 1957.12864431 + ------------------------------------------------------------------------------------- + TOTAL 43.70508681 985.11311422 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 644308 +BPFP 0.6115 bits/point +EBPFP 0.6115 equivalent bits/point +MSE 985.113114 +---------------------- -------------------------------------------------------- +Time: 1.703s Load: 0.062s, Pack+Encode: 0.579s, Decode+Unpack: 1.063s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 985.1131 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03026506-0.001828_basketball _ basketball_0.6904969.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03026506-0.001828_basketball _ basketball_0.6904969.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03124043-0.001154_bow tie _ bow tie_0.9622156.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03124043-0.001154_bow tie _ bow tie_0.9622156.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 198,936B, BPFP=0.3776 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 436,364B, BPFP=0.8283 +⌛️ [2/4] FRONTEND: Frontend time: 0.546s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.006s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09893820 98.48547589 + layer.39.0 13.24554141 2073.34062196 + ------------------------------------------------------------------------------------- + TOTAL 6.67223981 1085.91304892 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 635300 +BPFP 0.6029 bits/point +EBPFP 0.6029 equivalent bits/point +MSE 1085.913049 +---------------------- -------------------------------------------------------- +Time: 1.603s Load: 0.051s, Pack+Encode: 0.546s, Decode+Unpack: 1.006s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1085.9130 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03124043-0.001154_bow tie _ bow tie_0.9622156.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03124043-0.001154_bow tie _ bow tie_0.9622156.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03196217-0.015958_soap dispenser _ envelope_0.80274254.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03196217-0.015958_soap dispenser _ envelope_0.80274254.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.081s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 179,980B, BPFP=0.3416 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 381,648B, BPFP=0.7244 +⌛️ [2/4] FRONTEND: Frontend time: 0.585s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.048s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10340443 12.75699727 + layer.39.0 8.70910111 1904.90634111 + ------------------------------------------------------------------------------------- + TOTAL 4.40625277 958.83166919 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 561628 +BPFP 0.5330 bits/point +EBPFP 0.5330 equivalent bits/point +MSE 958.831669 +---------------------- -------------------------------------------------------- +Time: 1.714s Load: 0.081s, Pack+Encode: 0.585s, Decode+Unpack: 1.048s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 958.8317 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03196217-0.015958_soap dispenser _ envelope_0.80274254.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03196217-0.015958_soap dispenser _ envelope_0.80274254.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03223299-0.001528_hot dog _ hot dog_0.9147216.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03223299-0.001528_hot dog _ hot dog_0.9147216.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 227,012B, BPFP=0.4309 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 403,708B, BPFP=0.7663 +⌛️ [2/4] FRONTEND: Frontend time: 0.582s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.059s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10130972 61.21767189 + layer.39.0 352.09596696 2175.06559767 + ------------------------------------------------------------------------------------- + TOTAL 176.09863834 1118.14163478 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 630720 +BPFP 0.5986 bits/point +EBPFP 0.5986 equivalent bits/point +MSE 1118.141635 +---------------------- -------------------------------------------------------- +Time: 1.713s Load: 0.072s, Pack+Encode: 0.582s, Decode+Unpack: 1.059s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1118.1416 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03223299-0.001528_hot dog _ hot dog_0.9147216.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03223299-0.001528_hot dog _ hot dog_0.9147216.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03255030-0.005469_bubble _ bubble_0.9381716.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03255030-0.005469_bubble _ bubble_0.9381716.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 214,672B, BPFP=0.4075 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 471,148B, BPFP=0.8943 +⌛️ [2/4] FRONTEND: Frontend time: 0.563s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.989s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09675161 37.28360362 + layer.39.0 42.23478499 2171.50413022 + ------------------------------------------------------------------------------------- + TOTAL 21.16576830 1104.39386692 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 685820 +BPFP 0.6509 bits/point +EBPFP 0.6509 equivalent bits/point +MSE 1104.393867 +---------------------- -------------------------------------------------------- +Time: 1.611s Load: 0.059s, Pack+Encode: 0.563s, Decode+Unpack: 0.989s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1104.3939 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03255030-0.005469_bubble _ bubble_0.9381716.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03255030-0.005469_bubble _ bubble_0.9381716.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03325584-0.000773_candle _ candle_0.810919.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03325584-0.000773_candle _ candle_0.810919.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 238,820B, BPFP=0.4533 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 410,356B, BPFP=0.7789 +⌛️ [2/4] FRONTEND: Frontend time: 0.601s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.061s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10394677 124.57265094 + layer.39.0 140.58187561 3319.97983479 + ------------------------------------------------------------------------------------- + TOTAL 70.34291119 1722.27624286 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 649176 +BPFP 0.6161 bits/point +EBPFP 0.6161 equivalent bits/point +MSE 1722.276243 +---------------------- -------------------------------------------------------- +Time: 1.742s Load: 0.080s, Pack+Encode: 0.601s, Decode+Unpack: 1.061s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1722.2762 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03325584-0.000773_candle _ candle_0.810919.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03325584-0.000773_candle _ candle_0.810919.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03355925-0.004997_spider web _ spider web_0.9142101.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03355925-0.004997_spider web _ spider web_0.9142101.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 158,064B, BPFP=0.3000 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 268,996B, BPFP=0.5106 +⌛️ [2/4] FRONTEND: Frontend time: 0.580s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.065s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09873271 13.02130121 + layer.39.0 6.60211199 1335.39601555 + ------------------------------------------------------------------------------------- + TOTAL 3.35042235 674.20865838 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 427060 +BPFP 0.4053 bits/point +EBPFP 0.4053 equivalent bits/point +MSE 674.208658 +---------------------- -------------------------------------------------------- +Time: 1.714s Load: 0.070s, Pack+Encode: 0.580s, Decode+Unpack: 1.065s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 674.2087 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03355925-0.004997_spider web _ spider web_0.9142101.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03355925-0.004997_spider web _ spider web_0.9142101.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03384352-0.002273_viaduct _ viaduct_0.6161024.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03384352-0.002273_viaduct _ viaduct_0.6161024.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.081s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 210,896B, BPFP=0.4003 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 422,972B, BPFP=0.8028 +⌛️ [2/4] FRONTEND: Frontend time: 0.593s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.053s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09647940 88.09129009 + layer.39.0 175.50411504 2120.32555879 + ------------------------------------------------------------------------------------- + TOTAL 87.80029722 1104.20842444 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 633868 +BPFP 0.6016 bits/point +EBPFP 0.6016 equivalent bits/point +MSE 1104.208424 +---------------------- -------------------------------------------------------- +Time: 1.727s Load: 0.081s, Pack+Encode: 0.593s, Decode+Unpack: 1.053s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1104.2084 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03384352-0.002273_viaduct _ viaduct_0.6161024.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03384352-0.002273_viaduct _ viaduct_0.6161024.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03388043-0.005154_candle _ candle_0.9636924.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03388043-0.005154_candle _ candle_0.9636924.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 202,200B, BPFP=0.3838 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 480,172B, BPFP=0.9114 +⌛️ [2/4] FRONTEND: Frontend time: 0.584s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.056s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09640297 12.77429638 + layer.39.0 7.87377147 1640.19861516 + ------------------------------------------------------------------------------------- + TOTAL 3.98508722 826.48645577 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 682372 +BPFP 0.6476 bits/point +EBPFP 0.6476 equivalent bits/point +MSE 826.486456 +---------------------- -------------------------------------------------------- +Time: 1.720s Load: 0.080s, Pack+Encode: 0.584s, Decode+Unpack: 1.056s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 826.4865 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03388043-0.005154_candle _ candle_0.9636924.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03388043-0.005154_candle _ candle_0.9636924.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03417042-0.001187_tank _ tank_0.70379025.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03417042-0.001187_tank _ tank_0.70379025.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 191,028B, BPFP=0.3626 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 361,352B, BPFP=0.6859 +⌛️ [2/4] FRONTEND: Frontend time: 0.589s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.044s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09848782 12.78673640 + layer.39.0 16.63742104 1998.05272109 + ------------------------------------------------------------------------------------- + TOTAL 8.36795443 1005.41972875 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 552380 +BPFP 0.5242 bits/point +EBPFP 0.5242 equivalent bits/point +MSE 1005.419729 +---------------------- -------------------------------------------------------- +Time: 1.684s Load: 0.050s, Pack+Encode: 0.589s, Decode+Unpack: 1.044s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1005.4197 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03417042-0.001187_tank _ tank_0.70379025.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03417042-0.001187_tank _ tank_0.70379025.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03444034-0.002100_maraca _ maraca_0.502369.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03444034-0.002100_maraca _ maraca_0.502369.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.081s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 256,420B, BPFP=0.4867 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 487,368B, BPFP=0.9251 +⌛️ [2/4] FRONTEND: Frontend time: 0.592s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.070s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11197850 294.71711006 + layer.39.0 347.54634354 2880.87706511 + ------------------------------------------------------------------------------------- + TOTAL 173.82916102 1587.79708759 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 743788 +BPFP 0.7059 bits/point +EBPFP 0.7059 equivalent bits/point +MSE 1587.797088 +---------------------- -------------------------------------------------------- +Time: 1.743s Load: 0.081s, Pack+Encode: 0.592s, Decode+Unpack: 1.070s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1587.7971 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03444034-0.002100_maraca _ maraca_0.502369.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03444034-0.002100_maraca _ maraca_0.502369.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03445924-0.003505_baseball player _ baseball player_0.6723684.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03445924-0.003505_baseball player _ baseball player_0.6723684.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.058s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 200,084B, BPFP=0.3798 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 411,776B, BPFP=0.7816 +⌛️ [2/4] FRONTEND: Frontend time: 0.535s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.047s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09665277 12.57183951 + layer.39.0 26.28463618 1984.45821186 + ------------------------------------------------------------------------------------- + TOTAL 13.19064447 998.51502568 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 611860 +BPFP 0.5807 bits/point +EBPFP 0.5807 equivalent bits/point +MSE 998.515026 +---------------------- -------------------------------------------------------- +Time: 1.640s Load: 0.058s, Pack+Encode: 0.535s, Decode+Unpack: 1.047s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 998.5150 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03445924-0.003505_baseball player _ baseball player_0.6723684.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03445924-0.003505_baseball player _ baseball player_0.6723684.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03452741-0.002771_chain _ chain_0.9575044.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03452741-0.002771_chain _ chain_0.9575044.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 241,408B, BPFP=0.4582 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 475,220B, BPFP=0.9020 +⌛️ [2/4] FRONTEND: Frontend time: 0.515s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.005s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12351380 124.55464954 + layer.39.0 42.82565370 2283.15208941 + ------------------------------------------------------------------------------------- + TOTAL 21.47458375 1203.85336947 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 716628 +BPFP 0.6801 bits/point +EBPFP 0.6801 equivalent bits/point +MSE 1203.853369 +---------------------- -------------------------------------------------------- +Time: 1.572s Load: 0.052s, Pack+Encode: 0.515s, Decode+Unpack: 1.005s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1203.8534 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03452741-0.002771_chain _ chain_0.9575044.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03452741-0.002771_chain _ chain_0.9575044.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03483316-0.004974_lighter _ lighter_0.27796906.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03483316-0.004974_lighter _ lighter_0.27796906.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 270,596B, BPFP=0.5136 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 421,100B, BPFP=0.7993 +⌛️ [2/4] FRONTEND: Frontend time: 0.552s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.064s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12993333 296.25728863 + layer.39.0 87.07173986 1978.65233236 + ------------------------------------------------------------------------------------- + TOTAL 43.60083660 1137.45481050 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 691696 +BPFP 0.6564 bits/point +EBPFP 0.6564 equivalent bits/point +MSE 1137.454810 +---------------------- -------------------------------------------------------- +Time: 1.665s Load: 0.050s, Pack+Encode: 0.552s, Decode+Unpack: 1.064s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1137.4548 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03483316-0.004974_lighter _ lighter_0.27796906.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03483316-0.004974_lighter _ lighter_0.27796906.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03584829-0.104805_golf cart _ golf cart_0.73568964.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03584829-0.104805_golf cart _ golf cart_0.73568964.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 224,640B, BPFP=0.4264 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 364,048B, BPFP=0.6910 +⌛️ [2/4] FRONTEND: Frontend time: 0.566s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.063s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09917131 25.57221438 + layer.39.0 24.34873246 2336.65354713 + ------------------------------------------------------------------------------------- + TOTAL 12.22395189 1181.11288075 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 588688 +BPFP 0.5587 bits/point +EBPFP 0.5587 equivalent bits/point +MSE 1181.112881 +---------------------- -------------------------------------------------------- +Time: 1.679s Load: 0.050s, Pack+Encode: 0.566s, Decode+Unpack: 1.063s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1181.1129 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03584829-0.104805_golf cart _ golf cart_0.73568964.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03584829-0.104805_golf cart _ golf cart_0.73568964.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03590841-0.001115_rocking chair _ rocking chair_0.2192492.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03590841-0.001115_rocking chair _ rocking chair_0.2192492.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 224,188B, BPFP=0.4255 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 339,020B, BPFP=0.6435 +⌛️ [2/4] FRONTEND: Frontend time: 0.504s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.985s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11329899 174.94337646 + layer.39.0 19.97532495 1808.68841108 + ------------------------------------------------------------------------------------- + TOTAL 10.04431197 991.81589377 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 563208 +BPFP 0.5345 bits/point +EBPFP 0.5345 equivalent bits/point +MSE 991.815894 +---------------------- -------------------------------------------------------- +Time: 1.539s Load: 0.050s, Pack+Encode: 0.504s, Decode+Unpack: 0.985s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 991.8159 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03590841-0.001115_rocking chair _ rocking chair_0.2192492.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03590841-0.001115_rocking chair _ rocking chair_0.2192492.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03617480-0.003238_basketball _ basketball_0.67568874.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03617480-0.003238_basketball _ basketball_0.67568874.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.079s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 283,916B, BPFP=0.5389 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 505,620B, BPFP=0.9597 +⌛️ [2/4] FRONTEND: Frontend time: 0.587s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.072s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12967051 261.32516399 + layer.39.0 57.10576865 2857.58163265 + ------------------------------------------------------------------------------------- + TOTAL 28.61771958 1559.45339832 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 789536 +BPFP 0.7493 bits/point +EBPFP 0.7493 equivalent bits/point +MSE 1559.453398 +---------------------- -------------------------------------------------------- +Time: 1.738s Load: 0.079s, Pack+Encode: 0.587s, Decode+Unpack: 1.072s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1559.4534 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03617480-0.003238_basketball _ basketball_0.67568874.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03617480-0.003238_basketball _ basketball_0.67568874.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03666591-0.004622_torch _ torch_0.99906796.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03666591-0.004622_torch _ torch_0.99906796.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 173,524B, BPFP=0.3294 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 396,744B, BPFP=0.7531 +⌛️ [2/4] FRONTEND: Frontend time: 0.605s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.016s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.05477861 12.96589244 + layer.39.0 7.78975672 1462.55405734 + ------------------------------------------------------------------------------------- + TOTAL 7.92226767 737.75997489 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 570268 +BPFP 0.5412 bits/point +EBPFP 0.5412 equivalent bits/point +MSE 737.759975 +---------------------- -------------------------------------------------------- +Time: 1.702s Load: 0.080s, Pack+Encode: 0.605s, Decode+Unpack: 1.016s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 737.7600 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03666591-0.004622_torch _ torch_0.99906796.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03666591-0.004622_torch _ torch_0.99906796.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03670208-0.003899_hair dryer _ grand piano_0.7359066.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03670208-0.003899_hair dryer _ grand piano_0.7359066.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 221,792B, BPFP=0.4210 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 441,532B, BPFP=0.8381 +⌛️ [2/4] FRONTEND: Frontend time: 0.551s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.055s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11232473 146.60419704 + layer.39.0 36.60432231 2242.33187561 + ------------------------------------------------------------------------------------- + TOTAL 18.35832352 1194.46803632 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 663324 +BPFP 0.6295 bits/point +EBPFP 0.6295 equivalent bits/point +MSE 1194.468036 +---------------------- -------------------------------------------------------- +Time: 1.657s Load: 0.052s, Pack+Encode: 0.551s, Decode+Unpack: 1.055s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1194.4680 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03670208-0.003899_hair dryer _ grand piano_0.7359066.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03670208-0.003899_hair dryer _ grand piano_0.7359066.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03717622-0.001175_sundial _ sundial_0.9998197.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03717622-0.001175_sundial _ sundial_0.9998197.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 260,540B, BPFP=0.4945 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 511,896B, BPFP=0.9716 +⌛️ [2/4] FRONTEND: Frontend time: 0.592s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.006s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13381931 295.52459913 + layer.39.0 773.52204810 3126.62487852 + ------------------------------------------------------------------------------------- + TOTAL 386.82793371 1711.07473882 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 772436 +BPFP 0.7331 bits/point +EBPFP 0.7331 equivalent bits/point +MSE 1711.074739 +---------------------- -------------------------------------------------------- +Time: 1.669s Load: 0.070s, Pack+Encode: 0.592s, Decode+Unpack: 1.006s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1711.0747 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03717622-0.001175_sundial _ sundial_0.9998197.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03717622-0.001175_sundial _ sundial_0.9998197.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03720891-0.001854_African bush elephant _ African bush elephant_0.722115.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03720891-0.001854_African bush elephant _ African bush elephant_0.722115.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 216,400B, BPFP=0.4107 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 445,404B, BPFP=0.8454 +⌛️ [2/4] FRONTEND: Frontend time: 0.532s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.061s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09642763 12.97638598 + layer.39.0 155.23232507 2817.01506317 + ------------------------------------------------------------------------------------- + TOTAL 77.66437635 1414.99572457 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 661804 +BPFP 0.6281 bits/point +EBPFP 0.6281 equivalent bits/point +MSE 1414.995725 +---------------------- -------------------------------------------------------- +Time: 1.654s Load: 0.061s, Pack+Encode: 0.532s, Decode+Unpack: 1.061s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1414.9957 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03720891-0.001854_African bush elephant _ African bush elephant_0.722115.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03720891-0.001854_African bush elephant _ African bush elephant_0.722115.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03721384-0.003327_chain _ chain_0.5599652.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03721384-0.003327_chain _ chain_0.5599652.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.079s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 188,208B, BPFP=0.3572 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 309,168B, BPFP=0.5868 +⌛️ [2/4] FRONTEND: Frontend time: 0.590s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.060s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09561452 24.72773931 + layer.39.0 742.66502672 2632.06341108 + ------------------------------------------------------------------------------------- + TOTAL 371.38032062 1328.39557519 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 497376 +BPFP 0.4720 bits/point +EBPFP 0.4720 equivalent bits/point +MSE 1328.395575 +---------------------- -------------------------------------------------------- +Time: 1.730s Load: 0.079s, Pack+Encode: 0.590s, Decode+Unpack: 1.060s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1328.3956 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03721384-0.003327_chain _ chain_0.5599652.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03721384-0.003327_chain _ chain_0.5599652.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03724870-0.001366_Christmas stocking _ Christmas stocking_0.5709616.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03724870-0.001366_Christmas stocking _ Christmas stocking_0.5709616.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 225,732B, BPFP=0.4285 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 388,564B, BPFP=0.7375 +⌛️ [2/4] FRONTEND: Frontend time: 0.559s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.062s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10329660 62.55971362 + layer.39.0 513.92243683 2517.42541302 + ------------------------------------------------------------------------------------- + TOTAL 257.01286671 1289.99256332 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 614296 +BPFP 0.5830 bits/point +EBPFP 0.5830 equivalent bits/point +MSE 1289.992563 +---------------------- -------------------------------------------------------- +Time: 1.672s Load: 0.051s, Pack+Encode: 0.559s, Decode+Unpack: 1.062s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1289.9926 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03724870-0.001366_Christmas stocking _ Christmas stocking_0.5709616.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03724870-0.001366_Christmas stocking _ Christmas stocking_0.5709616.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03775071-0.000145_Christmas stocking _ Christmas stocking_0.9986047.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03775071-0.000145_Christmas stocking _ Christmas stocking_0.9986047.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.081s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 219,080B, BPFP=0.4158 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 476,576B, BPFP=0.9046 +⌛️ [2/4] FRONTEND: Frontend time: 0.605s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.005s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09700392 25.28163341 + layer.39.0 284.92189018 2614.98469388 + ------------------------------------------------------------------------------------- + TOTAL 142.50944705 1320.13316364 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 695656 +BPFP 0.6602 bits/point +EBPFP 0.6602 equivalent bits/point +MSE 1320.133164 +---------------------- -------------------------------------------------------- +Time: 1.691s Load: 0.081s, Pack+Encode: 0.605s, Decode+Unpack: 1.005s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1320.1332 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03775071-0.000145_Christmas stocking _ Christmas stocking_0.9986047.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03775071-0.000145_Christmas stocking _ Christmas stocking_0.9986047.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03788195-0.005975_steam locomotive _ steam locomotive_0.42419377.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03788195-0.005975_steam locomotive _ steam locomotive_0.42419377.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 256,548B, BPFP=0.4869 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 447,676B, BPFP=0.8497 +⌛️ [2/4] FRONTEND: Frontend time: 0.521s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.022s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10790903 174.55686650 + layer.39.0 10.34781284 2048.62123421 + ------------------------------------------------------------------------------------- + TOTAL 5.22786094 1111.58905035 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 704224 +BPFP 0.6683 bits/point +EBPFP 0.6683 equivalent bits/point +MSE 1111.589050 +---------------------- -------------------------------------------------------- +Time: 1.603s Load: 0.060s, Pack+Encode: 0.521s, Decode+Unpack: 1.022s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1111.5891 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03788195-0.005975_steam locomotive _ steam locomotive_0.42419377.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03788195-0.005975_steam locomotive _ steam locomotive_0.42419377.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03837869-0.002023_lighthouse _ lighthouse_0.5898798.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03837869-0.002023_lighthouse _ lighthouse_0.5898798.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 196,916B, BPFP=0.3738 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 465,028B, BPFP=0.8827 +⌛️ [2/4] FRONTEND: Frontend time: 0.593s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.064s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12703056 78.05678298 + layer.39.0 141.21340500 1883.37159864 + ------------------------------------------------------------------------------------- + TOTAL 70.67021778 980.71419081 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 661944 +BPFP 0.6282 bits/point +EBPFP 0.6282 equivalent bits/point +MSE 980.714191 +---------------------- -------------------------------------------------------- +Time: 1.736s Load: 0.080s, Pack+Encode: 0.593s, Decode+Unpack: 1.064s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 980.7142 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03837869-0.002023_lighthouse _ lighthouse_0.5898798.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03837869-0.002023_lighthouse _ lighthouse_0.5898798.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03840681-0.002188_ladybug _ ladybug_0.9972365.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03840681-0.002188_ladybug _ ladybug_0.9972365.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 174,364B, BPFP=0.3310 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 280,384B, BPFP=0.5322 +⌛️ [2/4] FRONTEND: Frontend time: 0.529s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.017s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09487485 12.73115586 + layer.39.0 29.40353574 1519.99939261 + ------------------------------------------------------------------------------------- + TOTAL 14.74920530 766.36527423 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 454748 +BPFP 0.4316 bits/point +EBPFP 0.4316 equivalent bits/point +MSE 766.365274 +---------------------- -------------------------------------------------------- +Time: 1.616s Load: 0.070s, Pack+Encode: 0.529s, Decode+Unpack: 1.017s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 766.3653 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03840681-0.002188_ladybug _ ladybug_0.9972365.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03840681-0.002188_ladybug _ ladybug_0.9972365.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03888257-0.004083_rugby ball _ snail_0.41588444.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03888257-0.004083_rugby ball _ snail_0.41588444.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 182,896B, BPFP=0.3472 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 297,892B, BPFP=0.5654 +⌛️ [2/4] FRONTEND: Frontend time: 0.587s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.052s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10005040 61.73027894 + layer.39.0 7.47115060 1192.38945578 + ------------------------------------------------------------------------------------- + TOTAL 3.78560050 627.05986736 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 480788 +BPFP 0.4563 bits/point +EBPFP 0.4563 equivalent bits/point +MSE 627.059867 +---------------------- -------------------------------------------------------- +Time: 1.720s Load: 0.080s, Pack+Encode: 0.587s, Decode+Unpack: 1.052s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 627.0599 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03888257-0.004083_rugby ball _ snail_0.41588444.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03888257-0.004083_rugby ball _ snail_0.41588444.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03891332-0.003727_syringe _ syringe_0.93799996.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03891332-0.003727_syringe _ syringe_0.93799996.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 199,032B, BPFP=0.3778 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 381,108B, BPFP=0.7234 +⌛️ [2/4] FRONTEND: Frontend time: 0.579s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.012s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09617506 12.67211890 + layer.39.0 18.45312310 2293.71987366 + ------------------------------------------------------------------------------------- + TOTAL 9.27464908 1153.19599628 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 580140 +BPFP 0.5506 bits/point +EBPFP 0.5506 equivalent bits/point +MSE 1153.195996 +---------------------- -------------------------------------------------------- +Time: 1.640s Load: 0.050s, Pack+Encode: 0.579s, Decode+Unpack: 1.012s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1153.1960 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03891332-0.003727_syringe _ syringe_0.93799996.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03891332-0.003727_syringe _ syringe_0.93799996.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03982430-0.005102_couch _ couch_0.9976859.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03982430-0.005102_couch _ couch_0.9976859.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 173,840B, BPFP=0.3300 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 332,192B, BPFP=0.6305 +⌛️ [2/4] FRONTEND: Frontend time: 0.512s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.031s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09691652 12.75228624 + layer.39.0 169.89398081 1776.74222546 + ------------------------------------------------------------------------------------- + TOTAL 84.99544866 894.74725585 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 506032 +BPFP 0.4802 bits/point +EBPFP 0.4802 equivalent bits/point +MSE 894.747256 +---------------------- -------------------------------------------------------- +Time: 1.595s Load: 0.052s, Pack+Encode: 0.512s, Decode+Unpack: 1.031s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 894.7473 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03982430-0.005102_couch _ couch_0.9976859.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03982430-0.005102_couch _ couch_0.9976859.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04033901-0.007476_envelope _ envelope_0.9990971.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04033901-0.007476_envelope _ envelope_0.9990971.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 186,848B, BPFP=0.3547 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 384,016B, BPFP=0.7289 +⌛️ [2/4] FRONTEND: Frontend time: 0.603s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.011s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10364226 12.82197712 + layer.39.0 7.34252906 1414.59596696 + ------------------------------------------------------------------------------------- + TOTAL 3.72308566 713.70897204 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 570864 +BPFP 0.5418 bits/point +EBPFP 0.5418 equivalent bits/point +MSE 713.708972 +---------------------- -------------------------------------------------------- +Time: 1.673s Load: 0.059s, Pack+Encode: 0.603s, Decode+Unpack: 1.011s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 713.7090 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04033901-0.007476_envelope _ envelope_0.9990971.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04033901-0.007476_envelope _ envelope_0.9990971.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04039381-0.000054_hair dryer _ hair dryer_0.5667548.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04039381-0.000054_hair dryer _ hair dryer_0.5667548.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.081s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 196,028B, BPFP=0.3721 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 491,044B, BPFP=0.9320 +⌛️ [2/4] FRONTEND: Frontend time: 0.583s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.059s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09588603 12.85721498 + layer.39.0 26.21653304 1935.81280369 + ------------------------------------------------------------------------------------- + TOTAL 13.15620954 974.33500934 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 687072 +BPFP 0.6521 bits/point +EBPFP 0.6521 equivalent bits/point +MSE 974.335009 +---------------------- -------------------------------------------------------- +Time: 1.722s Load: 0.081s, Pack+Encode: 0.583s, Decode+Unpack: 1.059s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 974.3350 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04039381-0.000054_hair dryer _ hair dryer_0.5667548.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04039381-0.000054_hair dryer _ hair dryer_0.5667548.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04118538-0.005804_cowboy boot _ cowboy boot_0.21208441.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04118538-0.005804_cowboy boot _ cowboy boot_0.21208441.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.081s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 216,120B, BPFP=0.4102 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 473,176B, BPFP=0.8981 +⌛️ [2/4] FRONTEND: Frontend time: 0.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.066s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09664223 85.54969175 + layer.39.0 8.64007266 1699.53644315 + ------------------------------------------------------------------------------------- + TOTAL 4.36835744 892.54306745 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 689296 +BPFP 0.6542 bits/point +EBPFP 0.6542 equivalent bits/point +MSE 892.543067 +---------------------- -------------------------------------------------------- +Time: 1.728s Load: 0.081s, Pack+Encode: 0.581s, Decode+Unpack: 1.066s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 892.5431 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04118538-0.005804_cowboy boot _ cowboy boot_0.21208441.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04118538-0.005804_cowboy boot _ cowboy boot_0.21208441.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04146614-0.008793_marimba _ marimba_0.54555196.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04146614-0.008793_marimba _ marimba_0.54555196.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 212,216B, BPFP=0.4028 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 356,780B, BPFP=0.6772 +⌛️ [2/4] FRONTEND: Frontend time: 0.503s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.995s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09774729 209.93605746 + layer.39.0 155.07908163 2927.78984451 + ------------------------------------------------------------------------------------- + TOTAL 77.58841446 1568.86295098 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 568996 +BPFP 0.5400 bits/point +EBPFP 0.5400 equivalent bits/point +MSE 1568.862951 +---------------------- -------------------------------------------------------- +Time: 1.550s Load: 0.052s, Pack+Encode: 0.503s, Decode+Unpack: 0.995s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1568.8630 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04146614-0.008793_marimba _ marimba_0.54555196.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04146614-0.008793_marimba _ marimba_0.54555196.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04179913-0.006024_guacamole _ custard apple_0.5550306.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04179913-0.006024_guacamole _ custard apple_0.5550306.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 247,256B, BPFP=0.4693 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 493,592B, BPFP=0.9369 +⌛️ [2/4] FRONTEND: Frontend time: 0.549s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.038s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11409367 149.29863642 + layer.39.0 68.43204871 2066.21234208 + ------------------------------------------------------------------------------------- + TOTAL 34.27307119 1107.75548925 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 740848 +BPFP 0.7031 bits/point +EBPFP 0.7031 equivalent bits/point +MSE 1107.755489 +---------------------- -------------------------------------------------------- +Time: 1.638s Load: 0.051s, Pack+Encode: 0.549s, Decode+Unpack: 1.038s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1107.7555 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04179913-0.006024_guacamole _ custard apple_0.5550306.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04179913-0.006024_guacamole _ custard apple_0.5550306.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04208210-0.007213_doormat _ doormat_0.81736016.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04208210-0.007213_doormat _ doormat_0.81736016.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 259,424B, BPFP=0.4924 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 391,912B, BPFP=0.7439 +⌛️ [2/4] FRONTEND: Frontend time: 0.496s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.003s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10601767 270.05764091 + layer.39.0 349.44518343 2401.94897959 + ------------------------------------------------------------------------------------- + TOTAL 174.77560055 1336.00331025 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 651336 +BPFP 0.6181 bits/point +EBPFP 0.6181 equivalent bits/point +MSE 1336.003310 +---------------------- -------------------------------------------------------- +Time: 1.550s Load: 0.051s, Pack+Encode: 0.496s, Decode+Unpack: 1.003s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1336.0033 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04208210-0.007213_doormat _ doormat_0.81736016.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04208210-0.007213_doormat _ doormat_0.81736016.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04270147-0.000435_rotary dial telephone _ rotary dial telephone_0.9268941.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04270147-0.000435_rotary dial telephone _ rotary dial telephone_0.9268941.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 190,912B, BPFP=0.3624 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 501,848B, BPFP=0.9525 +⌛️ [2/4] FRONTEND: Frontend time: 0.544s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.055s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09464848 12.65454268 + layer.39.0 229.78908528 2393.52405248 + ------------------------------------------------------------------------------------- + TOTAL 114.94186688 1203.08929758 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 692760 +BPFP 0.6575 bits/point +EBPFP 0.6575 equivalent bits/point +MSE 1203.089298 +---------------------- -------------------------------------------------------- +Time: 1.650s Load: 0.051s, Pack+Encode: 0.544s, Decode+Unpack: 1.055s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1203.0893 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04270147-0.000435_rotary dial telephone _ rotary dial telephone_0.9268941.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04270147-0.000435_rotary dial telephone _ rotary dial telephone_0.9268941.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04317175-0.006894_bow tie _ bow tie_0.95877784.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04317175-0.006894_bow tie _ bow tie_0.95877784.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 204,704B, BPFP=0.3885 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 473,188B, BPFP=0.8981 +⌛️ [2/4] FRONTEND: Frontend time: 0.523s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.003s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09706025 12.84741804 + layer.39.0 10.87108806 1992.35665695 + ------------------------------------------------------------------------------------- + TOTAL 5.48407415 1002.60203749 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 677892 +BPFP 0.6433 bits/point +EBPFP 0.6433 equivalent bits/point +MSE 1002.602037 +---------------------- -------------------------------------------------------- +Time: 1.577s Load: 0.051s, Pack+Encode: 0.523s, Decode+Unpack: 1.003s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1002.6020 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04317175-0.006894_bow tie _ bow tie_0.95877784.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04317175-0.006894_bow tie _ bow tie_0.95877784.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04347754-0.001405_viaduct _ viaduct_0.8445672.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04347754-0.001405_viaduct _ viaduct_0.8445672.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 175,536B, BPFP=0.3332 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 395,884B, BPFP=0.7514 +⌛️ [2/4] FRONTEND: Frontend time: 0.515s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.032s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09586499 12.85267762 + layer.39.0 267.55718537 2215.01797862 + ------------------------------------------------------------------------------------- + TOTAL 133.82652518 1113.93532812 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 571420 +BPFP 0.5423 bits/point +EBPFP 0.5423 equivalent bits/point +MSE 1113.935328 +---------------------- -------------------------------------------------------- +Time: 1.598s Load: 0.052s, Pack+Encode: 0.515s, Decode+Unpack: 1.032s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1113.9353 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04347754-0.001405_viaduct _ viaduct_0.8445672.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04347754-0.001405_viaduct _ viaduct_0.8445672.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04355338-0.000791_envelope _ envelope_0.9969598.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04355338-0.000791_envelope _ envelope_0.9969598.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 207,368B, BPFP=0.3936 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 439,944B, BPFP=0.8350 +⌛️ [2/4] FRONTEND: Frontend time: 0.557s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.063s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10273007 13.89770104 + layer.39.0 331.89978134 2696.11564626 + ------------------------------------------------------------------------------------- + TOTAL 166.00125571 1355.00667365 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 647312 +BPFP 0.6143 bits/point +EBPFP 0.6143 equivalent bits/point +MSE 1355.006674 +---------------------- -------------------------------------------------------- +Time: 1.670s Load: 0.051s, Pack+Encode: 0.557s, Decode+Unpack: 1.063s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1355.0067 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04355338-0.000791_envelope _ envelope_0.9969598.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04355338-0.000791_envelope _ envelope_0.9969598.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04366367-0.002021_parachute _ parachute_0.9226023.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04366367-0.002021_parachute _ parachute_0.9226023.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 170,204B, BPFP=0.3231 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 348,740B, BPFP=0.6619 +⌛️ [2/4] FRONTEND: Frontend time: 0.573s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.012s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09577132 24.97831633 + layer.39.0 47.60657343 1577.39844509 + ------------------------------------------------------------------------------------- + TOTAL 23.85117238 801.18838071 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 518944 +BPFP 0.4925 bits/point +EBPFP 0.4925 equivalent bits/point +MSE 801.188381 +---------------------- -------------------------------------------------------- +Time: 1.647s Load: 0.061s, Pack+Encode: 0.573s, Decode+Unpack: 1.012s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 801.1884 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04366367-0.002021_parachute _ parachute_0.9226023.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04366367-0.002021_parachute _ parachute_0.9226023.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04376876-0.004615_lighter _ lighter_0.69176143.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04376876-0.004615_lighter _ lighter_0.69176143.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 204,108B, BPFP=0.3874 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 473,124B, BPFP=0.8980 +⌛️ [2/4] FRONTEND: Frontend time: 0.511s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.073s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09912059 13.23558977 + layer.39.0 173.01079628 2188.34548105 + ------------------------------------------------------------------------------------- + TOTAL 86.55495844 1100.79053541 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 677232 +BPFP 0.6427 bits/point +EBPFP 0.6427 equivalent bits/point +MSE 1100.790535 +---------------------- -------------------------------------------------------- +Time: 1.636s Load: 0.052s, Pack+Encode: 0.511s, Decode+Unpack: 1.073s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1100.7905 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04376876-0.004615_lighter _ lighter_0.69176143.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04376876-0.004615_lighter _ lighter_0.69176143.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04442312-0.001690_washing machine _ washing machine_0.8494714.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04442312-0.001690_washing machine _ washing machine_0.8494714.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 168,380B, BPFP=0.3196 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 419,936B, BPFP=0.7971 +⌛️ [2/4] FRONTEND: Frontend time: 0.582s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.053s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.08302300 24.90186733 + layer.39.0 28.24609944 1756.73809524 + ------------------------------------------------------------------------------------- + TOTAL 18.16456122 890.81998128 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 588316 +BPFP 0.5583 bits/point +EBPFP 0.5583 equivalent bits/point +MSE 890.819981 +---------------------- -------------------------------------------------------- +Time: 1.706s Load: 0.071s, Pack+Encode: 0.582s, Decode+Unpack: 1.053s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 890.8200 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04442312-0.001690_washing machine _ washing machine_0.8494714.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04442312-0.001690_washing machine _ washing machine_0.8494714.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04456115-0.002573_hair dryer _ envelope_0.34823084.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04456115-0.002573_hair dryer _ envelope_0.34823084.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.082s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 169,120B, BPFP=0.3210 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 458,972B, BPFP=0.8712 +⌛️ [2/4] FRONTEND: Frontend time: 0.595s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.060s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09444211 12.65729015 + layer.39.0 8.80792942 1680.48603013 + ------------------------------------------------------------------------------------- + TOTAL 4.45118577 846.57166014 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 628092 +BPFP 0.5961 bits/point +EBPFP 0.5961 equivalent bits/point +MSE 846.571660 +---------------------- -------------------------------------------------------- +Time: 1.737s Load: 0.082s, Pack+Encode: 0.595s, Decode+Unpack: 1.060s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 846.5717 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04456115-0.002573_hair dryer _ envelope_0.34823084.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04456115-0.002573_hair dryer _ envelope_0.34823084.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04509417-0.000350_sundial _ sundial_0.99936897.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04509417-0.000350_sundial _ sundial_0.99936897.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 201,232B, BPFP=0.3820 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 404,656B, BPFP=0.7681 +⌛️ [2/4] FRONTEND: Frontend time: 0.573s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.046s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10319057 99.86988277 + layer.39.0 8.14296913 1514.51797862 + ------------------------------------------------------------------------------------- + TOTAL 4.12307985 807.19393070 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 605888 +BPFP 0.5750 bits/point +EBPFP 0.5750 equivalent bits/point +MSE 807.193931 +---------------------- -------------------------------------------------------- +Time: 1.689s Load: 0.069s, Pack+Encode: 0.573s, Decode+Unpack: 1.046s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 807.1939 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04509417-0.000350_sundial _ sundial_0.99936897.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04509417-0.000350_sundial _ sundial_0.99936897.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04532670-0.000670_spider web _ spider web_0.70711935.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04532670-0.000670_spider web _ spider web_0.70711935.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.083s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 230,908B, BPFP=0.4383 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 489,820B, BPFP=0.9297 +⌛️ [2/4] FRONTEND: Frontend time: 0.584s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.070s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09618602 38.20211522 + layer.39.0 175.41615039 2311.71914480 + ------------------------------------------------------------------------------------- + TOTAL 87.75616821 1174.96063001 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 720728 +BPFP 0.6840 bits/point +EBPFP 0.6840 equivalent bits/point +MSE 1174.960630 +---------------------- -------------------------------------------------------- +Time: 1.737s Load: 0.083s, Pack+Encode: 0.584s, Decode+Unpack: 1.070s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1174.9606 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04532670-0.000670_spider web _ spider web_0.70711935.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04532670-0.000670_spider web _ spider web_0.70711935.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04540053-0.000734_balance beam _ balance beam_0.99765223.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04540053-0.000734_balance beam _ balance beam_0.99765223.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 170,752B, BPFP=0.3241 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 411,184B, BPFP=0.7805 +⌛️ [2/4] FRONTEND: Frontend time: 0.582s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.053s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09941827 12.90446201 + layer.39.0 8.11341412 1674.19642857 + ------------------------------------------------------------------------------------- + TOTAL 4.10641619 843.55044529 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 581936 +BPFP 0.5523 bits/point +EBPFP 0.5523 equivalent bits/point +MSE 843.550445 +---------------------- -------------------------------------------------------- +Time: 1.705s Load: 0.069s, Pack+Encode: 0.582s, Decode+Unpack: 1.053s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 843.5504 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04540053-0.000734_balance beam _ balance beam_0.99765223.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04540053-0.000734_balance beam _ balance beam_0.99765223.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04606251-0.003483_cliff _ cliff_0.9972174.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04606251-0.003483_cliff _ cliff_0.9972174.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 219,164B, BPFP=0.4160 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 304,420B, BPFP=0.5778 +⌛️ [2/4] FRONTEND: Frontend time: 0.537s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.053s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09940710 150.18473639 + layer.39.0 906.86880466 2651.88556851 + ------------------------------------------------------------------------------------- + TOTAL 453.48410588 1401.03515245 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 523584 +BPFP 0.4969 bits/point +EBPFP 0.4969 equivalent bits/point +MSE 1401.035152 +---------------------- -------------------------------------------------------- +Time: 1.649s Load: 0.059s, Pack+Encode: 0.537s, Decode+Unpack: 1.053s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1401.0352 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04606251-0.003483_cliff _ cliff_0.9972174.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04606251-0.003483_cliff _ cliff_0.9972174.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07583066-0.003935_maraca _ maraca_0.5370013.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07583066-0.003935_maraca _ maraca_0.5370013.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 191,348B, BPFP=0.3632 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 314,484B, BPFP=0.5969 +⌛️ [2/4] FRONTEND: Frontend time: 0.578s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.053s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12045678 61.83093796 + layer.39.0 38.29438092 1912.86734694 + ------------------------------------------------------------------------------------- + TOTAL 19.20741885 987.34914245 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 505832 +BPFP 0.4801 bits/point +EBPFP 0.4801 equivalent bits/point +MSE 987.349142 +---------------------- -------------------------------------------------------- +Time: 1.702s Load: 0.070s, Pack+Encode: 0.578s, Decode+Unpack: 1.053s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 987.3491 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07583066-0.003935_maraca _ maraca_0.5370013.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n07583066-0.003935_maraca _ maraca_0.5370013.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07695742-0.003226_ant _ ant_0.64413536.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07695742-0.003226_ant _ ant_0.64413536.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 309,656B, BPFP=0.5878 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 428,260B, BPFP=0.8129 +⌛️ [2/4] FRONTEND: Frontend time: 0.517s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.036s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.16263347 309.62427114 + layer.39.0 172.10254191 2212.70408163 + ------------------------------------------------------------------------------------- + TOTAL 86.13258769 1261.16417638 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 737916 +BPFP 0.7003 bits/point +EBPFP 0.7003 equivalent bits/point +MSE 1261.164176 +---------------------- -------------------------------------------------------- +Time: 1.613s Load: 0.060s, Pack+Encode: 0.517s, Decode+Unpack: 1.036s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1261.1642 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07695742-0.003226_ant _ ant_0.64413536.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n07695742-0.003226_ant _ ant_0.64413536.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07718472-0.001330_spatula _ spatula_0.5388231.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07718472-0.001330_spatula _ spatula_0.5388231.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 205,928B, BPFP=0.3909 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 375,784B, BPFP=0.7133 +⌛️ [2/4] FRONTEND: Frontend time: 0.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.045s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09672572 12.62461943 + layer.39.0 34.52145211 2174.36637512 + ------------------------------------------------------------------------------------- + TOTAL 17.30908891 1093.49549728 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 581712 +BPFP 0.5521 bits/point +EBPFP 0.5521 equivalent bits/point +MSE 1093.495497 +---------------------- -------------------------------------------------------- +Time: 1.685s Load: 0.059s, Pack+Encode: 0.581s, Decode+Unpack: 1.045s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1093.4955 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07718472-0.001330_spatula _ spatula_0.5388231.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n07718472-0.001330_spatula _ spatula_0.5388231.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07831146-0.002710_guacamole _ guacamole_0.99510396.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07831146-0.002710_guacamole _ guacamole_0.99510396.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 227,876B, BPFP=0.4325 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 434,736B, BPFP=0.8252 +⌛️ [2/4] FRONTEND: Frontend time: 0.566s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.060s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09717902 13.01232234 + layer.39.0 26.55584533 2293.67468416 + ------------------------------------------------------------------------------------- + TOTAL 13.32651218 1153.34350325 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 662612 +BPFP 0.6288 bits/point +EBPFP 0.6288 equivalent bits/point +MSE 1153.343503 +---------------------- -------------------------------------------------------- +Time: 1.697s Load: 0.072s, Pack+Encode: 0.566s, Decode+Unpack: 1.060s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1153.3435 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07831146-0.002710_guacamole _ guacamole_0.99510396.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n07831146-0.002710_guacamole _ guacamole_0.99510396.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n09229709-0.002628_stethoscope _ stethoscope_0.9726458.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n09229709-0.002628_stethoscope _ stethoscope_0.9726458.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 204,244B, BPFP=0.3877 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 449,636B, BPFP=0.8534 +⌛️ [2/4] FRONTEND: Frontend time: 0.588s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.058s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10247729 25.73818255 + layer.39.0 58.71458181 1815.52016521 + ------------------------------------------------------------------------------------- + TOTAL 29.40852955 920.62917388 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 653880 +BPFP 0.6206 bits/point +EBPFP 0.6206 equivalent bits/point +MSE 920.629174 +---------------------- -------------------------------------------------------- +Time: 1.718s Load: 0.072s, Pack+Encode: 0.588s, Decode+Unpack: 1.058s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 920.6292 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n09229709-0.002628_stethoscope _ stethoscope_0.9726458.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n09229709-0.002628_stethoscope _ stethoscope_0.9726458.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12057211-0.000404_nail _ newt_0.31321314.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12057211-0.000404_nail _ newt_0.31321314.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 306,380B, BPFP=0.5815 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 367,460B, BPFP=0.6975 +⌛️ [2/4] FRONTEND: Frontend time: 0.561s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.061s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11577855 1171.98845967 + layer.39.0 8.72387956 1473.08588435 + ------------------------------------------------------------------------------------- + TOTAL 4.41982905 1322.53717201 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 673840 +BPFP 0.6395 bits/point +EBPFP 0.6395 equivalent bits/point +MSE 1322.537172 +---------------------- -------------------------------------------------------- +Time: 1.691s Load: 0.069s, Pack+Encode: 0.561s, Decode+Unpack: 1.061s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1322.5372 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12057211-0.000404_nail _ newt_0.31321314.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n12057211-0.000404_nail _ newt_0.31321314.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12144580-0.002806_banana _ banana_0.999156.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12144580-0.002806_banana _ banana_0.999156.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 219,852B, BPFP=0.4173 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 407,992B, BPFP=0.7744 +⌛️ [2/4] FRONTEND: Frontend time: 0.588s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.075s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09629347 61.69904792 + layer.39.0 105.38953930 3460.42857143 + ------------------------------------------------------------------------------------- + TOTAL 52.74291638 1761.06380968 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 627844 +BPFP 0.5958 bits/point +EBPFP 0.5958 equivalent bits/point +MSE 1761.063810 +---------------------- -------------------------------------------------------- +Time: 1.714s Load: 0.050s, Pack+Encode: 0.588s, Decode+Unpack: 1.075s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1761.0638 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12144580-0.002806_banana _ banana_0.999156.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n12144580-0.002806_banana _ banana_0.999156.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12267677-0.004617_pomegranate _ pomegranate_0.9159373.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12267677-0.004617_pomegranate _ pomegranate_0.9159373.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 230,364B, BPFP=0.4372 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 463,060B, BPFP=0.8789 +⌛️ [2/4] FRONTEND: Frontend time: 0.516s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.994s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10323383 8.71094319 + layer.39.0 78.12042942 2269.94509232 + ------------------------------------------------------------------------------------- + TOTAL 39.11183162 1139.32801776 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 693424 +BPFP 0.6581 bits/point +EBPFP 0.6581 equivalent bits/point +MSE 1139.328018 +---------------------- -------------------------------------------------------- +Time: 1.569s Load: 0.059s, Pack+Encode: 0.516s, Decode+Unpack: 0.994s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1139.3280 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12267677-0.004617_pomegranate _ pomegranate_0.9159373.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n12267677-0.004617_pomegranate _ pomegranate_0.9159373.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 0.5986 bits/point +Avg EBPFP 0.5986 equivalent bits/point +Avg MSE 1090.470667 +Avg Time 1.818s +------------------------ ---------------------------- diff --git a/lambda0.007/hyperprior-featurecoding-8bit-individual/cls_in1kr/dtufc_hyperprior-featurecoding_dinov3-total_individual.log b/lambda0.007/hyperprior-featurecoding-8bit-individual/cls_in1kr/dtufc_hyperprior-featurecoding_dinov3-total_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..3c5a058f41826b65efcf5ef4c0062304c3ff7b3b --- /dev/null +++ b/lambda0.007/hyperprior-featurecoding-8bit-individual/cls_in1kr/dtufc_hyperprior-featurecoding_dinov3-total_individual.log @@ -0,0 +1,9344 @@ +Experiment: dtufc_hyperprior-featurecoding_dinov3-total_individual +Log file: output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/dtufc_hyperprior-featurecoding_dinov3-total_individual.log +DTUFCCodecConfig: + arch: hyperprior-featurecoding + handler: dinov3-total + checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.007_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.007_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 599 +Loaded hyperprior-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.9' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.19' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.29' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.39' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json +Loaded per-key mappings: model=dinov3-total + Keys: ['layer.9', 'layer.19', 'layer.29', 'layer.39'] +---------------- ------------------------------------------------------------------------------------------------------------------------------ +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +Checkpoint codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.007_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-DINOv3/imagenet1k-r +Output output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr +---------------- ------------------------------------------------------------------------------------------------------------------------------ +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01443537-graffiti_3.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01443537-graffiti_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.091s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 207,288B, BPFP=0.3934 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 416,192B, BPFP=0.7900 +⌛️ [2/4] FRONTEND: Frontend time: 0.965s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.214s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09690064 61.22364705 + layer.39.0 23.14008974 1979.65682702 + ------------------------------------------------------------------------------------- + TOTAL 11.61849519 1020.44023703 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 623480 +BPFP 0.5917 bits/point +EBPFP 0.5917 equivalent bits/point +MSE 1020.440237 +---------------------- -------------------------------------------------------- +Time: 2.270s Load: 0.091s, Pack+Encode: 0.965s, Decode+Unpack: 1.214s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1020.4402 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01443537-graffiti_3.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01443537-graffiti_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01494475-misc_31.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01494475-misc_31.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 198,452B, BPFP=0.3767 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 447,416B, BPFP=0.8492 +⌛️ [2/4] FRONTEND: Frontend time: 0.511s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.994s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09558801 12.82739804 + layer.39.0 281.54433916 2816.24076774 + ------------------------------------------------------------------------------------- + TOTAL 140.81996359 1414.53408289 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 645868 +BPFP 0.6130 bits/point +EBPFP 0.6130 equivalent bits/point +MSE 1414.534083 +---------------------- -------------------------------------------------------- +Time: 1.556s Load: 0.051s, Pack+Encode: 0.511s, Decode+Unpack: 0.994s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1414.5341 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01494475-misc_31.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01494475-misc_31.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01531178-cartoon_22.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01531178-cartoon_22.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 215,048B, BPFP=0.4082 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 507,744B, BPFP=0.9637 +⌛️ [2/4] FRONTEND: Frontend time: 0.524s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.011s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10319715 25.37189094 + layer.39.0 12.97479918 1577.53814383 + ------------------------------------------------------------------------------------- + TOTAL 6.53899817 801.45501739 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 722792 +BPFP 0.6860 bits/point +EBPFP 0.6860 equivalent bits/point +MSE 801.455017 +---------------------- -------------------------------------------------------- +Time: 1.587s Load: 0.052s, Pack+Encode: 0.524s, Decode+Unpack: 1.011s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 801.4550 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01531178-cartoon_22.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01531178-cartoon_22.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01534433-painting_9.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01534433-painting_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 247,980B, BPFP=0.4707 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 358,804B, BPFP=0.6810 +⌛️ [2/4] FRONTEND: Frontend time: 0.542s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.981s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10660143 404.78146259 + layer.39.0 8.42910859 1649.71367833 + ------------------------------------------------------------------------------------- + TOTAL 4.26785501 1027.24757046 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 606784 +BPFP 0.5759 bits/point +EBPFP 0.5759 equivalent bits/point +MSE 1027.247570 +---------------------- -------------------------------------------------------- +Time: 1.575s Load: 0.052s, Pack+Encode: 0.542s, Decode+Unpack: 0.981s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1027.2476 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01534433-painting_9.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01534433-painting_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01632777-toy_21.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01632777-toy_21.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.056s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 194,660B, BPFP=0.3695 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 410,456B, BPFP=0.7791 +⌛️ [2/4] FRONTEND: Frontend time: 0.568s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.008s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09516629 12.56481756 + layer.39.0 31.73491595 2720.98080661 + ------------------------------------------------------------------------------------- + TOTAL 15.91504112 1366.77281208 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 605116 +BPFP 0.5743 bits/point +EBPFP 0.5743 equivalent bits/point +MSE 1366.772812 +---------------------- -------------------------------------------------------- +Time: 1.633s Load: 0.056s, Pack+Encode: 0.568s, Decode+Unpack: 1.008s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1366.7728 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01632777-toy_21.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01632777-toy_21.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01748264-misc_18.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01748264-misc_18.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 259,956B, BPFP=0.4934 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 342,312B, BPFP=0.6497 +⌛️ [2/4] FRONTEND: Frontend time: 15.502s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.022s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.16139180 820.98906706 + layer.39.0 362.83485180 2396.08600583 + ------------------------------------------------------------------------------------- + TOTAL 181.49812180 1608.53753644 + (elements=8,429,568) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 8429568 +Total Bytes 602268 +BPFP 0.5716 bits/point +EBPFP 0.5716 equivalent bits/point +MSE 1608.537536 +---------------------- --------------------------------------------------------- +Time: 16.576s Load: 0.051s, Pack+Encode: 15.502s, Decode+Unpack: 1.022s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1608.5375 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01748264-misc_18.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01748264-misc_18.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01784675-painting_2.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01784675-painting_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 263,096B, BPFP=0.4994 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 338,972B, BPFP=0.6434 +⌛️ [2/4] FRONTEND: Frontend time: 0.544s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.037s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13866578 184.34355260 + layer.39.0 232.10166120 2418.02502430 + ------------------------------------------------------------------------------------- + TOTAL 116.12016349 1301.18428845 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 602068 +BPFP 0.5714 bits/point +EBPFP 0.5714 equivalent bits/point +MSE 1301.184288 +---------------------- -------------------------------------------------------- +Time: 1.633s Load: 0.052s, Pack+Encode: 0.544s, Decode+Unpack: 1.037s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1301.1843 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01784675-painting_2.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01784675-painting_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01820546-painting_29.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01820546-painting_29.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 254,616B, BPFP=0.4833 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 342,020B, BPFP=0.6492 +⌛️ [2/4] FRONTEND: Frontend time: 0.546s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.995s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10398871 492.67182945 + layer.39.0 202.99580904 3045.26117590 + ------------------------------------------------------------------------------------- + TOTAL 101.54989888 1768.96650267 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 596636 +BPFP 0.5662 bits/point +EBPFP 0.5662 equivalent bits/point +MSE 1768.966503 +---------------------- -------------------------------------------------------- +Time: 1.592s Load: 0.051s, Pack+Encode: 0.546s, Decode+Unpack: 0.995s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1768.9665 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01820546-painting_29.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01820546-painting_29.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01833805-painting_23.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01833805-painting_23.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 216,756B, BPFP=0.4114 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 441,892B, BPFP=0.8387 +⌛️ [2/4] FRONTEND: Frontend time: 0.585s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.042s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09675035 74.26028000 + layer.39.0 56.43029868 1739.35738581 + ------------------------------------------------------------------------------------- + TOTAL 28.26352451 906.80883291 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 658648 +BPFP 0.6251 bits/point +EBPFP 0.6251 equivalent bits/point +MSE 906.808833 +---------------------- -------------------------------------------------------- +Time: 1.678s Load: 0.051s, Pack+Encode: 0.585s, Decode+Unpack: 1.042s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 906.8088 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01833805-painting_23.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01833805-painting_23.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01860187-painting_2.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01860187-painting_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 215,004B, BPFP=0.4081 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 429,424B, BPFP=0.8151 +⌛️ [2/4] FRONTEND: Frontend time: 0.505s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.981s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09532418 24.69162073 + layer.39.0 11.39113179 1780.14467930 + ------------------------------------------------------------------------------------- + TOTAL 5.74322799 902.41815002 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 644428 +BPFP 0.6116 bits/point +EBPFP 0.6116 equivalent bits/point +MSE 902.418150 +---------------------- -------------------------------------------------------- +Time: 1.536s Load: 0.051s, Pack+Encode: 0.505s, Decode+Unpack: 0.981s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 902.4182 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01860187-painting_2.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01860187-painting_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01944390-deviantart_6.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01944390-deviantart_6.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 197,960B, BPFP=0.3757 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 311,124B, BPFP=0.5905 +⌛️ [2/4] FRONTEND: Frontend time: 0.529s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.020s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10713051 50.26325240 + layer.39.0 82.30322218 2427.06316812 + ------------------------------------------------------------------------------------- + TOTAL 41.20517635 1238.66321026 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 509084 +BPFP 0.4831 bits/point +EBPFP 0.4831 equivalent bits/point +MSE 1238.663210 +---------------------- -------------------------------------------------------- +Time: 1.600s Load: 0.051s, Pack+Encode: 0.529s, Decode+Unpack: 1.020s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1238.6632 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01944390-deviantart_6.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01944390-deviantart_6.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01983481-misc_6.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01983481-misc_6.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 236,280B, BPFP=0.4485 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 375,988B, BPFP=0.7137 +⌛️ [2/4] FRONTEND: Frontend time: 0.546s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.998s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10315659 236.47069363 + layer.39.0 236.29731535 2644.91739553 + ------------------------------------------------------------------------------------- + TOTAL 118.20023597 1440.69404458 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 612268 +BPFP 0.5811 bits/point +EBPFP 0.5811 equivalent bits/point +MSE 1440.694045 +---------------------- -------------------------------------------------------- +Time: 1.595s Load: 0.051s, Pack+Encode: 0.546s, Decode+Unpack: 0.998s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1440.6940 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01983481-misc_6.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01983481-misc_6.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02051845-cartoon_2.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02051845-cartoon_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 212,208B, BPFP=0.4028 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 500,612B, BPFP=0.9502 +⌛️ [2/4] FRONTEND: Frontend time: 0.544s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.039s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11657756 12.66971593 + layer.39.0 123.57765428 2079.62925170 + ------------------------------------------------------------------------------------- + TOTAL 61.84711592 1046.14948382 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 712820 +BPFP 0.6765 bits/point +EBPFP 0.6765 equivalent bits/point +MSE 1046.149484 +---------------------- -------------------------------------------------------- +Time: 1.634s Load: 0.051s, Pack+Encode: 0.544s, Decode+Unpack: 1.039s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1046.1495 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02051845-cartoon_2.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02051845-cartoon_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02056570-art_2.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02056570-art_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 192,932B, BPFP=0.3662 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 344,020B, BPFP=0.6530 +⌛️ [2/4] FRONTEND: Frontend time: 0.576s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.027s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09569211 24.68287628 + layer.39.0 33.39981930 2303.27453839 + ------------------------------------------------------------------------------------- + TOTAL 16.74775571 1163.97870733 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 536952 +BPFP 0.5096 bits/point +EBPFP 0.5096 equivalent bits/point +MSE 1163.978707 +---------------------- -------------------------------------------------------- +Time: 1.653s Load: 0.050s, Pack+Encode: 0.576s, Decode+Unpack: 1.027s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1163.9787 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02056570-art_2.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02056570-art_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02085620-misc_90.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02085620-misc_90.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 217,636B, BPFP=0.4131 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 457,112B, BPFP=0.8676 +⌛️ [2/4] FRONTEND: Frontend time: 0.509s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.004s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09843166 12.63297858 + layer.39.0 72.76188958 2497.40014577 + ------------------------------------------------------------------------------------- + TOTAL 36.43016062 1255.01656218 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 674748 +BPFP 0.6404 bits/point +EBPFP 0.6404 equivalent bits/point +MSE 1255.016562 +---------------------- -------------------------------------------------------- +Time: 1.565s Load: 0.051s, Pack+Encode: 0.509s, Decode+Unpack: 1.004s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1255.0166 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02085620-misc_90.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02085620-misc_90.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088094-misc_39.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088094-misc_39.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 220,028B, BPFP=0.4176 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 390,848B, BPFP=0.7419 +⌛️ [2/4] FRONTEND: Frontend time: 0.503s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.983s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09820385 12.68128663 + layer.39.0 12.32374423 1841.16970360 + ------------------------------------------------------------------------------------- + TOTAL 6.21097404 926.92549511 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 610876 +BPFP 0.5797 bits/point +EBPFP 0.5797 equivalent bits/point +MSE 926.925495 +---------------------- -------------------------------------------------------- +Time: 1.537s Load: 0.051s, Pack+Encode: 0.503s, Decode+Unpack: 0.983s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 926.9255 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088094-misc_39.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02088094-misc_39.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088466-sketch_11.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088466-sketch_11.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 191,144B, BPFP=0.3628 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 448,040B, BPFP=0.8504 +⌛️ [2/4] FRONTEND: Frontend time: 0.510s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.983s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09459993 12.66619404 + layer.39.0 16.33682960 2022.63581147 + ------------------------------------------------------------------------------------- + TOTAL 8.21571477 1017.65100276 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 639184 +BPFP 0.6066 bits/point +EBPFP 0.6066 equivalent bits/point +MSE 1017.651003 +---------------------- -------------------------------------------------------- +Time: 1.545s Load: 0.052s, Pack+Encode: 0.510s, Decode+Unpack: 0.983s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1017.6510 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088466-sketch_11.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02088466-sketch_11.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02094433-misc_20.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02094433-misc_20.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 217,868B, BPFP=0.4135 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 371,880B, BPFP=0.7059 +⌛️ [2/4] FRONTEND: Frontend time: 0.522s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.991s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09538842 62.23853559 + layer.39.0 94.83275632 3201.05466472 + ------------------------------------------------------------------------------------- + TOTAL 47.46407237 1631.64660016 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 589748 +BPFP 0.5597 bits/point +EBPFP 0.5597 equivalent bits/point +MSE 1631.646600 +---------------------- -------------------------------------------------------- +Time: 1.565s Load: 0.052s, Pack+Encode: 0.522s, Decode+Unpack: 0.991s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1631.6466 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02094433-misc_20.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02094433-misc_20.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02097298-misc_9.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02097298-misc_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 285,012B, BPFP=0.5410 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 459,692B, BPFP=0.8725 +⌛️ [2/4] FRONTEND: Frontend time: 0.516s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.000s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11199322 592.32009232 + layer.39.0 26.16675018 2288.59718173 + ------------------------------------------------------------------------------------- + TOTAL 13.13937170 1440.45863703 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 744704 +BPFP 0.7068 bits/point +EBPFP 0.7068 equivalent bits/point +MSE 1440.458637 +---------------------- -------------------------------------------------------- +Time: 1.576s Load: 0.060s, Pack+Encode: 0.516s, Decode+Unpack: 1.000s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1440.4586 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02097298-misc_9.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02097298-misc_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02106662-misc_55.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02106662-misc_55.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 221,820B, BPFP=0.4210 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 475,476B, BPFP=0.9025 +⌛️ [2/4] FRONTEND: Frontend time: 0.577s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.007s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09642073 99.42563320 + layer.39.0 14.86428154 1778.91800292 + ------------------------------------------------------------------------------------- + TOTAL 7.48035113 939.17181806 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 697296 +BPFP 0.6618 bits/point +EBPFP 0.6618 equivalent bits/point +MSE 939.171818 +---------------------- -------------------------------------------------------- +Time: 1.635s Load: 0.052s, Pack+Encode: 0.577s, Decode+Unpack: 1.007s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 939.1718 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02106662-misc_55.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02106662-misc_55.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02109525-sketch_23.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02109525-sketch_23.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 189,768B, BPFP=0.3602 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 459,096B, BPFP=0.8714 +⌛️ [2/4] FRONTEND: Frontend time: 0.567s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.011s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09568003 12.61820582 + layer.39.0 14.01675815 1937.40208941 + ------------------------------------------------------------------------------------- + TOTAL 7.05621909 975.01014761 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 648864 +BPFP 0.6158 bits/point +EBPFP 0.6158 equivalent bits/point +MSE 975.010148 +---------------------- -------------------------------------------------------- +Time: 1.649s Load: 0.071s, Pack+Encode: 0.567s, Decode+Unpack: 1.011s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 975.0101 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02109525-sketch_23.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02109525-sketch_23.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110185-painting_33.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110185-painting_33.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 214,164B, BPFP=0.4065 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 515,472B, BPFP=0.9784 +⌛️ [2/4] FRONTEND: Frontend time: 0.511s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.984s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09599521 12.60504073 + layer.39.0 22.05506522 2414.58843537 + ------------------------------------------------------------------------------------- + TOTAL 11.07553021 1213.59673805 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 729636 +BPFP 0.6925 bits/point +EBPFP 0.6925 equivalent bits/point +MSE 1213.596738 +---------------------- -------------------------------------------------------- +Time: 1.556s Load: 0.061s, Pack+Encode: 0.511s, Decode+Unpack: 0.984s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1213.5967 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110185-painting_33.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02110185-painting_33.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110341-misc_162.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110341-misc_162.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 196,692B, BPFP=0.3733 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 424,216B, BPFP=0.8052 +⌛️ [2/4] FRONTEND: Frontend time: 0.521s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.011s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11124049 25.76205091 + layer.39.0 14.33747210 1602.17310496 + ------------------------------------------------------------------------------------- + TOTAL 7.22435629 813.96757794 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 620908 +BPFP 0.5893 bits/point +EBPFP 0.5893 equivalent bits/point +MSE 813.967578 +---------------------- -------------------------------------------------------- +Time: 1.583s Load: 0.051s, Pack+Encode: 0.521s, Decode+Unpack: 1.011s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 813.9676 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110341-misc_162.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02110341-misc_162.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02165456-tattoo_37.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02165456-tattoo_37.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 237,556B, BPFP=0.4509 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 424,688B, BPFP=0.8061 +⌛️ [2/4] FRONTEND: Frontend time: 0.539s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.983s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09780899 37.80585672 + layer.39.0 88.96013271 2836.67662779 + ------------------------------------------------------------------------------------- + TOTAL 44.52897085 1437.24124226 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 662244 +BPFP 0.6285 bits/point +EBPFP 0.6285 equivalent bits/point +MSE 1437.241242 +---------------------- -------------------------------------------------------- +Time: 1.572s Load: 0.051s, Pack+Encode: 0.539s, Decode+Unpack: 0.983s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1437.2412 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02165456-tattoo_37.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02165456-tattoo_37.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02219486-misc_3.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02219486-misc_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 183,648B, BPFP=0.3486 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 422,552B, BPFP=0.8020 +⌛️ [2/4] FRONTEND: Frontend time: 0.564s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.012s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10021695 12.84496572 + layer.39.0 75.73793580 1379.25680272 + ------------------------------------------------------------------------------------- + TOTAL 37.91907638 696.05088422 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 606200 +BPFP 0.5753 bits/point +EBPFP 0.5753 equivalent bits/point +MSE 696.050884 +---------------------- -------------------------------------------------------- +Time: 1.626s Load: 0.050s, Pack+Encode: 0.564s, Decode+Unpack: 1.012s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 696.0509 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02219486-misc_3.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02219486-misc_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02226429-tattoo_0.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02226429-tattoo_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 16.358s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 212,304B, BPFP=0.4030 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 409,072B, BPFP=0.7765 +⌛️ [2/4] FRONTEND: Frontend time: 0.515s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.984s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09506506 12.59349091 + layer.39.0 201.13660107 2128.48007775 + ------------------------------------------------------------------------------------- + TOTAL 100.61583306 1070.53678433 + (elements=8,429,568) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 8429568 +Total Bytes 621376 +BPFP 0.5897 bits/point +EBPFP 0.5897 equivalent bits/point +MSE 1070.536784 +---------------------- --------------------------------------------------------- +Time: 17.858s Load: 16.358s, Pack+Encode: 0.515s, Decode+Unpack: 0.984s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1070.5368 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02226429-tattoo_0.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02226429-tattoo_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02233338-tattoo_5.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02233338-tattoo_5.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 206,280B, BPFP=0.3915 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 458,772B, BPFP=0.8708 +⌛️ [2/4] FRONTEND: Frontend time: 0.556s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.021s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09502332 12.59464305 + layer.39.0 172.43500972 2695.51700680 + ------------------------------------------------------------------------------------- + TOTAL 86.26501652 1354.05582492 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 665052 +BPFP 0.6312 bits/point +EBPFP 0.6312 equivalent bits/point +MSE 1354.055825 +---------------------- -------------------------------------------------------- +Time: 1.628s Load: 0.051s, Pack+Encode: 0.556s, Decode+Unpack: 1.021s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1354.0558 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02233338-tattoo_5.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02233338-tattoo_5.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02279972-painting_2.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02279972-painting_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 287,732B, BPFP=0.5461 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 473,592B, BPFP=0.8989 +⌛️ [2/4] FRONTEND: Frontend time: 0.503s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.006s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11337867 356.24207362 + layer.39.0 361.17623299 2529.32798834 + ------------------------------------------------------------------------------------- + TOTAL 180.64480583 1442.78503098 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 761324 +BPFP 0.7225 bits/point +EBPFP 0.7225 equivalent bits/point +MSE 1442.785031 +---------------------- -------------------------------------------------------- +Time: 1.559s Load: 0.051s, Pack+Encode: 0.503s, Decode+Unpack: 1.006s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1442.7850 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02279972-painting_2.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02279972-painting_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02317335-sculpture_0.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02317335-sculpture_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 211,892B, BPFP=0.4022 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 502,980B, BPFP=0.9547 +⌛️ [2/4] FRONTEND: Frontend time: 0.521s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.999s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09546056 26.26872457 + layer.39.0 1163.18707483 2617.81729835 + ------------------------------------------------------------------------------------- + TOTAL 581.64126769 1322.04301146 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 714872 +BPFP 0.6784 bits/point +EBPFP 0.6784 equivalent bits/point +MSE 1322.043011 +---------------------- -------------------------------------------------------- +Time: 1.570s Load: 0.050s, Pack+Encode: 0.521s, Decode+Unpack: 0.999s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1322.0430 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02317335-sculpture_0.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02317335-sculpture_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02346627-painting_9.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02346627-painting_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 247,060B, BPFP=0.4689 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 497,988B, BPFP=0.9452 +⌛️ [2/4] FRONTEND: Frontend time: 0.531s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.992s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13205896 348.23344874 + layer.39.0 503.01482021 2599.07555879 + ------------------------------------------------------------------------------------- + TOTAL 251.57343959 1473.65450377 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 745048 +BPFP 0.7071 bits/point +EBPFP 0.7071 equivalent bits/point +MSE 1473.654504 +---------------------- -------------------------------------------------------- +Time: 1.575s Load: 0.052s, Pack+Encode: 0.531s, Decode+Unpack: 0.992s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1473.6545 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02346627-painting_9.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02346627-painting_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02391049-deviantart_0.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02391049-deviantart_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 197,188B, BPFP=0.3743 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 398,732B, BPFP=0.7568 +⌛️ [2/4] FRONTEND: Frontend time: 0.545s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.021s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10116939 38.21236865 + layer.39.0 17.42674737 1877.39504373 + ------------------------------------------------------------------------------------- + TOTAL 8.76395838 957.80370619 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 595920 +BPFP 0.5656 bits/point +EBPFP 0.5656 equivalent bits/point +MSE 957.803706 +---------------------- -------------------------------------------------------- +Time: 1.618s Load: 0.052s, Pack+Encode: 0.545s, Decode+Unpack: 1.021s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 957.8037 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02391049-deviantart_0.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02391049-deviantart_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02395406-sculpture_31.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02395406-sculpture_31.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 282,328B, BPFP=0.5359 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 351,048B, BPFP=0.6663 +⌛️ [2/4] FRONTEND: Frontend time: 0.507s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.983s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11469608 1165.79324587 + layer.39.0 30.55020044 1684.07106414 + ------------------------------------------------------------------------------------- + TOTAL 15.33244826 1424.93215500 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 633376 +BPFP 0.6011 bits/point +EBPFP 0.6011 equivalent bits/point +MSE 1424.932155 +---------------------- -------------------------------------------------------- +Time: 1.541s Load: 0.050s, Pack+Encode: 0.507s, Decode+Unpack: 0.983s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1424.9322 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02395406-sculpture_31.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02395406-sculpture_31.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02445715-art_3.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02445715-art_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.053s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 207,908B, BPFP=0.3946 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 441,504B, BPFP=0.8380 +⌛️ [2/4] FRONTEND: Frontend time: 0.542s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.007s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09587883 25.25557276 + layer.39.0 77.63827138 2798.92808552 + ------------------------------------------------------------------------------------- + TOTAL 38.86707511 1412.09182914 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 649412 +BPFP 0.6163 bits/point +EBPFP 0.6163 equivalent bits/point +MSE 1412.091829 +---------------------- -------------------------------------------------------- +Time: 1.601s Load: 0.053s, Pack+Encode: 0.542s, Decode+Unpack: 1.007s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1412.0918 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02445715-art_3.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02445715-art_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02672831-sculpture_2.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02672831-sculpture_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 258,648B, BPFP=0.4909 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 390,956B, BPFP=0.7421 +⌛️ [2/4] FRONTEND: Frontend time: 0.547s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.010s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11638676 321.82206633 + layer.39.0 42.74346681 3045.72327502 + ------------------------------------------------------------------------------------- + TOTAL 21.42992678 1683.77267068 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 649604 +BPFP 0.6165 bits/point +EBPFP 0.6165 equivalent bits/point +MSE 1683.772671 +---------------------- -------------------------------------------------------- +Time: 1.608s Load: 0.052s, Pack+Encode: 0.547s, Decode+Unpack: 1.010s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1683.7727 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02672831-sculpture_2.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02672831-sculpture_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02701002-videogame_1.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02701002-videogame_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 213,220B, BPFP=0.4047 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 379,600B, BPFP=0.7205 +⌛️ [2/4] FRONTEND: Frontend time: 0.557s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.009s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10320827 134.89445153 + layer.39.0 160.61054422 2901.10568513 + ------------------------------------------------------------------------------------- + TOTAL 80.35687624 1518.00006833 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 592820 +BPFP 0.5626 bits/point +EBPFP 0.5626 equivalent bits/point +MSE 1518.000068 +---------------------- -------------------------------------------------------- +Time: 1.618s Load: 0.051s, Pack+Encode: 0.557s, Decode+Unpack: 1.009s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1518.0001 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02701002-videogame_1.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02701002-videogame_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02749479-misc_35.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02749479-misc_35.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 178,704B, BPFP=0.3392 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 341,828B, BPFP=0.6488 +⌛️ [2/4] FRONTEND: Frontend time: 0.504s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.015s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09764870 13.12207885 + layer.39.0 172.65676628 2285.56073858 + ------------------------------------------------------------------------------------- + TOTAL 86.37720749 1149.34140872 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 520532 +BPFP 0.4940 bits/point +EBPFP 0.4940 equivalent bits/point +MSE 1149.341409 +---------------------- -------------------------------------------------------- +Time: 1.588s Load: 0.069s, Pack+Encode: 0.504s, Decode+Unpack: 1.015s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1149.3414 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02749479-misc_35.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02749479-misc_35.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02769748-cartoon_11.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02769748-cartoon_11.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 188,528B, BPFP=0.3578 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 497,304B, BPFP=0.9439 +⌛️ [2/4] FRONTEND: Frontend time: 0.559s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.000s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12263774 24.83912438 + layer.39.0 11.02823964 1827.18537415 + ------------------------------------------------------------------------------------- + TOTAL 5.57543869 926.01224926 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 685832 +BPFP 0.6509 bits/point +EBPFP 0.6509 equivalent bits/point +MSE 926.012249 +---------------------- -------------------------------------------------------- +Time: 1.610s Load: 0.050s, Pack+Encode: 0.559s, Decode+Unpack: 1.000s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 926.0122 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02769748-cartoon_11.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02769748-cartoon_11.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02793495-sketch_11.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02793495-sketch_11.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 185,384B, BPFP=0.3519 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 327,492B, BPFP=0.6216 +⌛️ [2/4] FRONTEND: Frontend time: 0.575s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.026s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09793751 75.81698706 + layer.39.0 182.75789602 2333.23347911 + ------------------------------------------------------------------------------------- + TOTAL 91.42791676 1204.52523308 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 512876 +BPFP 0.4867 bits/point +EBPFP 0.4867 equivalent bits/point +MSE 1204.525233 +---------------------- -------------------------------------------------------- +Time: 1.652s Load: 0.051s, Pack+Encode: 0.575s, Decode+Unpack: 1.026s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1204.5252 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02793495-sketch_11.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02793495-sketch_11.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02797295-misc_13.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02797295-misc_13.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 323,676B, BPFP=0.6144 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 425,756B, BPFP=0.8081 +⌛️ [2/4] FRONTEND: Frontend time: 0.592s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.012s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.17140635 1131.23530126 + layer.39.0 172.50999150 3028.86345967 + ------------------------------------------------------------------------------------- + TOTAL 86.34069892 2080.04938047 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 749432 +BPFP 0.7112 bits/point +EBPFP 0.7112 equivalent bits/point +MSE 2080.049380 +---------------------- -------------------------------------------------------- +Time: 1.656s Load: 0.051s, Pack+Encode: 0.592s, Decode+Unpack: 1.012s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2080.0494 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02797295-misc_13.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02797295-misc_13.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02802426-art_1.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02802426-art_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 297,824B, BPFP=0.5653 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 447,184B, BPFP=0.8488 +⌛️ [2/4] FRONTEND: Frontend time: 0.529s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.987s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.16523854 343.90084427 + layer.39.0 477.65184645 2934.94193392 + ------------------------------------------------------------------------------------- + TOTAL 238.90854250 1639.42138909 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 745008 +BPFP 0.7070 bits/point +EBPFP 0.7070 equivalent bits/point +MSE 1639.421389 +---------------------- -------------------------------------------------------- +Time: 1.568s Load: 0.052s, Pack+Encode: 0.529s, Decode+Unpack: 0.987s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1639.4214 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02802426-art_1.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02802426-art_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02814860-sticker_8.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02814860-sticker_8.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 216,276B, BPFP=0.4105 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 495,316B, BPFP=0.9401 +⌛️ [2/4] FRONTEND: Frontend time: 0.569s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.986s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12757226 84.90224125 + layer.39.0 19.27598852 1539.59961127 + ------------------------------------------------------------------------------------- + TOTAL 9.70178039 812.25092626 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 711592 +BPFP 0.6753 bits/point +EBPFP 0.6753 equivalent bits/point +MSE 812.250926 +---------------------- -------------------------------------------------------- +Time: 1.625s Load: 0.070s, Pack+Encode: 0.569s, Decode+Unpack: 0.986s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 812.2509 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02814860-sticker_8.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02814860-sticker_8.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02841315-graphic_4.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02841315-graphic_4.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 229,012B, BPFP=0.4347 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 415,928B, BPFP=0.7895 +⌛️ [2/4] FRONTEND: Frontend time: 0.575s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.013s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11826141 62.28618881 + layer.39.0 55.46440340 2153.94460641 + ------------------------------------------------------------------------------------- + TOTAL 27.79133240 1108.11539761 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 644940 +BPFP 0.6121 bits/point +EBPFP 0.6121 equivalent bits/point +MSE 1108.115398 +---------------------- -------------------------------------------------------- +Time: 1.659s Load: 0.070s, Pack+Encode: 0.575s, Decode+Unpack: 1.013s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1108.1154 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02841315-graphic_4.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02841315-graphic_4.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02843684-cartoon_9.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02843684-cartoon_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 250,648B, BPFP=0.4758 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 478,912B, BPFP=0.9090 +⌛️ [2/4] FRONTEND: Frontend time: 0.574s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.032s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12386809 87.73624271 + layer.39.0 312.00962707 1729.90706997 + ------------------------------------------------------------------------------------- + TOTAL 156.06674758 908.82165634 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 729560 +BPFP 0.6924 bits/point +EBPFP 0.6924 equivalent bits/point +MSE 908.821656 +---------------------- -------------------------------------------------------- +Time: 1.657s Load: 0.051s, Pack+Encode: 0.574s, Decode+Unpack: 1.032s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 908.8217 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02843684-cartoon_9.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02843684-cartoon_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02883205-sketch_15.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02883205-sketch_15.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 198,960B, BPFP=0.3776 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 366,548B, BPFP=0.6957 +⌛️ [2/4] FRONTEND: Frontend time: 0.560s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.016s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09796664 50.85359724 + layer.39.0 103.64267493 2123.39528669 + ------------------------------------------------------------------------------------- + TOTAL 51.87032078 1087.12444196 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 565508 +BPFP 0.5367 bits/point +EBPFP 0.5367 equivalent bits/point +MSE 1087.124442 +---------------------- -------------------------------------------------------- +Time: 1.629s Load: 0.052s, Pack+Encode: 0.560s, Decode+Unpack: 1.016s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1087.1244 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02883205-sketch_15.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02883205-sketch_15.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02906734-graffiti_3.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02906734-graffiti_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 339,876B, BPFP=0.6451 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 506,340B, BPFP=0.9611 +⌛️ [2/4] FRONTEND: Frontend time: 0.525s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.010s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.17339475 1316.41909621 + layer.39.0 166.12656402 2625.80442177 + ------------------------------------------------------------------------------------- + TOTAL 83.14997939 1971.11175899 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 846216 +BPFP 0.8031 bits/point +EBPFP 0.8031 equivalent bits/point +MSE 1971.111759 +---------------------- -------------------------------------------------------- +Time: 1.586s Load: 0.050s, Pack+Encode: 0.525s, Decode+Unpack: 1.010s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1971.1118 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02906734-graffiti_3.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02906734-graffiti_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02909870-sketch_0.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02909870-sketch_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 4.541s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 217,932B, BPFP=0.4137 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 404,832B, BPFP=0.7684 +⌛️ [2/4] FRONTEND: Frontend time: 0.507s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.024s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.15317524 231.31631135 + layer.39.0 167.75886783 1752.61601069 + ------------------------------------------------------------------------------------- + TOTAL 83.95602154 991.96616102 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 622764 +BPFP 0.5910 bits/point +EBPFP 0.5910 equivalent bits/point +MSE 991.966161 +---------------------- -------------------------------------------------------- +Time: 6.072s Load: 4.541s, Pack+Encode: 0.507s, Decode+Unpack: 1.024s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 991.9662 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02909870-sketch_0.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02909870-sketch_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02939185-painting_0.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02939185-painting_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 192,688B, BPFP=0.3657 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 441,408B, BPFP=0.8378 +⌛️ [2/4] FRONTEND: Frontend time: 0.571s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.998s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09512242 24.83298029 + layer.39.0 131.28711127 2102.40767736 + ------------------------------------------------------------------------------------- + TOTAL 65.69111684 1063.62032882 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 634096 +BPFP 0.6018 bits/point +EBPFP 0.6018 equivalent bits/point +MSE 1063.620329 +---------------------- -------------------------------------------------------- +Time: 1.619s Load: 0.051s, Pack+Encode: 0.571s, Decode+Unpack: 0.998s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1063.6203 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02939185-painting_0.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02939185-painting_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02948072-misc_10.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02948072-misc_10.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 200,896B, BPFP=0.3813 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 498,288B, BPFP=0.9458 +⌛️ [2/4] FRONTEND: Frontend time: 0.556s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.994s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09566823 12.80516107 + layer.39.0 102.81622783 2427.70019436 + ------------------------------------------------------------------------------------- + TOTAL 51.45594803 1220.25267772 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 699184 +BPFP 0.6636 bits/point +EBPFP 0.6636 equivalent bits/point +MSE 1220.252678 +---------------------- -------------------------------------------------------- +Time: 1.600s Load: 0.051s, Pack+Encode: 0.556s, Decode+Unpack: 0.994s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1220.2527 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02948072-misc_10.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02948072-misc_10.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02950826-art_1.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02950826-art_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 211,220B, BPFP=0.4009 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 455,560B, BPFP=0.8647 +⌛️ [2/4] FRONTEND: Frontend time: 0.582s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.025s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09506074 26.28468932 + layer.39.0 1071.96149174 3544.40281827 + ------------------------------------------------------------------------------------- + TOTAL 536.02827624 1785.34375380 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 666780 +BPFP 0.6328 bits/point +EBPFP 0.6328 equivalent bits/point +MSE 1785.343754 +---------------------- -------------------------------------------------------- +Time: 1.658s Load: 0.051s, Pack+Encode: 0.582s, Decode+Unpack: 1.025s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1785.3438 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02950826-art_1.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02950826-art_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02951358-misc_1.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02951358-misc_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 176,940B, BPFP=0.3358 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 304,232B, BPFP=0.5775 +⌛️ [2/4] FRONTEND: Frontend time: 0.551s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.017s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09568294 12.72492351 + layer.39.0 598.97078474 2126.40476190 + ------------------------------------------------------------------------------------- + TOTAL 299.53323384 1069.56484271 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 481172 +BPFP 0.4567 bits/point +EBPFP 0.4567 equivalent bits/point +MSE 1069.564843 +---------------------- -------------------------------------------------------- +Time: 1.619s Load: 0.051s, Pack+Encode: 0.551s, Decode+Unpack: 1.017s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1069.5648 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02951358-misc_1.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02951358-misc_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02966193-cartoon_22.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02966193-cartoon_22.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 293,492B, BPFP=0.5571 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 434,772B, BPFP=0.8252 +⌛️ [2/4] FRONTEND: Frontend time: 0.534s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.017s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10376222 618.23311467 + layer.39.0 767.85532070 3546.20845481 + ------------------------------------------------------------------------------------- + TOTAL 383.97954146 2082.22078474 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 728264 +BPFP 0.6912 bits/point +EBPFP 0.6912 equivalent bits/point +MSE 2082.220785 +---------------------- -------------------------------------------------------- +Time: 1.602s Load: 0.051s, Pack+Encode: 0.534s, Decode+Unpack: 1.017s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2082.2208 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02966193-cartoon_22.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02966193-cartoon_22.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02980441-graphic_3.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02980441-graphic_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 178,644B, BPFP=0.3391 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 333,368B, BPFP=0.6328 +⌛️ [2/4] FRONTEND: Frontend time: 0.555s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.993s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09509088 24.64027386 + layer.39.0 13.13791359 1349.86868319 + ------------------------------------------------------------------------------------- + TOTAL 6.61650224 687.25447852 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 512012 +BPFP 0.4859 bits/point +EBPFP 0.4859 equivalent bits/point +MSE 687.254479 +---------------------- -------------------------------------------------------- +Time: 1.599s Load: 0.051s, Pack+Encode: 0.555s, Decode+Unpack: 0.993s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 687.2545 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02980441-graphic_3.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02980441-graphic_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03124170-painting_15.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03124170-painting_15.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 249,804B, BPFP=0.4741 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 457,072B, BPFP=0.8676 +⌛️ [2/4] FRONTEND: Frontend time: 0.547s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.017s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10783903 63.32610544 + layer.39.0 326.57091229 4061.36248785 + ------------------------------------------------------------------------------------- + TOTAL 163.33937566 2062.34429665 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 706876 +BPFP 0.6709 bits/point +EBPFP 0.6709 equivalent bits/point +MSE 2062.344297 +---------------------- -------------------------------------------------------- +Time: 1.616s Load: 0.051s, Pack+Encode: 0.547s, Decode+Unpack: 1.017s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2062.3443 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03124170-painting_15.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03124170-painting_15.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03345487-toy_17.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03345487-toy_17.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 204,944B, BPFP=0.3890 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 379,608B, BPFP=0.7205 +⌛️ [2/4] FRONTEND: Frontend time: 0.512s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.983s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10662318 61.71901573 + layer.39.0 198.63900024 2722.42784257 + ------------------------------------------------------------------------------------- + TOTAL 99.37281171 1392.07342915 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 584552 +BPFP 0.5548 bits/point +EBPFP 0.5548 equivalent bits/point +MSE 1392.073429 +---------------------- -------------------------------------------------------- +Time: 1.546s Load: 0.051s, Pack+Encode: 0.512s, Decode+Unpack: 0.983s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1392.0734 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03345487-toy_17.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03345487-toy_17.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03372029-sketch_14.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03372029-sketch_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 243,128B, BPFP=0.4615 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 498,576B, BPFP=0.9463 +⌛️ [2/4] FRONTEND: Frontend time: 0.569s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.009s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12162214 149.29982082 + layer.39.0 228.06095117 2517.71744412 + ------------------------------------------------------------------------------------- + TOTAL 114.09128665 1333.50863247 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 741704 +BPFP 0.7039 bits/point +EBPFP 0.7039 equivalent bits/point +MSE 1333.508632 +---------------------- -------------------------------------------------------- +Time: 1.629s Load: 0.051s, Pack+Encode: 0.569s, Decode+Unpack: 1.009s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1333.5086 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03372029-sketch_14.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03372029-sketch_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03424325-misc_4.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03424325-misc_4.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 237,276B, BPFP=0.4504 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 466,008B, BPFP=0.8845 +⌛️ [2/4] FRONTEND: Frontend time: 0.576s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.029s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10761499 99.34211006 + layer.39.0 21.03287666 2000.16520894 + ------------------------------------------------------------------------------------- + TOTAL 10.57024582 1049.75365950 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 703284 +BPFP 0.6674 bits/point +EBPFP 0.6674 equivalent bits/point +MSE 1049.753659 +---------------------- -------------------------------------------------------- +Time: 1.658s Load: 0.052s, Pack+Encode: 0.576s, Decode+Unpack: 1.029s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1049.7537 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03424325-misc_4.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03424325-misc_4.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03467068-sketch_17.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03467068-sketch_17.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 198,316B, BPFP=0.3764 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 503,392B, BPFP=0.9555 +⌛️ [2/4] FRONTEND: Frontend time: 0.519s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.983s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09564773 74.21355685 + layer.39.0 208.14688107 2131.07288630 + ------------------------------------------------------------------------------------- + TOTAL 104.12126440 1102.64322157 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 701708 +BPFP 0.6659 bits/point +EBPFP 0.6659 equivalent bits/point +MSE 1102.643222 +---------------------- -------------------------------------------------------- +Time: 1.572s Load: 0.070s, Pack+Encode: 0.519s, Decode+Unpack: 0.983s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1102.6432 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03467068-sketch_17.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03467068-sketch_17.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03481172-sketch_9.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03481172-sketch_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 199,404B, BPFP=0.3785 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 428,124B, BPFP=0.8126 +⌛️ [2/4] FRONTEND: Frontend time: 0.551s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.997s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14641065 62.60087919 + layer.39.0 516.28267736 2191.90257532 + ------------------------------------------------------------------------------------- + TOTAL 258.21454400 1127.25172725 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 627528 +BPFP 0.5955 bits/point +EBPFP 0.5955 equivalent bits/point +MSE 1127.251727 +---------------------- -------------------------------------------------------- +Time: 1.619s Load: 0.071s, Pack+Encode: 0.551s, Decode+Unpack: 0.997s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1127.2517 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03481172-sketch_9.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03481172-sketch_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03494278-deviantart_3.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03494278-deviantart_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 173,068B, BPFP=0.3285 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 364,356B, BPFP=0.6916 +⌛️ [2/4] FRONTEND: Frontend time: 0.541s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.990s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09714438 12.70727420 + layer.39.0 11.38600982 1812.78024781 + ------------------------------------------------------------------------------------- + TOTAL 5.74157710 912.74376101 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 537424 +BPFP 0.5100 bits/point +EBPFP 0.5100 equivalent bits/point +MSE 912.743761 +---------------------- -------------------------------------------------------- +Time: 1.583s Load: 0.052s, Pack+Encode: 0.541s, Decode+Unpack: 0.990s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 912.7438 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03494278-deviantart_3.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03494278-deviantart_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03495258-painting_3.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03495258-painting_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 244,160B, BPFP=0.4634 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 446,732B, BPFP=0.8479 +⌛️ [2/4] FRONTEND: Frontend time: 0.564s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.014s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10398556 112.26150996 + layer.39.0 359.17207240 3046.27793975 + ------------------------------------------------------------------------------------- + TOTAL 179.63802898 1579.26972485 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 690892 +BPFP 0.6557 bits/point +EBPFP 0.6557 equivalent bits/point +MSE 1579.269725 +---------------------- -------------------------------------------------------- +Time: 1.649s Load: 0.070s, Pack+Encode: 0.564s, Decode+Unpack: 1.014s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1579.2697 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03495258-painting_3.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03495258-painting_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03498962-sketch_8.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03498962-sketch_8.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 268,820B, BPFP=0.5102 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 478,268B, BPFP=0.9078 +⌛️ [2/4] FRONTEND: Frontend time: 0.513s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.986s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.16074808 390.43136540 + layer.39.0 476.99061589 2547.16618076 + ------------------------------------------------------------------------------------- + TOTAL 238.57568198 1468.79877308 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 747088 +BPFP 0.7090 bits/point +EBPFP 0.7090 equivalent bits/point +MSE 1468.798773 +---------------------- -------------------------------------------------------- +Time: 1.551s Load: 0.052s, Pack+Encode: 0.513s, Decode+Unpack: 0.986s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1468.7988 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03498962-sketch_8.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03498962-sketch_8.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03602883-misc_12.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03602883-misc_12.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 166,520B, BPFP=0.3161 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 331,800B, BPFP=0.6298 +⌛️ [2/4] FRONTEND: Frontend time: 0.506s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.981s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.09080038 12.85563768 + layer.39.0 100.93773536 1785.77368805 + ------------------------------------------------------------------------------------- + TOTAL 54.51426787 899.31466286 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 498320 +BPFP 0.4729 bits/point +EBPFP 0.4729 equivalent bits/point +MSE 899.314663 +---------------------- -------------------------------------------------------- +Time: 1.536s Load: 0.050s, Pack+Encode: 0.506s, Decode+Unpack: 0.981s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 899.3147 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03602883-misc_12.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03602883-misc_12.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03630383-toy_1.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03630383-toy_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 186,484B, BPFP=0.3540 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 450,648B, BPFP=0.8554 +⌛️ [2/4] FRONTEND: Frontend time: 0.570s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.006s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09574974 12.73414723 + layer.39.0 14.66923857 1854.48955296 + ------------------------------------------------------------------------------------- + TOTAL 7.38249415 933.61185010 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 637132 +BPFP 0.6047 bits/point +EBPFP 0.6047 equivalent bits/point +MSE 933.611850 +---------------------- -------------------------------------------------------- +Time: 1.628s Load: 0.051s, Pack+Encode: 0.570s, Decode+Unpack: 1.006s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 933.6119 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03630383-toy_1.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03630383-toy_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03649909-toy_22.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03649909-toy_22.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 179,632B, BPFP=0.3410 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 287,376B, BPFP=0.5455 +⌛️ [2/4] FRONTEND: Frontend time: 0.510s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.976s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09878858 24.87650518 + layer.39.0 29.68475348 1375.31462585 + ------------------------------------------------------------------------------------- + TOTAL 14.89177103 700.09556551 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 467008 +BPFP 0.4432 bits/point +EBPFP 0.4432 equivalent bits/point +MSE 700.095566 +---------------------- -------------------------------------------------------- +Time: 1.535s Load: 0.050s, Pack+Encode: 0.510s, Decode+Unpack: 0.976s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 700.0956 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03649909-toy_22.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03649909-toy_22.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03676483-sculpture_3.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03676483-sculpture_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 193,464B, BPFP=0.3672 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 450,968B, BPFP=0.8560 +⌛️ [2/4] FRONTEND: Frontend time: 0.608s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.062s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09491264 12.71225477 + layer.39.0 32.22669916 2644.43756074 + ------------------------------------------------------------------------------------- + TOTAL 16.16080590 1328.57490775 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 644432 +BPFP 0.6116 bits/point +EBPFP 0.6116 equivalent bits/point +MSE 1328.574908 +---------------------- -------------------------------------------------------- +Time: 1.721s Load: 0.051s, Pack+Encode: 0.608s, Decode+Unpack: 1.062s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1328.5749 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03676483-sculpture_3.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03676483-sculpture_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03710193-sketch_14.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03710193-sketch_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.054s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 204,948B, BPFP=0.3890 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 443,160B, BPFP=0.8412 +⌛️ [2/4] FRONTEND: Frontend time: 0.577s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.992s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.47394152 73.96983570 + layer.39.0 335.99814747 2149.19484937 + ------------------------------------------------------------------------------------- + TOTAL 168.23604450 1111.58234254 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 648108 +BPFP 0.6151 bits/point +EBPFP 0.6151 equivalent bits/point +MSE 1111.582343 +---------------------- -------------------------------------------------------- +Time: 1.623s Load: 0.054s, Pack+Encode: 0.577s, Decode+Unpack: 0.992s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1111.5823 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03710193-sketch_14.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03710193-sketch_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03773504-graphic_4.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03773504-graphic_4.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 176,552B, BPFP=0.3351 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 308,160B, BPFP=0.5849 +⌛️ [2/4] FRONTEND: Frontend time: 0.562s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.988s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09681199 12.91770966 + layer.39.0 18.83313593 1436.75595238 + ------------------------------------------------------------------------------------- + TOTAL 9.46497396 724.83683102 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 484712 +BPFP 0.4600 bits/point +EBPFP 0.4600 equivalent bits/point +MSE 724.836831 +---------------------- -------------------------------------------------------- +Time: 1.601s Load: 0.051s, Pack+Encode: 0.562s, Decode+Unpack: 0.988s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 724.8368 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03773504-graphic_4.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03773504-graphic_4.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03775071-painting_1.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03775071-painting_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 220,052B, BPFP=0.4177 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 363,980B, BPFP=0.6909 +⌛️ [2/4] FRONTEND: Frontend time: 0.554s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.015s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11048905 39.03840197 + layer.39.0 386.73560496 2505.13362488 + ------------------------------------------------------------------------------------- + TOTAL 193.42304701 1272.08601342 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 584032 +BPFP 0.5543 bits/point +EBPFP 0.5543 equivalent bits/point +MSE 1272.086013 +---------------------- -------------------------------------------------------- +Time: 1.620s Load: 0.051s, Pack+Encode: 0.554s, Decode+Unpack: 1.015s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1272.0860 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03775071-painting_1.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03775071-painting_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03888257-cartoon_30.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03888257-cartoon_30.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 226,628B, BPFP=0.4302 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 439,360B, BPFP=0.8339 +⌛️ [2/4] FRONTEND: Frontend time: 0.553s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.043s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13203045 97.95811316 + layer.39.0 375.96832483 2332.42565598 + ------------------------------------------------------------------------------------- + TOTAL 188.05017764 1215.19188457 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 665988 +BPFP 0.6320 bits/point +EBPFP 0.6320 equivalent bits/point +MSE 1215.191885 +---------------------- -------------------------------------------------------- +Time: 1.647s Load: 0.052s, Pack+Encode: 0.553s, Decode+Unpack: 1.043s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1215.1919 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03888257-cartoon_30.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03888257-cartoon_30.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03930630-toy_2.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03930630-toy_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 175,480B, BPFP=0.3331 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 374,072B, BPFP=0.7100 +⌛️ [2/4] FRONTEND: Frontend time: 0.523s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.016s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09699417 12.84689702 + layer.39.0 46.17573949 1878.58527697 + ------------------------------------------------------------------------------------- + TOTAL 23.13636683 945.71608699 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 549552 +BPFP 0.5215 bits/point +EBPFP 0.5215 equivalent bits/point +MSE 945.716087 +---------------------- -------------------------------------------------------- +Time: 1.589s Load: 0.050s, Pack+Encode: 0.523s, Decode+Unpack: 1.016s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 945.7161 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03930630-toy_2.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03930630-toy_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04086273-sticker_5.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04086273-sticker_5.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 186,164B, BPFP=0.3534 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 297,008B, BPFP=0.5637 +⌛️ [2/4] FRONTEND: Frontend time: 0.502s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.991s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10161624 25.34114014 + layer.39.0 24.98063198 1489.02137998 + ------------------------------------------------------------------------------------- + TOTAL 12.54112411 757.18126006 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 483172 +BPFP 0.4585 bits/point +EBPFP 0.4585 equivalent bits/point +MSE 757.181260 +---------------------- -------------------------------------------------------- +Time: 1.544s Load: 0.052s, Pack+Encode: 0.502s, Decode+Unpack: 0.991s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 757.1813 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04086273-sticker_5.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04086273-sticker_5.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04118538-sculpture_0.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04118538-sculpture_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 219,468B, BPFP=0.4166 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 497,348B, BPFP=0.9440 +⌛️ [2/4] FRONTEND: Frontend time: 0.572s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.014s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09846411 25.61842884 + layer.39.0 11.87055944 2300.39917396 + ------------------------------------------------------------------------------------- + TOTAL 5.98451177 1163.00880140 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 716816 +BPFP 0.6803 bits/point +EBPFP 0.6803 equivalent bits/point +MSE 1163.008801 +---------------------- -------------------------------------------------------- +Time: 1.637s Load: 0.050s, Pack+Encode: 0.572s, Decode+Unpack: 1.014s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1163.0088 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04118538-sculpture_0.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04118538-sculpture_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04133789-cartoon_7.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04133789-cartoon_7.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 288,472B, BPFP=0.5475 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 467,060B, BPFP=0.8865 +⌛️ [2/4] FRONTEND: Frontend time: 0.546s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.020s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13739287 368.88751215 + layer.39.0 370.52532799 2736.09329446 + ------------------------------------------------------------------------------------- + TOTAL 185.33136043 1552.49040330 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 755532 +BPFP 0.7170 bits/point +EBPFP 0.7170 equivalent bits/point +MSE 1552.490403 +---------------------- -------------------------------------------------------- +Time: 1.617s Load: 0.051s, Pack+Encode: 0.546s, Decode+Unpack: 1.020s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1552.4904 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04133789-cartoon_7.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04133789-cartoon_7.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04141076-cartoon_14.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04141076-cartoon_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 198,896B, BPFP=0.3775 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 436,080B, BPFP=0.8277 +⌛️ [2/4] FRONTEND: Frontend time: 0.511s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.984s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11960477 86.14783011 + layer.39.0 53.25505649 1916.44023324 + ------------------------------------------------------------------------------------- + TOTAL 26.68733063 1001.29403168 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 634976 +BPFP 0.6026 bits/point +EBPFP 0.6026 equivalent bits/point +MSE 1001.294032 +---------------------- -------------------------------------------------------- +Time: 1.547s Load: 0.052s, Pack+Encode: 0.511s, Decode+Unpack: 0.984s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1001.2940 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04141076-cartoon_14.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04141076-cartoon_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04146614-misc_4.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04146614-misc_4.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 204,292B, BPFP=0.3878 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 393,632B, BPFP=0.7471 +⌛️ [2/4] FRONTEND: Frontend time: 0.572s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.020s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10047569 48.80333607 + layer.39.0 167.29959305 2491.43561710 + ------------------------------------------------------------------------------------- + TOTAL 83.70003437 1270.11947659 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 597924 +BPFP 0.5675 bits/point +EBPFP 0.5675 equivalent bits/point +MSE 1270.119477 +---------------------- -------------------------------------------------------- +Time: 1.644s Load: 0.052s, Pack+Encode: 0.572s, Decode+Unpack: 1.020s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1270.1195 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04146614-misc_4.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04146614-misc_4.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04147183-art_9.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04147183-art_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 206,380B, BPFP=0.3917 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 331,532B, BPFP=0.6293 +⌛️ [2/4] FRONTEND: Frontend time: 0.560s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.009s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11332939 148.33673469 + layer.39.0 22.95352360 1953.90536929 + ------------------------------------------------------------------------------------- + TOTAL 11.53342649 1051.12105199 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 537912 +BPFP 0.5105 bits/point +EBPFP 0.5105 equivalent bits/point +MSE 1051.121052 +---------------------- -------------------------------------------------------- +Time: 1.619s Load: 0.050s, Pack+Encode: 0.560s, Decode+Unpack: 1.009s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1051.1211 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04147183-art_9.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04147183-art_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04192698-videogame_16.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04192698-videogame_16.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 214,720B, BPFP=0.4076 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 481,744B, BPFP=0.9144 +⌛️ [2/4] FRONTEND: Frontend time: 0.577s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.013s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09706018 13.75403817 + layer.39.0 404.66927843 2170.92614189 + ------------------------------------------------------------------------------------- + TOTAL 202.38316930 1092.34009003 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 696464 +BPFP 0.6610 bits/point +EBPFP 0.6610 equivalent bits/point +MSE 1092.340090 +---------------------- -------------------------------------------------------- +Time: 1.641s Load: 0.051s, Pack+Encode: 0.577s, Decode+Unpack: 1.013s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1092.3401 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04192698-videogame_16.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04192698-videogame_16.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04254680-deviantart_17.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04254680-deviantart_17.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 201,892B, BPFP=0.3832 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 461,968B, BPFP=0.8769 +⌛️ [2/4] FRONTEND: Frontend time: 0.575s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.008s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10685510 25.42349976 + layer.39.0 151.81593173 2062.59961127 + ------------------------------------------------------------------------------------- + TOTAL 75.96139341 1044.01155552 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 663860 +BPFP 0.6300 bits/point +EBPFP 0.6300 equivalent bits/point +MSE 1044.011556 +---------------------- -------------------------------------------------------- +Time: 1.634s Load: 0.052s, Pack+Encode: 0.575s, Decode+Unpack: 1.008s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1044.0116 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04254680-deviantart_17.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04254680-deviantart_17.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04266014-painting_13.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04266014-painting_13.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 197,108B, BPFP=0.3741 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 316,232B, BPFP=0.6002 +⌛️ [2/4] FRONTEND: Frontend time: 0.562s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.991s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09568562 38.25618015 + layer.39.0 29.62437363 1769.27526725 + ------------------------------------------------------------------------------------- + TOTAL 14.86002963 903.76572370 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 513340 +BPFP 0.4872 bits/point +EBPFP 0.4872 equivalent bits/point +MSE 903.765724 +---------------------- -------------------------------------------------------- +Time: 1.602s Load: 0.050s, Pack+Encode: 0.562s, Decode+Unpack: 0.991s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 903.7657 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04266014-painting_13.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04266014-painting_13.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04310018-art_3.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04310018-art_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 253,636B, BPFP=0.4814 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 330,900B, BPFP=0.6281 +⌛️ [2/4] FRONTEND: Frontend time: 0.501s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.984s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13375617 528.33831390 + layer.39.0 75.24515610 2103.82896016 + ------------------------------------------------------------------------------------- + TOTAL 37.68945614 1316.08363703 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 584536 +BPFP 0.5547 bits/point +EBPFP 0.5547 equivalent bits/point +MSE 1316.083637 +---------------------- -------------------------------------------------------- +Time: 1.536s Load: 0.051s, Pack+Encode: 0.501s, Decode+Unpack: 0.984s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1316.0836 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04310018-art_3.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04310018-art_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04347754-sculpture_0.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04347754-sculpture_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 281,968B, BPFP=0.5352 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 442,620B, BPFP=0.8401 +⌛️ [2/4] FRONTEND: Frontend time: 0.573s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.015s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14257451 763.20675413 + layer.39.0 394.23636419 2012.60507775 + ------------------------------------------------------------------------------------- + TOTAL 197.18946935 1387.90591594 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 724588 +BPFP 0.6877 bits/point +EBPFP 0.6877 equivalent bits/point +MSE 1387.905916 +---------------------- -------------------------------------------------------- +Time: 1.640s Load: 0.051s, Pack+Encode: 0.573s, Decode+Unpack: 1.015s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1387.9059 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04347754-sculpture_0.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04347754-sculpture_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04409515-deviantart_1.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04409515-deviantart_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 181,084B, BPFP=0.3437 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 321,460B, BPFP=0.6102 +⌛️ [2/4] FRONTEND: Frontend time: 0.497s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.983s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09627266 13.20493577 + layer.39.0 9.33068077 1859.70335277 + ------------------------------------------------------------------------------------- + TOTAL 4.71347671 936.45414427 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 502544 +BPFP 0.4769 bits/point +EBPFP 0.4769 equivalent bits/point +MSE 936.454144 +---------------------- -------------------------------------------------------- +Time: 1.529s Load: 0.050s, Pack+Encode: 0.497s, Decode+Unpack: 0.983s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 936.4541 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04409515-deviantart_1.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04409515-deviantart_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04487394-deviantart_8.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04487394-deviantart_8.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 221,252B, BPFP=0.4200 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 419,316B, BPFP=0.7959 +⌛️ [2/4] FRONTEND: Frontend time: 0.544s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.991s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09911632 171.67632410 + layer.39.0 99.63155977 2322.09766764 + ------------------------------------------------------------------------------------- + TOTAL 49.86533804 1246.88699587 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 640568 +BPFP 0.6079 bits/point +EBPFP 0.6079 equivalent bits/point +MSE 1246.886996 +---------------------- -------------------------------------------------------- +Time: 1.586s Load: 0.050s, Pack+Encode: 0.544s, Decode+Unpack: 0.991s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1246.8870 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04487394-deviantart_8.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04487394-deviantart_8.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04522168-painting_32.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04522168-painting_32.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 190,392B, BPFP=0.3614 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 428,308B, BPFP=0.8130 +⌛️ [2/4] FRONTEND: Frontend time: 0.507s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.981s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11740584 123.48329689 + layer.39.0 10.95138066 1517.63799806 + ------------------------------------------------------------------------------------- + TOTAL 5.53439325 820.56064747 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 618700 +BPFP 0.5872 bits/point +EBPFP 0.5872 equivalent bits/point +MSE 820.560647 +---------------------- -------------------------------------------------------- +Time: 1.538s Load: 0.050s, Pack+Encode: 0.507s, Decode+Unpack: 0.981s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 820.5606 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04522168-painting_32.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04522168-painting_32.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04591713-painting_14.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04591713-painting_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 259,828B, BPFP=0.4932 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 485,716B, BPFP=0.9219 +⌛️ [2/4] FRONTEND: Frontend time: 0.555s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.012s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11212821 63.53393009 + layer.39.0 165.22564383 1947.60386297 + ------------------------------------------------------------------------------------- + TOTAL 82.66888602 1005.56889653 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 745544 +BPFP 0.7076 bits/point +EBPFP 0.7076 equivalent bits/point +MSE 1005.568897 +---------------------- -------------------------------------------------------- +Time: 1.619s Load: 0.052s, Pack+Encode: 0.555s, Decode+Unpack: 1.012s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1005.5689 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04591713-painting_14.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04591713-painting_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07693725-sketch_15.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07693725-sketch_15.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 228,492B, BPFP=0.4337 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 395,164B, BPFP=0.7501 +⌛️ [2/4] FRONTEND: Frontend time: 0.543s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.022s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10569874 161.48258321 + layer.39.0 214.96065658 2634.06316812 + ------------------------------------------------------------------------------------- + TOTAL 107.53317766 1397.77287567 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 623656 +BPFP 0.5919 bits/point +EBPFP 0.5919 equivalent bits/point +MSE 1397.772876 +---------------------- -------------------------------------------------------- +Time: 1.616s Load: 0.051s, Pack+Encode: 0.543s, Decode+Unpack: 1.022s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1397.7729 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07693725-sketch_15.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07693725-sketch_15.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07695742-videogame_1.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07695742-videogame_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 263,804B, BPFP=0.5007 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 435,576B, BPFP=0.8268 +⌛️ [2/4] FRONTEND: Frontend time: 0.548s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.024s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12460778 246.26302843 + layer.39.0 438.29433916 2395.96550049 + ------------------------------------------------------------------------------------- + TOTAL 219.20947347 1321.11426446 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 699380 +BPFP 0.6637 bits/point +EBPFP 0.6637 equivalent bits/point +MSE 1321.114264 +---------------------- -------------------------------------------------------- +Time: 1.622s Load: 0.051s, Pack+Encode: 0.548s, Decode+Unpack: 1.024s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1321.1143 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07695742-videogame_1.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07695742-videogame_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697313-deviantart_7.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697313-deviantart_7.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 194,620B, BPFP=0.3694 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 301,744B, BPFP=0.5727 +⌛️ [2/4] FRONTEND: Frontend time: 0.554s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.001s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09520741 13.04066923 + layer.39.0 14.69109212 2248.99757046 + ------------------------------------------------------------------------------------- + TOTAL 7.39314977 1131.01911984 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 496364 +BPFP 0.4711 bits/point +EBPFP 0.4711 equivalent bits/point +MSE 1131.019120 +---------------------- -------------------------------------------------------- +Time: 1.606s Load: 0.051s, Pack+Encode: 0.554s, Decode+Unpack: 1.001s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1131.0191 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697313-deviantart_7.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07697313-deviantart_7.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697537-deviantart_16.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697537-deviantart_16.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 212,060B, BPFP=0.4025 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 423,388B, BPFP=0.8036 +⌛️ [2/4] FRONTEND: Frontend time: 0.572s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.041s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09755328 49.40701682 + layer.39.0 90.32537658 2164.15476190 + ------------------------------------------------------------------------------------- + TOTAL 45.21146493 1106.78088936 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 635448 +BPFP 0.6031 bits/point +EBPFP 0.6031 equivalent bits/point +MSE 1106.780889 +---------------------- -------------------------------------------------------- +Time: 1.684s Load: 0.071s, Pack+Encode: 0.572s, Decode+Unpack: 1.041s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1106.7809 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697537-deviantart_16.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07697537-deviantart_16.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714571-deviantart_0.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714571-deviantart_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 210,536B, BPFP=0.3996 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 450,412B, BPFP=0.8549 +⌛️ [2/4] FRONTEND: Frontend time: 0.555s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.016s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09528512 12.94001496 + layer.39.0 45.81401467 3060.27648202 + ------------------------------------------------------------------------------------- + TOTAL 22.95464989 1536.60824849 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 660948 +BPFP 0.6273 bits/point +EBPFP 0.6273 equivalent bits/point +MSE 1536.608248 +---------------------- -------------------------------------------------------- +Time: 1.640s Load: 0.069s, Pack+Encode: 0.555s, Decode+Unpack: 1.016s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1536.6082 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714571-deviantart_0.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07714571-deviantart_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714990-toy_14.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714990-toy_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 208,528B, BPFP=0.3958 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 425,992B, BPFP=0.8086 +⌛️ [2/4] FRONTEND: Frontend time: 0.530s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.996s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09793257 12.59042931 + layer.39.0 322.50334062 2970.62196307 + ------------------------------------------------------------------------------------- + TOTAL 161.30063660 1491.60619619 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 634520 +BPFP 0.6022 bits/point +EBPFP 0.6022 equivalent bits/point +MSE 1491.606196 +---------------------- -------------------------------------------------------- +Time: 1.577s Load: 0.051s, Pack+Encode: 0.530s, Decode+Unpack: 0.996s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1491.6062 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714990-toy_14.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07714990-toy_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07718472-cartoon_1.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07718472-cartoon_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 175,668B, BPFP=0.3334 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 293,276B, BPFP=0.5567 +⌛️ [2/4] FRONTEND: Frontend time: 0.570s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.004s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11235230 87.61022534 + layer.39.0 14.49942963 1542.23080661 + ------------------------------------------------------------------------------------- + TOTAL 7.30589096 814.92051597 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 468944 +BPFP 0.4450 bits/point +EBPFP 0.4450 equivalent bits/point +MSE 814.920516 +---------------------- -------------------------------------------------------- +Time: 1.625s Load: 0.051s, Pack+Encode: 0.570s, Decode+Unpack: 1.004s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 814.9205 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07718472-cartoon_1.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07718472-cartoon_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07742313-deviantart_8.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07742313-deviantart_8.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 170,228B, BPFP=0.3231 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 312,144B, BPFP=0.5925 +⌛️ [2/4] FRONTEND: Frontend time: 0.525s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.980s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09669835 12.91383853 + layer.39.0 8.77690150 1675.52611759 + ------------------------------------------------------------------------------------- + TOTAL 4.43679992 844.21997806 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 482372 +BPFP 0.4578 bits/point +EBPFP 0.4578 equivalent bits/point +MSE 844.219978 +---------------------- -------------------------------------------------------- +Time: 1.556s Load: 0.051s, Pack+Encode: 0.525s, Decode+Unpack: 0.980s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 844.2200 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07742313-deviantart_8.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07742313-deviantart_8.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07749582-sticker_0.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07749582-sticker_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 217,300B, BPFP=0.4125 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 464,340B, BPFP=0.8814 +⌛️ [2/4] FRONTEND: Frontend time: 0.514s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.029s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09550123 24.60117339 + layer.39.0 34.64631545 2950.49295432 + ------------------------------------------------------------------------------------- + TOTAL 17.37090834 1487.54706386 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 681640 +BPFP 0.6469 bits/point +EBPFP 0.6469 equivalent bits/point +MSE 1487.547064 +---------------------- -------------------------------------------------------- +Time: 1.613s Load: 0.070s, Pack+Encode: 0.514s, Decode+Unpack: 1.029s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1487.5471 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07749582-sticker_0.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07749582-sticker_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07753275-painting_2.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07753275-painting_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 305,940B, BPFP=0.5807 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 360,600B, BPFP=0.6844 +⌛️ [2/4] FRONTEND: Frontend time: 0.502s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.998s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10429548 1154.52915452 + layer.39.0 540.43106171 3608.54373178 + ------------------------------------------------------------------------------------- + TOTAL 270.26767859 2381.53644315 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 666540 +BPFP 0.6326 bits/point +EBPFP 0.6326 equivalent bits/point +MSE 2381.536443 +---------------------- -------------------------------------------------------- +Time: 1.552s Load: 0.052s, Pack+Encode: 0.502s, Decode+Unpack: 0.998s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2381.5364 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07753275-painting_2.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07753275-painting_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07768694-painting_12.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07768694-painting_12.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 236,244B, BPFP=0.4484 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 411,260B, BPFP=0.7806 +⌛️ [2/4] FRONTEND: Frontend time: 0.578s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.062s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09821300 111.48668307 + layer.39.0 635.68343052 3210.77162293 + ------------------------------------------------------------------------------------- + TOTAL 317.89082176 1661.12915300 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 647504 +BPFP 0.6145 bits/point +EBPFP 0.6145 equivalent bits/point +MSE 1661.129153 +---------------------- -------------------------------------------------------- +Time: 1.691s Load: 0.051s, Pack+Encode: 0.578s, Decode+Unpack: 1.062s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1661.1292 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07768694-painting_12.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07768694-painting_12.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07920052-painting_1.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07920052-painting_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 217,652B, BPFP=0.4131 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 467,064B, BPFP=0.8865 +⌛️ [2/4] FRONTEND: Frontend time: 0.567s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.040s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09582097 49.02677433 + layer.39.0 9.59182155 2020.67018950 + ------------------------------------------------------------------------------------- + TOTAL 4.84382126 1034.84848192 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 684716 +BPFP 0.6498 bits/point +EBPFP 0.6498 equivalent bits/point +MSE 1034.848482 +---------------------- -------------------------------------------------------- +Time: 1.658s Load: 0.050s, Pack+Encode: 0.567s, Decode+Unpack: 1.040s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1034.8485 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07920052-painting_1.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07920052-painting_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09472597-painting_15.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09472597-painting_15.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 177,008B, BPFP=0.3360 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 296,972B, BPFP=0.5637 +⌛️ [2/4] FRONTEND: Frontend time: 0.595s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.010s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09164813 12.97350564 + layer.39.0 9.11265014 1663.62876579 + ------------------------------------------------------------------------------------- + TOTAL 4.60214913 838.30113572 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 473980 +BPFP 0.4498 bits/point +EBPFP 0.4498 equivalent bits/point +MSE 838.301136 +---------------------- -------------------------------------------------------- +Time: 1.655s Load: 0.050s, Pack+Encode: 0.595s, Decode+Unpack: 1.010s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 838.3011 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09472597-painting_15.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n09472597-painting_15.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09835506-videogame_3.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09835506-videogame_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 203,960B, BPFP=0.3871 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 417,108B, BPFP=0.7917 +⌛️ [2/4] FRONTEND: Frontend time: 0.519s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.983s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09585661 12.75143969 + layer.39.0 12.34450164 2194.18415938 + ------------------------------------------------------------------------------------- + TOTAL 6.22017912 1103.46779954 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 621068 +BPFP 0.5894 bits/point +EBPFP 0.5894 equivalent bits/point +MSE 1103.467800 +---------------------- -------------------------------------------------------- +Time: 1.553s Load: 0.051s, Pack+Encode: 0.519s, Decode+Unpack: 0.983s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1103.4678 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09835506-videogame_3.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n09835506-videogame_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n12267677-misc_105.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n12267677-misc_105.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 181,216B, BPFP=0.3440 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 466,452B, BPFP=0.8854 +⌛️ [2/4] FRONTEND: Frontend time: 0.531s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.995s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10166193 12.66037358 + layer.39.0 219.41089650 2259.31778426 + ------------------------------------------------------------------------------------- + TOTAL 109.75627921 1135.98907892 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 647668 +BPFP 0.6147 bits/point +EBPFP 0.6147 equivalent bits/point +MSE 1135.989079 +---------------------- -------------------------------------------------------- +Time: 1.578s Load: 0.052s, Pack+Encode: 0.531s, Decode+Unpack: 0.995s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1135.9891 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n12267677-misc_105.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n12267677-misc_105.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 0.6012 bits/point +Avg EBPFP 0.6012 equivalent bits/point +Avg MSE 1217.963109 +Avg Time 1.968s +------------------------ ---------------------------- diff --git a/lambda0.007/hyperprior-featurecoding-8bit-individual/cls_in1kval/dtufc_hyperprior-featurecoding_dinov3-total_individual.log b/lambda0.007/hyperprior-featurecoding-8bit-individual/cls_in1kval/dtufc_hyperprior-featurecoding_dinov3-total_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..68be137f16f7bfdda6577d17cdae214c29e68e64 --- /dev/null +++ b/lambda0.007/hyperprior-featurecoding-8bit-individual/cls_in1kval/dtufc_hyperprior-featurecoding_dinov3-total_individual.log @@ -0,0 +1,9344 @@ +Experiment: dtufc_hyperprior-featurecoding_dinov3-total_individual +Log file: output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/dtufc_hyperprior-featurecoding_dinov3-total_individual.log +DTUFCCodecConfig: + arch: hyperprior-featurecoding + handler: dinov3-total + checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.007_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.007_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 599 +Loaded hyperprior-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.9' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.19' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.29' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.39' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json +Loaded per-key mappings: model=dinov3-total + Keys: ['layer.9', 'layer.19', 'layer.29', 'layer.39'] +---------------- ------------------------------------------------------------------------------------------------------------------------------ +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +Checkpoint codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.007_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-DINOv3/imagenet1k-val +Output output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval +---------------- ------------------------------------------------------------------------------------------------------------------------------ +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02825657-ILSVRC2012_val_00001103.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02825657-ILSVRC2012_val_00001103.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.088s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 187,112B, BPFP=0.3552 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 467,460B, BPFP=0.8873 +⌛️ [2/4] FRONTEND: Frontend time: 0.778s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.078s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10264289 25.40818414 + layer.39.0 9.47367932 1712.41047133 + ------------------------------------------------------------------------------------- + TOTAL 4.78816110 868.90932774 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 654572 +BPFP 0.6212 bits/point +EBPFP 0.6212 equivalent bits/point +MSE 868.909328 +---------------------- -------------------------------------------------------- +Time: 1.944s Load: 0.088s, Pack+Encode: 0.778s, Decode+Unpack: 1.078s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 868.9093 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02825657-ILSVRC2012_val_00001103.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02825657-ILSVRC2012_val_00001103.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02834397-ILSVRC2012_val_00001252.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02834397-ILSVRC2012_val_00001252.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.079s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 266,900B, BPFP=0.5066 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 517,300B, BPFP=0.9819 +⌛️ [2/4] FRONTEND: Frontend time: 0.580s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.054s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14789204 674.78954082 + layer.39.0 415.43227648 2721.42954325 + ------------------------------------------------------------------------------------- + TOTAL 207.79008426 1698.10954203 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 784200 +BPFP 0.7442 bits/point +EBPFP 0.7442 equivalent bits/point +MSE 1698.109542 +---------------------- -------------------------------------------------------- +Time: 1.713s Load: 0.079s, Pack+Encode: 0.580s, Decode+Unpack: 1.054s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1698.1095 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02834397-ILSVRC2012_val_00001252.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02834397-ILSVRC2012_val_00001252.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02840245-ILSVRC2012_val_00003446.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02840245-ILSVRC2012_val_00003446.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.078s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 186,328B, BPFP=0.3537 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 339,000B, BPFP=0.6434 +⌛️ [2/4] FRONTEND: Frontend time: 0.542s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.030s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10761288 37.83973366 + layer.39.0 28.71820525 1578.45991254 + ------------------------------------------------------------------------------------- + TOTAL 14.41290906 808.14982310 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 525328 +BPFP 0.4986 bits/point +EBPFP 0.4986 equivalent bits/point +MSE 808.149823 +---------------------- -------------------------------------------------------- +Time: 1.651s Load: 0.078s, Pack+Encode: 0.542s, Decode+Unpack: 1.030s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 808.1498 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02840245-ILSVRC2012_val_00003446.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02840245-ILSVRC2012_val_00003446.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02843684-ILSVRC2012_val_00000514.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02843684-ILSVRC2012_val_00000514.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 214,956B, BPFP=0.4080 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 360,408B, BPFP=0.6841 +⌛️ [2/4] FRONTEND: Frontend time: 0.583s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.056s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11482661 74.69916940 + layer.39.0 84.54469600 2044.30490768 + ------------------------------------------------------------------------------------- + TOTAL 42.32976130 1059.50203854 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 575364 +BPFP 0.5460 bits/point +EBPFP 0.5460 equivalent bits/point +MSE 1059.502039 +---------------------- -------------------------------------------------------- +Time: 1.697s Load: 0.059s, Pack+Encode: 0.583s, Decode+Unpack: 1.056s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1059.5020 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02843684-ILSVRC2012_val_00000514.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02843684-ILSVRC2012_val_00000514.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02859443-ILSVRC2012_val_00000193.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02859443-ILSVRC2012_val_00000193.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 173,444B, BPFP=0.3292 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 250,612B, BPFP=0.4757 +⌛️ [2/4] FRONTEND: Frontend time: 0.535s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.031s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11417333 221.12446854 + layer.39.0 9.67809406 1522.22315355 + ------------------------------------------------------------------------------------- + TOTAL 4.89613370 871.67381104 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 424056 +BPFP 0.4024 bits/point +EBPFP 0.4024 equivalent bits/point +MSE 871.673811 +---------------------- -------------------------------------------------------- +Time: 1.618s Load: 0.052s, Pack+Encode: 0.535s, Decode+Unpack: 1.031s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 871.6738 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02859443-ILSVRC2012_val_00000193.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02859443-ILSVRC2012_val_00000193.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02860847-ILSVRC2012_val_00000601.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02860847-ILSVRC2012_val_00000601.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 249,216B, BPFP=0.4730 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 333,352B, BPFP=0.6327 +⌛️ [2/4] FRONTEND: Frontend time: 0.589s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.050s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12653054 373.52818270 + layer.39.0 266.35249636 2069.07264334 + ------------------------------------------------------------------------------------- + TOTAL 133.23951345 1221.30041302 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 582568 +BPFP 0.5529 bits/point +EBPFP 0.5529 equivalent bits/point +MSE 1221.300413 +---------------------- -------------------------------------------------------- +Time: 1.689s Load: 0.050s, Pack+Encode: 0.589s, Decode+Unpack: 1.050s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1221.3004 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02860847-ILSVRC2012_val_00000601.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02860847-ILSVRC2012_val_00000601.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02865351-ILSVRC2012_val_00000763.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02865351-ILSVRC2012_val_00000763.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 183,252B, BPFP=0.3478 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 417,364B, BPFP=0.7922 +⌛️ [2/4] FRONTEND: Frontend time: 0.587s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.081s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09467571 25.48961370 + layer.39.0 15.47581086 2302.96185617 + ------------------------------------------------------------------------------------- + TOTAL 7.78524328 1164.22573494 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 600616 +BPFP 0.5700 bits/point +EBPFP 0.5700 equivalent bits/point +MSE 1164.225735 +---------------------- -------------------------------------------------------- +Time: 1.749s Load: 0.080s, Pack+Encode: 0.587s, Decode+Unpack: 1.081s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1164.2257 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02865351-ILSVRC2012_val_00000763.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02865351-ILSVRC2012_val_00000763.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02869837-ILSVRC2012_val_00000906.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02869837-ILSVRC2012_val_00000906.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 228,688B, BPFP=0.4341 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 478,648B, BPFP=0.9085 +⌛️ [2/4] FRONTEND: Frontend time: 0.582s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.072s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09659988 49.94924153 + layer.39.0 16.39405483 2244.52988338 + ------------------------------------------------------------------------------------- + TOTAL 8.24532736 1147.23956245 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 707336 +BPFP 0.6713 bits/point +EBPFP 0.6713 equivalent bits/point +MSE 1147.239562 +---------------------- -------------------------------------------------------- +Time: 1.725s Load: 0.070s, Pack+Encode: 0.582s, Decode+Unpack: 1.072s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1147.2396 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02869837-ILSVRC2012_val_00000906.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02869837-ILSVRC2012_val_00000906.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02870880-ILSVRC2012_val_00003274.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02870880-ILSVRC2012_val_00003274.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 254,648B, BPFP=0.4833 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 379,840B, BPFP=0.7210 +⌛️ [2/4] FRONTEND: Frontend time: 0.506s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.001s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10254154 160.39067055 + layer.39.0 9.36513093 1878.36916910 + ------------------------------------------------------------------------------------- + TOTAL 4.73383623 1019.37991983 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 634488 +BPFP 0.6022 bits/point +EBPFP 0.6022 equivalent bits/point +MSE 1019.379920 +---------------------- -------------------------------------------------------- +Time: 1.558s Load: 0.050s, Pack+Encode: 0.506s, Decode+Unpack: 1.001s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1019.3799 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02870880-ILSVRC2012_val_00003274.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02870880-ILSVRC2012_val_00003274.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02871525-ILSVRC2012_val_00000879.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02871525-ILSVRC2012_val_00000879.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 289,392B, BPFP=0.5493 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 441,692B, BPFP=0.8384 +⌛️ [2/4] FRONTEND: Frontend time: 0.551s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.064s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.17072899 806.69266278 + layer.39.0 20.29403547 2079.97716229 + ------------------------------------------------------------------------------------- + TOTAL 10.23238223 1443.33491254 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 731084 +BPFP 0.6938 bits/point +EBPFP 0.6938 equivalent bits/point +MSE 1443.334913 +---------------------- -------------------------------------------------------- +Time: 1.665s Load: 0.050s, Pack+Encode: 0.551s, Decode+Unpack: 1.064s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1443.3349 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02871525-ILSVRC2012_val_00000879.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02871525-ILSVRC2012_val_00000879.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02877765-ILSVRC2012_val_00000634.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02877765-ILSVRC2012_val_00000634.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 210,236B, BPFP=0.3990 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 391,656B, BPFP=0.7434 +⌛️ [2/4] FRONTEND: Frontend time: 0.572s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.058s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10908128 25.24847015 + layer.39.0 364.97770894 3111.17517007 + ------------------------------------------------------------------------------------- + TOTAL 182.54339511 1568.21182011 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 601892 +BPFP 0.5712 bits/point +EBPFP 0.5712 equivalent bits/point +MSE 1568.211820 +---------------------- -------------------------------------------------------- +Time: 1.679s Load: 0.050s, Pack+Encode: 0.572s, Decode+Unpack: 1.058s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1568.2118 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02877765-ILSVRC2012_val_00000634.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02877765-ILSVRC2012_val_00000634.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02879718-ILSVRC2012_val_00001354.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02879718-ILSVRC2012_val_00001354.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 246,892B, BPFP=0.4686 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 400,696B, BPFP=0.7606 +⌛️ [2/4] FRONTEND: Frontend time: 0.546s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.003s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10948122 160.72950073 + layer.39.0 55.92460444 2083.78231293 + ------------------------------------------------------------------------------------- + TOTAL 28.01704283 1122.25590683 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 647588 +BPFP 0.6146 bits/point +EBPFP 0.6146 equivalent bits/point +MSE 1122.255907 +---------------------- -------------------------------------------------------- +Time: 1.600s Load: 0.051s, Pack+Encode: 0.546s, Decode+Unpack: 1.003s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1122.2559 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02879718-ILSVRC2012_val_00001354.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02879718-ILSVRC2012_val_00001354.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02883205-ILSVRC2012_val_00000126.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02883205-ILSVRC2012_val_00000126.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 177,528B, BPFP=0.3370 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 458,092B, BPFP=0.8695 +⌛️ [2/4] FRONTEND: Frontend time: 0.579s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.070s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.06711708 12.98799939 + layer.39.0 7.82069686 1524.00109329 + ------------------------------------------------------------------------------------- + TOTAL 7.94390697 768.49454634 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 635620 +BPFP 0.6032 bits/point +EBPFP 0.6032 equivalent bits/point +MSE 768.494546 +---------------------- -------------------------------------------------------- +Time: 1.720s Load: 0.070s, Pack+Encode: 0.579s, Decode+Unpack: 1.070s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 768.4945 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02883205-ILSVRC2012_val_00000126.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02883205-ILSVRC2012_val_00000126.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892201-ILSVRC2012_val_00001145.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892201-ILSVRC2012_val_00001145.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 263,300B, BPFP=0.4998 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 435,840B, BPFP=0.8273 +⌛️ [2/4] FRONTEND: Frontend time: 0.608s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.063s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11297333 457.03179665 + layer.39.0 15.09638643 1840.51882896 + ------------------------------------------------------------------------------------- + TOTAL 7.60467988 1148.77531280 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 699140 +BPFP 0.6635 bits/point +EBPFP 0.6635 equivalent bits/point +MSE 1148.775313 +---------------------- -------------------------------------------------------- +Time: 1.723s Load: 0.052s, Pack+Encode: 0.608s, Decode+Unpack: 1.063s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1148.7753 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892201-ILSVRC2012_val_00001145.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02892201-ILSVRC2012_val_00001145.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892767-ILSVRC2012_val_00000808.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892767-ILSVRC2012_val_00000808.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 209,656B, BPFP=0.3979 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 487,708B, BPFP=0.9257 +⌛️ [2/4] FRONTEND: Frontend time: 0.571s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.061s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09598007 36.98674001 + layer.39.0 31.15013059 2022.78012634 + ------------------------------------------------------------------------------------- + TOTAL 15.62305533 1029.88343317 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 697364 +BPFP 0.6618 bits/point +EBPFP 0.6618 equivalent bits/point +MSE 1029.883433 +---------------------- -------------------------------------------------------- +Time: 1.702s Load: 0.070s, Pack+Encode: 0.571s, Decode+Unpack: 1.061s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1029.8834 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892767-ILSVRC2012_val_00000808.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02892767-ILSVRC2012_val_00000808.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02895154-ILSVRC2012_val_00000080.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02895154-ILSVRC2012_val_00000080.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 212,000B, BPFP=0.4024 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 493,172B, BPFP=0.9361 +⌛️ [2/4] FRONTEND: Frontend time: 0.512s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.993s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09530723 37.45970375 + layer.39.0 971.40427600 3513.13556851 + ------------------------------------------------------------------------------------- + TOTAL 485.74979162 1775.29763613 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 705172 +BPFP 0.6692 bits/point +EBPFP 0.6692 equivalent bits/point +MSE 1775.297636 +---------------------- -------------------------------------------------------- +Time: 1.557s Load: 0.052s, Pack+Encode: 0.512s, Decode+Unpack: 0.993s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1775.2976 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02895154-ILSVRC2012_val_00000080.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02895154-ILSVRC2012_val_00000080.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02906734-ILSVRC2012_val_00002937.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02906734-ILSVRC2012_val_00002937.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 219,120B, BPFP=0.4159 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 319,520B, BPFP=0.6065 +⌛️ [2/4] FRONTEND: Frontend time: 0.604s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.049s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09767962 64.00261935 + layer.39.0 32.09536716 1986.86661808 + ------------------------------------------------------------------------------------- + TOTAL 16.09652339 1025.43461871 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 538640 +BPFP 0.5112 bits/point +EBPFP 0.5112 equivalent bits/point +MSE 1025.434619 +---------------------- -------------------------------------------------------- +Time: 1.723s Load: 0.070s, Pack+Encode: 0.604s, Decode+Unpack: 1.049s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1025.4346 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02906734-ILSVRC2012_val_00002937.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02906734-ILSVRC2012_val_00002937.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02910353-ILSVRC2012_val_00000558.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02910353-ILSVRC2012_val_00000558.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 211,152B, BPFP=0.4008 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 346,484B, BPFP=0.6577 +⌛️ [2/4] FRONTEND: Frontend time: 0.529s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.027s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11017090 281.35198008 + layer.39.0 483.40066205 2528.57069971 + ------------------------------------------------------------------------------------- + TOTAL 241.75541648 1404.96133989 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 557636 +BPFP 0.5292 bits/point +EBPFP 0.5292 equivalent bits/point +MSE 1404.961340 +---------------------- -------------------------------------------------------- +Time: 1.606s Load: 0.050s, Pack+Encode: 0.529s, Decode+Unpack: 1.027s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1404.9613 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02910353-ILSVRC2012_val_00000558.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02910353-ILSVRC2012_val_00000558.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02916936-ILSVRC2012_val_00000366.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02916936-ILSVRC2012_val_00000366.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 192,900B, BPFP=0.3661 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 381,080B, BPFP=0.7233 +⌛️ [2/4] FRONTEND: Frontend time: 0.558s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.066s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10657579 13.11047399 + layer.39.0 435.18944363 2653.36880466 + ------------------------------------------------------------------------------------- + TOTAL 217.64800971 1333.23963933 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 573980 +BPFP 0.5447 bits/point +EBPFP 0.5447 equivalent bits/point +MSE 1333.239639 +---------------------- -------------------------------------------------------- +Time: 1.684s Load: 0.061s, Pack+Encode: 0.558s, Decode+Unpack: 1.066s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1333.2396 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02916936-ILSVRC2012_val_00000366.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02916936-ILSVRC2012_val_00000366.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02917067-ILSVRC2012_val_00000562.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02917067-ILSVRC2012_val_00000562.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 254,912B, BPFP=0.4838 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 366,392B, BPFP=0.6954 +⌛️ [2/4] FRONTEND: Frontend time: 0.500s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.986s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10760244 172.97234876 + layer.39.0 37.55795979 2300.21768707 + ------------------------------------------------------------------------------------- + TOTAL 18.83278111 1236.59501792 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 621304 +BPFP 0.5896 bits/point +EBPFP 0.5896 equivalent bits/point +MSE 1236.595018 +---------------------- -------------------------------------------------------- +Time: 1.556s Load: 0.070s, Pack+Encode: 0.500s, Decode+Unpack: 0.986s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1236.5950 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02917067-ILSVRC2012_val_00000562.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02917067-ILSVRC2012_val_00000562.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02930766-ILSVRC2012_val_00000056.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02930766-ILSVRC2012_val_00000056.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.065s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 237,320B, BPFP=0.4505 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 430,328B, BPFP=0.8168 +⌛️ [2/4] FRONTEND: Frontend time: 0.507s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.993s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10591127 38.29550838 + layer.39.0 18.32421875 2359.13726919 + ------------------------------------------------------------------------------------- + TOTAL 9.21506501 1198.71638879 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 667648 +BPFP 0.6336 bits/point +EBPFP 0.6336 equivalent bits/point +MSE 1198.716389 +---------------------- -------------------------------------------------------- +Time: 1.565s Load: 0.065s, Pack+Encode: 0.507s, Decode+Unpack: 0.993s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1198.7164 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02930766-ILSVRC2012_val_00000056.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02930766-ILSVRC2012_val_00000056.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02939185-ILSVRC2012_val_00000302.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02939185-ILSVRC2012_val_00000302.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 219,452B, BPFP=0.4165 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 402,800B, BPFP=0.7645 +⌛️ [2/4] FRONTEND: Frontend time: 0.511s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.982s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09694758 62.84419415 + layer.39.0 25.52453269 2440.42930029 + ------------------------------------------------------------------------------------- + TOTAL 12.81074014 1251.63674722 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 622252 +BPFP 0.5905 bits/point +EBPFP 0.5905 equivalent bits/point +MSE 1251.636747 +---------------------- -------------------------------------------------------- +Time: 1.543s Load: 0.050s, Pack+Encode: 0.511s, Decode+Unpack: 0.982s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1251.6367 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02939185-ILSVRC2012_val_00000302.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02939185-ILSVRC2012_val_00000302.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02950826-ILSVRC2012_val_00000392.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02950826-ILSVRC2012_val_00000392.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 232,876B, BPFP=0.4420 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 349,756B, BPFP=0.6639 +⌛️ [2/4] FRONTEND: Frontend time: 0.592s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.072s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10873010 324.72956147 + layer.39.0 707.96944849 2989.79543246 + ------------------------------------------------------------------------------------- + TOTAL 354.03908930 1657.26249696 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 582632 +BPFP 0.5529 bits/point +EBPFP 0.5529 equivalent bits/point +MSE 1657.262497 +---------------------- -------------------------------------------------------- +Time: 1.743s Load: 0.080s, Pack+Encode: 0.592s, Decode+Unpack: 1.072s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1657.2625 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02950826-ILSVRC2012_val_00000392.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02950826-ILSVRC2012_val_00000392.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951358-ILSVRC2012_val_00000347.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951358-ILSVRC2012_val_00000347.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 218,064B, BPFP=0.4139 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 311,364B, BPFP=0.5910 +⌛️ [2/4] FRONTEND: Frontend time: 0.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.067s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12200860 160.75183734 + layer.39.0 237.66299198 2176.43002915 + ------------------------------------------------------------------------------------- + TOTAL 118.89250029 1168.59093325 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 529428 +BPFP 0.5024 bits/point +EBPFP 0.5024 equivalent bits/point +MSE 1168.590933 +---------------------- -------------------------------------------------------- +Time: 1.717s Load: 0.069s, Pack+Encode: 0.581s, Decode+Unpack: 1.067s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1168.5909 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951358-ILSVRC2012_val_00000347.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02951358-ILSVRC2012_val_00000347.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951585-ILSVRC2012_val_00000101.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951585-ILSVRC2012_val_00000101.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 166,148B, BPFP=0.3154 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 320,356B, BPFP=0.6081 +⌛️ [2/4] FRONTEND: Frontend time: 0.603s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.005s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.07385432 12.93316573 + layer.39.0 181.90962099 2160.66496599 + ------------------------------------------------------------------------------------- + TOTAL 94.99173765 1086.79906586 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 486504 +BPFP 0.4617 bits/point +EBPFP 0.4617 equivalent bits/point +MSE 1086.799066 +---------------------- -------------------------------------------------------- +Time: 1.679s Load: 0.071s, Pack+Encode: 0.603s, Decode+Unpack: 1.005s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1086.7991 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951585-ILSVRC2012_val_00000101.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02951585-ILSVRC2012_val_00000101.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02963159-ILSVRC2012_val_00000061.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02963159-ILSVRC2012_val_00000061.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 217,932B, BPFP=0.4137 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 501,248B, BPFP=0.9514 +⌛️ [2/4] FRONTEND: Frontend time: 0.528s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.001s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11232698 50.09812318 + layer.39.0 24.77479842 1962.35228377 + ------------------------------------------------------------------------------------- + TOTAL 12.44356270 1006.22520347 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 719180 +BPFP 0.6825 bits/point +EBPFP 0.6825 equivalent bits/point +MSE 1006.225203 +---------------------- -------------------------------------------------------- +Time: 1.581s Load: 0.051s, Pack+Encode: 0.528s, Decode+Unpack: 1.001s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1006.2252 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02963159-ILSVRC2012_val_00000061.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02963159-ILSVRC2012_val_00000061.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02965783-ILSVRC2012_val_00000213.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02965783-ILSVRC2012_val_00000213.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 198,852B, BPFP=0.3774 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 397,196B, BPFP=0.7539 +⌛️ [2/4] FRONTEND: Frontend time: 0.525s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.006s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09516161 25.46268753 + layer.39.0 223.32294704 2565.67079689 + ------------------------------------------------------------------------------------- + TOTAL 111.70905432 1295.56674221 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 596048 +BPFP 0.5657 bits/point +EBPFP 0.5657 equivalent bits/point +MSE 1295.566742 +---------------------- -------------------------------------------------------- +Time: 1.593s Load: 0.062s, Pack+Encode: 0.525s, Decode+Unpack: 1.006s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1295.5667 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02965783-ILSVRC2012_val_00000213.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02965783-ILSVRC2012_val_00000213.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966193-ILSVRC2012_val_00000074.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966193-ILSVRC2012_val_00000074.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 286,372B, BPFP=0.5436 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 461,132B, BPFP=0.8753 +⌛️ [2/4] FRONTEND: Frontend time: 0.593s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.071s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12190965 332.79081633 + layer.39.0 378.75431244 3502.57167153 + ------------------------------------------------------------------------------------- + TOTAL 189.43811104 1917.68124393 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 747504 +BPFP 0.7094 bits/point +EBPFP 0.7094 equivalent bits/point +MSE 1917.681244 +---------------------- -------------------------------------------------------- +Time: 1.744s Load: 0.080s, Pack+Encode: 0.593s, Decode+Unpack: 1.071s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1917.6812 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966193-ILSVRC2012_val_00000074.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02966193-ILSVRC2012_val_00000074.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966687-ILSVRC2012_val_00001041.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966687-ILSVRC2012_val_00001041.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 221,632B, BPFP=0.4207 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 436,292B, BPFP=0.8281 +⌛️ [2/4] FRONTEND: Frontend time: 0.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.061s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12487827 124.14466412 + layer.39.0 254.07423773 2336.62973761 + ------------------------------------------------------------------------------------- + TOTAL 127.09955800 1230.38720086 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 657924 +BPFP 0.6244 bits/point +EBPFP 0.6244 equivalent bits/point +MSE 1230.387201 +---------------------- -------------------------------------------------------- +Time: 1.713s Load: 0.071s, Pack+Encode: 0.581s, Decode+Unpack: 1.061s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1230.3872 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966687-ILSVRC2012_val_00001041.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02966687-ILSVRC2012_val_00001041.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02971356-ILSVRC2012_val_00000019.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02971356-ILSVRC2012_val_00000019.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 182,644B, BPFP=0.3467 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 311,316B, BPFP=0.5909 +⌛️ [2/4] FRONTEND: Frontend time: 0.529s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.008s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09754465 24.84278198 + layer.39.0 24.51746044 1590.54251701 + ------------------------------------------------------------------------------------- + TOTAL 12.30750255 807.69264949 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 493960 +BPFP 0.4688 bits/point +EBPFP 0.4688 equivalent bits/point +MSE 807.692649 +---------------------- -------------------------------------------------------- +Time: 1.589s Load: 0.052s, Pack+Encode: 0.529s, Decode+Unpack: 1.008s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 807.6926 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02971356-ILSVRC2012_val_00000019.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02971356-ILSVRC2012_val_00000019.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02978881-ILSVRC2012_val_00000353.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02978881-ILSVRC2012_val_00000353.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 225,040B, BPFP=0.4271 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 446,228B, BPFP=0.8470 +⌛️ [2/4] FRONTEND: Frontend time: 0.508s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.991s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09975241 37.59594798 + layer.39.0 226.62124939 2270.79275996 + ------------------------------------------------------------------------------------- + TOTAL 113.36050090 1154.19435397 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 671268 +BPFP 0.6371 bits/point +EBPFP 0.6371 equivalent bits/point +MSE 1154.194354 +---------------------- -------------------------------------------------------- +Time: 1.550s Load: 0.051s, Pack+Encode: 0.508s, Decode+Unpack: 0.991s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1154.1944 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02978881-ILSVRC2012_val_00000353.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02978881-ILSVRC2012_val_00000353.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02980441-ILSVRC2012_val_00000122.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02980441-ILSVRC2012_val_00000122.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 202,736B, BPFP=0.3848 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 429,660B, BPFP=0.8155 +⌛️ [2/4] FRONTEND: Frontend time: 0.527s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.062s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10186533 12.75871409 + layer.39.0 8.25151846 1694.69363460 + ------------------------------------------------------------------------------------- + TOTAL 4.17669190 853.72617434 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 632396 +BPFP 0.6002 bits/point +EBPFP 0.6002 equivalent bits/point +MSE 853.726174 +---------------------- -------------------------------------------------------- +Time: 1.641s Load: 0.051s, Pack+Encode: 0.527s, Decode+Unpack: 1.062s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 853.7262 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02980441-ILSVRC2012_val_00000122.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02980441-ILSVRC2012_val_00000122.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02988304-ILSVRC2012_val_00003491.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02988304-ILSVRC2012_val_00003491.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.049s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 201,172B, BPFP=0.3818 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 327,568B, BPFP=0.6218 +⌛️ [2/4] FRONTEND: Frontend time: 0.549s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.005s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10176498 85.59937591 + layer.39.0 516.16180758 2846.74344023 + ------------------------------------------------------------------------------------- + TOTAL 258.13178628 1466.17140807 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 528740 +BPFP 0.5018 bits/point +EBPFP 0.5018 equivalent bits/point +MSE 1466.171408 +---------------------- -------------------------------------------------------- +Time: 1.603s Load: 0.049s, Pack+Encode: 0.549s, Decode+Unpack: 1.005s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1466.1714 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02988304-ILSVRC2012_val_00003491.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02988304-ILSVRC2012_val_00003491.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992211-ILSVRC2012_val_00000108.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992211-ILSVRC2012_val_00000108.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 228,476B, BPFP=0.4337 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 353,024B, BPFP=0.6701 +⌛️ [2/4] FRONTEND: Frontend time: 0.576s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.054s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10107529 61.38298333 + layer.39.0 89.13089923 3084.29446064 + ------------------------------------------------------------------------------------- + TOTAL 44.61598726 1572.83872198 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 581500 +BPFP 0.5519 bits/point +EBPFP 0.5519 equivalent bits/point +MSE 1572.838722 +---------------------- -------------------------------------------------------- +Time: 1.681s Load: 0.052s, Pack+Encode: 0.576s, Decode+Unpack: 1.054s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1572.8387 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992211-ILSVRC2012_val_00000108.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02992211-ILSVRC2012_val_00000108.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992529-ILSVRC2012_val_00000089.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992529-ILSVRC2012_val_00000089.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 220,012B, BPFP=0.4176 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 410,820B, BPFP=0.7798 +⌛️ [2/4] FRONTEND: Frontend time: 0.523s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.004s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11197385 12.68470982 + layer.39.0 964.25631681 3414.22351798 + ------------------------------------------------------------------------------------- + TOTAL 482.18414533 1713.45411390 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 630832 +BPFP 0.5987 bits/point +EBPFP 0.5987 equivalent bits/point +MSE 1713.454114 +---------------------- -------------------------------------------------------- +Time: 1.589s Load: 0.061s, Pack+Encode: 0.523s, Decode+Unpack: 1.004s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1713.4541 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992529-ILSVRC2012_val_00000089.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02992529-ILSVRC2012_val_00000089.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02999410-ILSVRC2012_val_00000376.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02999410-ILSVRC2012_val_00000376.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.079s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 226,264B, BPFP=0.4295 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 477,728B, BPFP=0.9068 +⌛️ [2/4] FRONTEND: Frontend time: 0.589s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.076s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10398186 197.09097121 + layer.39.0 145.78410471 1933.06827017 + ------------------------------------------------------------------------------------- + TOTAL 72.94404329 1065.07962069 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 703992 +BPFP 0.6681 bits/point +EBPFP 0.6681 equivalent bits/point +MSE 1065.079621 +---------------------- -------------------------------------------------------- +Time: 1.744s Load: 0.079s, Pack+Encode: 0.589s, Decode+Unpack: 1.076s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1065.0796 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02999410-ILSVRC2012_val_00000376.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02999410-ILSVRC2012_val_00000376.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000134-ILSVRC2012_val_00001094.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000134-ILSVRC2012_val_00001094.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 199,712B, BPFP=0.3791 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 449,784B, BPFP=0.8537 +⌛️ [2/4] FRONTEND: Frontend time: 0.586s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.053s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09696872 13.17741455 + layer.39.0 22.81329530 2420.93051506 + ------------------------------------------------------------------------------------- + TOTAL 11.45513201 1217.05396481 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 649496 +BPFP 0.6164 bits/point +EBPFP 0.6164 equivalent bits/point +MSE 1217.053965 +---------------------- -------------------------------------------------------- +Time: 1.709s Load: 0.070s, Pack+Encode: 0.586s, Decode+Unpack: 1.053s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1217.0540 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000134-ILSVRC2012_val_00001094.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03000134-ILSVRC2012_val_00001094.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000247-ILSVRC2012_val_00002280.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000247-ILSVRC2012_val_00002280.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.079s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 399,776B, BPFP=0.7588 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 321,024B, BPFP=0.6093 +⌛️ [2/4] FRONTEND: Frontend time: 0.599s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.057s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.29135144 2614.69922255 + layer.39.0 428.26293732 2313.58357629 + ------------------------------------------------------------------------------------- + TOTAL 214.27714438 2464.14139942 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 720800 +BPFP 0.6841 bits/point +EBPFP 0.6841 equivalent bits/point +MSE 2464.141399 +---------------------- -------------------------------------------------------- +Time: 1.735s Load: 0.079s, Pack+Encode: 0.599s, Decode+Unpack: 1.057s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2464.1414 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000247-ILSVRC2012_val_00002280.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03000247-ILSVRC2012_val_00002280.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000684-ILSVRC2012_val_00000537.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000684-ILSVRC2012_val_00000537.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 277,112B, BPFP=0.5260 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 434,240B, BPFP=0.8242 +⌛️ [2/4] FRONTEND: Frontend time: 0.589s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.071s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13150742 629.20001215 + layer.39.0 55.24585459 2286.80077745 + ------------------------------------------------------------------------------------- + TOTAL 27.68868101 1458.00039480 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 711352 +BPFP 0.6751 bits/point +EBPFP 0.6751 equivalent bits/point +MSE 1458.000395 +---------------------- -------------------------------------------------------- +Time: 1.730s Load: 0.070s, Pack+Encode: 0.589s, Decode+Unpack: 1.071s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1458.0004 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000684-ILSVRC2012_val_00000537.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03000684-ILSVRC2012_val_00000537.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03014705-ILSVRC2012_val_00001168.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03014705-ILSVRC2012_val_00001168.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.079s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 176,904B, BPFP=0.3358 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 364,384B, BPFP=0.6916 +⌛️ [2/4] FRONTEND: Frontend time: 0.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.029s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09787338 12.71849091 + layer.39.0 322.89622813 2416.50534500 + ------------------------------------------------------------------------------------- + TOTAL 161.49705076 1214.61191795 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 541288 +BPFP 0.5137 bits/point +EBPFP 0.5137 equivalent bits/point +MSE 1214.611918 +---------------------- -------------------------------------------------------- +Time: 1.689s Load: 0.079s, Pack+Encode: 0.581s, Decode+Unpack: 1.029s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1214.6119 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03014705-ILSVRC2012_val_00001168.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03014705-ILSVRC2012_val_00001168.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03017168-ILSVRC2012_val_00001601.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03017168-ILSVRC2012_val_00001601.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 197,580B, BPFP=0.3750 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 367,284B, BPFP=0.6971 +⌛️ [2/4] FRONTEND: Frontend time: 0.543s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.028s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10213913 13.73380463 + layer.39.0 475.40952988 3396.64455782 + ------------------------------------------------------------------------------------- + TOTAL 237.75583451 1705.18918122 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 564864 +BPFP 0.5361 bits/point +EBPFP 0.5361 equivalent bits/point +MSE 1705.189181 +---------------------- -------------------------------------------------------- +Time: 1.631s Load: 0.060s, Pack+Encode: 0.543s, Decode+Unpack: 1.028s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1705.1892 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03017168-ILSVRC2012_val_00001601.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03017168-ILSVRC2012_val_00001601.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03018349-ILSVRC2012_val_00000346.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03018349-ILSVRC2012_val_00000346.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 222,896B, BPFP=0.4231 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 498,188B, BPFP=0.9456 +⌛️ [2/4] FRONTEND: Frontend time: 0.528s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.987s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09959339 48.78411231 + layer.39.0 56.59841169 2492.63362488 + ------------------------------------------------------------------------------------- + TOTAL 28.34900254 1270.70886859 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 721084 +BPFP 0.6843 bits/point +EBPFP 0.6843 equivalent bits/point +MSE 1270.708869 +---------------------- -------------------------------------------------------- +Time: 1.576s Load: 0.061s, Pack+Encode: 0.528s, Decode+Unpack: 0.987s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1270.7089 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03018349-ILSVRC2012_val_00000346.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03018349-ILSVRC2012_val_00000346.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03026506-ILSVRC2012_val_00001908.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03026506-ILSVRC2012_val_00001908.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 229,120B, BPFP=0.4349 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 486,972B, BPFP=0.9243 +⌛️ [2/4] FRONTEND: Frontend time: 0.537s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.064s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10977067 86.07618137 + layer.39.0 668.54063411 3533.05466472 + ------------------------------------------------------------------------------------- + TOTAL 334.32520239 1809.56542304 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 716092 +BPFP 0.6796 bits/point +EBPFP 0.6796 equivalent bits/point +MSE 1809.565423 +---------------------- -------------------------------------------------------- +Time: 1.671s Load: 0.070s, Pack+Encode: 0.537s, Decode+Unpack: 1.064s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1809.5654 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03026506-ILSVRC2012_val_00001908.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03026506-ILSVRC2012_val_00001908.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03028079-ILSVRC2012_val_00003351.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03028079-ILSVRC2012_val_00003351.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 210,396B, BPFP=0.3993 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 449,744B, BPFP=0.8537 +⌛️ [2/4] FRONTEND: Frontend time: 0.514s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.987s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10934904 73.75601312 + layer.39.0 15.31112010 1999.72752672 + ------------------------------------------------------------------------------------- + TOTAL 7.71023457 1036.74176992 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 660140 +BPFP 0.6265 bits/point +EBPFP 0.6265 equivalent bits/point +MSE 1036.741770 +---------------------- -------------------------------------------------------- +Time: 1.561s Load: 0.060s, Pack+Encode: 0.514s, Decode+Unpack: 0.987s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1036.7418 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03028079-ILSVRC2012_val_00003351.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03028079-ILSVRC2012_val_00003351.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03032252-ILSVRC2012_val_00000086.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03032252-ILSVRC2012_val_00000086.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 272,600B, BPFP=0.5174 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 421,592B, BPFP=0.8002 +⌛️ [2/4] FRONTEND: Frontend time: 0.595s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.074s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13507480 246.87683734 + layer.39.0 103.55165816 2064.40038873 + ------------------------------------------------------------------------------------- + TOTAL 51.84336648 1155.63861303 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 694192 +BPFP 0.6588 bits/point +EBPFP 0.6588 equivalent bits/point +MSE 1155.638613 +---------------------- -------------------------------------------------------- +Time: 1.748s Load: 0.080s, Pack+Encode: 0.595s, Decode+Unpack: 1.074s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1155.6386 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03032252-ILSVRC2012_val_00000086.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03032252-ILSVRC2012_val_00000086.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03041632-ILSVRC2012_val_00000564.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03041632-ILSVRC2012_val_00000564.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 196,756B, BPFP=0.3735 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 334,368B, BPFP=0.6347 +⌛️ [2/4] FRONTEND: Frontend time: 0.515s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.997s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10123130 110.81127004 + layer.39.0 371.34277818 2882.86297376 + ------------------------------------------------------------------------------------- + TOTAL 185.72200474 1496.83712190 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 531124 +BPFP 0.5041 bits/point +EBPFP 0.5041 equivalent bits/point +MSE 1496.837122 +---------------------- -------------------------------------------------------- +Time: 1.572s Load: 0.060s, Pack+Encode: 0.515s, Decode+Unpack: 0.997s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1496.8371 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03041632-ILSVRC2012_val_00000564.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03041632-ILSVRC2012_val_00000564.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03042490-ILSVRC2012_val_00001426.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03042490-ILSVRC2012_val_00001426.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 237,272B, BPFP=0.4504 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 355,824B, BPFP=0.6754 +⌛️ [2/4] FRONTEND: Frontend time: 0.525s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.993s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10706725 86.66451804 + layer.39.0 141.71039845 3033.34548105 + ------------------------------------------------------------------------------------- + TOTAL 70.90873285 1560.00499954 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 593096 +BPFP 0.5629 bits/point +EBPFP 0.5629 equivalent bits/point +MSE 1560.005000 +---------------------- -------------------------------------------------------- +Time: 1.569s Load: 0.051s, Pack+Encode: 0.525s, Decode+Unpack: 0.993s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1560.0050 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03042490-ILSVRC2012_val_00001426.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03042490-ILSVRC2012_val_00001426.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03047690-ILSVRC2012_val_00001500.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03047690-ILSVRC2012_val_00001500.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 198,456B, BPFP=0.3767 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 476,348B, BPFP=0.9041 +⌛️ [2/4] FRONTEND: Frontend time: 0.547s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.997s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09570478 12.56856532 + layer.39.0 226.76483540 2530.14795918 + ------------------------------------------------------------------------------------- + TOTAL 113.43027009 1271.35826225 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 674804 +BPFP 0.6404 bits/point +EBPFP 0.6404 equivalent bits/point +MSE 1271.358262 +---------------------- -------------------------------------------------------- +Time: 1.595s Load: 0.052s, Pack+Encode: 0.547s, Decode+Unpack: 0.997s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1271.3583 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03047690-ILSVRC2012_val_00001500.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03047690-ILSVRC2012_val_00001500.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03062245-ILSVRC2012_val_00000344.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03062245-ILSVRC2012_val_00000344.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 185,540B, BPFP=0.3522 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 411,236B, BPFP=0.7806 +⌛️ [2/4] FRONTEND: Frontend time: 0.558s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.056s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09619164 24.72506871 + layer.39.0 46.71096787 1966.33527697 + ------------------------------------------------------------------------------------- + TOTAL 23.40357976 995.53017284 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 596776 +BPFP 0.5664 bits/point +EBPFP 0.5664 equivalent bits/point +MSE 995.530173 +---------------------- -------------------------------------------------------- +Time: 1.666s Load: 0.051s, Pack+Encode: 0.558s, Decode+Unpack: 1.056s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 995.5302 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03062245-ILSVRC2012_val_00000344.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03062245-ILSVRC2012_val_00000344.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063599-ILSVRC2012_val_00000164.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063599-ILSVRC2012_val_00000164.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 213,940B, BPFP=0.4061 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 499,324B, BPFP=0.9478 +⌛️ [2/4] FRONTEND: Frontend time: 0.511s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.992s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10111790 12.56617563 + layer.39.0 9.80528160 1908.57810982 + ------------------------------------------------------------------------------------- + TOTAL 4.95319975 960.57214272 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 713264 +BPFP 0.6769 bits/point +EBPFP 0.6769 equivalent bits/point +MSE 960.572143 +---------------------- -------------------------------------------------------- +Time: 1.554s Load: 0.051s, Pack+Encode: 0.511s, Decode+Unpack: 0.992s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 960.5721 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063599-ILSVRC2012_val_00000164.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03063599-ILSVRC2012_val_00000164.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063689-ILSVRC2012_val_00001940.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063689-ILSVRC2012_val_00001940.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 198,108B, BPFP=0.3760 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 416,044B, BPFP=0.7897 +⌛️ [2/4] FRONTEND: Frontend time: 0.547s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.017s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09645106 13.44063183 + layer.39.0 18.48014797 2495.14382896 + ------------------------------------------------------------------------------------- + TOTAL 9.28829952 1254.29223040 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 614152 +BPFP 0.5829 bits/point +EBPFP 0.5829 equivalent bits/point +MSE 1254.292230 +---------------------- -------------------------------------------------------- +Time: 1.615s Load: 0.051s, Pack+Encode: 0.547s, Decode+Unpack: 1.017s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1254.2922 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063689-ILSVRC2012_val_00001940.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03063689-ILSVRC2012_val_00001940.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03065424-ILSVRC2012_val_00000915.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03065424-ILSVRC2012_val_00000915.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 272,884B, BPFP=0.5180 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 422,504B, BPFP=0.8019 +⌛️ [2/4] FRONTEND: Frontend time: 0.568s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 16.177s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12384982 184.58861759 + layer.39.0 2154.15986395 4035.15014577 + ------------------------------------------------------------------------------------- + TOTAL 1077.14185688 2109.86938168 + (elements=8,429,568) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 8429568 +Total Bytes 695388 +BPFP 0.6600 bits/point +EBPFP 0.6600 equivalent bits/point +MSE 2109.869382 +---------------------- --------------------------------------------------------- +Time: 16.795s Load: 0.050s, Pack+Encode: 0.568s, Decode+Unpack: 16.177s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2109.8694 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03065424-ILSVRC2012_val_00000915.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03065424-ILSVRC2012_val_00000915.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03075370-ILSVRC2012_val_00004971.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03075370-ILSVRC2012_val_00004971.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.058s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 193,732B, BPFP=0.3677 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 429,448B, BPFP=0.8151 +⌛️ [2/4] FRONTEND: Frontend time: 0.560s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.002s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10672879 13.31568213 + layer.39.0 301.29020894 2010.70007289 + ------------------------------------------------------------------------------------- + TOTAL 150.69846886 1012.00787751 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 623180 +BPFP 0.5914 bits/point +EBPFP 0.5914 equivalent bits/point +MSE 1012.007878 +---------------------- -------------------------------------------------------- +Time: 1.619s Load: 0.058s, Pack+Encode: 0.560s, Decode+Unpack: 1.002s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1012.0079 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03075370-ILSVRC2012_val_00004971.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03075370-ILSVRC2012_val_00004971.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03089624-ILSVRC2012_val_00001190.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03089624-ILSVRC2012_val_00001190.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 222,884B, BPFP=0.4231 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 450,920B, BPFP=0.8559 +⌛️ [2/4] FRONTEND: Frontend time: 0.517s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.990s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10385029 50.27353620 + layer.39.0 606.38896987 3340.30879495 + ------------------------------------------------------------------------------------- + TOTAL 303.24641008 1695.29116557 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 673804 +BPFP 0.6395 bits/point +EBPFP 0.6395 equivalent bits/point +MSE 1695.291166 +---------------------- -------------------------------------------------------- +Time: 1.558s Load: 0.051s, Pack+Encode: 0.517s, Decode+Unpack: 0.990s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1695.2912 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03089624-ILSVRC2012_val_00001190.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03089624-ILSVRC2012_val_00001190.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03095699-ILSVRC2012_val_00000403.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03095699-ILSVRC2012_val_00000403.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.079s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 262,744B, BPFP=0.4987 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 365,048B, BPFP=0.6929 +⌛️ [2/4] FRONTEND: Frontend time: 0.577s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.068s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12139760 641.24016035 + layer.39.0 62.59250486 2540.90306122 + ------------------------------------------------------------------------------------- + TOTAL 31.35695123 1591.07161079 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 627792 +BPFP 0.5958 bits/point +EBPFP 0.5958 equivalent bits/point +MSE 1591.071611 +---------------------- -------------------------------------------------------- +Time: 1.724s Load: 0.079s, Pack+Encode: 0.577s, Decode+Unpack: 1.068s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1591.0716 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03095699-ILSVRC2012_val_00000403.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03095699-ILSVRC2012_val_00000403.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03100240-ILSVRC2012_val_00001201.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03100240-ILSVRC2012_val_00001201.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 217,328B, BPFP=0.4125 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 321,380B, BPFP=0.6100 +⌛️ [2/4] FRONTEND: Frontend time: 0.507s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.986s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10258218 99.44926810 + layer.39.0 42.98202138 1908.60860058 + ------------------------------------------------------------------------------------- + TOTAL 21.54230178 1004.02893434 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 538708 +BPFP 0.5113 bits/point +EBPFP 0.5113 equivalent bits/point +MSE 1004.028934 +---------------------- -------------------------------------------------------- +Time: 1.544s Load: 0.052s, Pack+Encode: 0.507s, Decode+Unpack: 0.986s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1004.0289 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03100240-ILSVRC2012_val_00001201.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03100240-ILSVRC2012_val_00001201.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03109150-ILSVRC2012_val_00000678.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03109150-ILSVRC2012_val_00000678.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 225,152B, BPFP=0.4274 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 429,436B, BPFP=0.8151 +⌛️ [2/4] FRONTEND: Frontend time: 0.552s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.060s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09720685 49.54244488 + layer.39.0 496.21158285 2816.40670554 + ------------------------------------------------------------------------------------- + TOTAL 248.15439485 1432.97457521 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 654588 +BPFP 0.6212 bits/point +EBPFP 0.6212 equivalent bits/point +MSE 1432.974575 +---------------------- -------------------------------------------------------- +Time: 1.662s Load: 0.051s, Pack+Encode: 0.552s, Decode+Unpack: 1.060s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1432.9746 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03109150-ILSVRC2012_val_00000678.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03109150-ILSVRC2012_val_00000678.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03110669-ILSVRC2012_val_00002171.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03110669-ILSVRC2012_val_00002171.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 287,080B, BPFP=0.5449 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 472,572B, BPFP=0.8970 +⌛️ [2/4] FRONTEND: Frontend time: 0.596s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.075s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.15128201 454.19964772 + layer.39.0 15.00769387 2383.16520894 + ------------------------------------------------------------------------------------- + TOTAL 7.57948794 1418.68242833 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 759652 +BPFP 0.7209 bits/point +EBPFP 0.7209 equivalent bits/point +MSE 1418.682428 +---------------------- -------------------------------------------------------- +Time: 1.741s Load: 0.070s, Pack+Encode: 0.596s, Decode+Unpack: 1.075s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1418.6824 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03110669-ILSVRC2012_val_00002171.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03110669-ILSVRC2012_val_00002171.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124043-ILSVRC2012_val_00000766.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124043-ILSVRC2012_val_00000766.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 203,604B, BPFP=0.3865 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 420,336B, BPFP=0.7978 +⌛️ [2/4] FRONTEND: Frontend time: 0.585s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.089s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11473456 14.14538349 + layer.39.0 54.83309418 2790.02429543 + ------------------------------------------------------------------------------------- + TOTAL 27.47391437 1402.08483946 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 623940 +BPFP 0.5921 bits/point +EBPFP 0.5921 equivalent bits/point +MSE 1402.084839 +---------------------- -------------------------------------------------------- +Time: 1.744s Load: 0.070s, Pack+Encode: 0.585s, Decode+Unpack: 1.089s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1402.0848 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124043-ILSVRC2012_val_00000766.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03124043-ILSVRC2012_val_00000766.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124170-ILSVRC2012_val_00001875.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124170-ILSVRC2012_val_00001875.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 210,176B, BPFP=0.3989 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 338,472B, BPFP=0.6424 +⌛️ [2/4] FRONTEND: Frontend time: 0.501s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.995s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11393612 38.86155779 + layer.39.0 9.06747107 1853.93537415 + ------------------------------------------------------------------------------------- + TOTAL 4.59070360 946.39846597 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 548648 +BPFP 0.5207 bits/point +EBPFP 0.5207 equivalent bits/point +MSE 946.398466 +---------------------- -------------------------------------------------------- +Time: 1.547s Load: 0.051s, Pack+Encode: 0.501s, Decode+Unpack: 0.995s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 946.3985 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124170-ILSVRC2012_val_00001875.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03124170-ILSVRC2012_val_00001875.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03126707-ILSVRC2012_val_00000020.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03126707-ILSVRC2012_val_00000020.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 232,944B, BPFP=0.4421 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 334,616B, BPFP=0.6351 +⌛️ [2/4] FRONTEND: Frontend time: 0.573s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.073s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.15273996 476.56219631 + layer.39.0 1033.15269679 2331.48250729 + ------------------------------------------------------------------------------------- + TOTAL 516.65271838 1404.02235180 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 567560 +BPFP 0.5386 bits/point +EBPFP 0.5386 equivalent bits/point +MSE 1404.022352 +---------------------- -------------------------------------------------------- +Time: 1.715s Load: 0.069s, Pack+Encode: 0.573s, Decode+Unpack: 1.073s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1404.0224 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03126707-ILSVRC2012_val_00000020.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03126707-ILSVRC2012_val_00000020.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03127747-ILSVRC2012_val_00001689.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03127747-ILSVRC2012_val_00001689.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 191,400B, BPFP=0.3633 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 427,544B, BPFP=0.8115 +⌛️ [2/4] FRONTEND: Frontend time: 0.573s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.063s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10152024 12.58315017 + layer.39.0 322.92343902 2648.63702624 + ------------------------------------------------------------------------------------- + TOTAL 161.51247963 1330.61008820 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 618944 +BPFP 0.5874 bits/point +EBPFP 0.5874 equivalent bits/point +MSE 1330.610088 +---------------------- -------------------------------------------------------- +Time: 1.686s Load: 0.050s, Pack+Encode: 0.573s, Decode+Unpack: 1.063s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1330.6101 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03127747-ILSVRC2012_val_00001689.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03127747-ILSVRC2012_val_00001689.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03131574-ILSVRC2012_val_00003036.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03131574-ILSVRC2012_val_00003036.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.054s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 192,308B, BPFP=0.3650 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 389,008B, BPFP=0.7384 +⌛️ [2/4] FRONTEND: Frontend time: 0.598s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.997s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09568423 13.01640701 + layer.39.0 163.24681122 2805.70408163 + ------------------------------------------------------------------------------------- + TOTAL 81.67124773 1409.36024432 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 581316 +BPFP 0.5517 bits/point +EBPFP 0.5517 equivalent bits/point +MSE 1409.360244 +---------------------- -------------------------------------------------------- +Time: 1.649s Load: 0.054s, Pack+Encode: 0.598s, Decode+Unpack: 0.997s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1409.3602 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03131574-ILSVRC2012_val_00003036.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03131574-ILSVRC2012_val_00003036.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03133878-ILSVRC2012_val_00000534.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03133878-ILSVRC2012_val_00000534.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 263,160B, BPFP=0.4995 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 425,768B, BPFP=0.8081 +⌛️ [2/4] FRONTEND: Frontend time: 0.609s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.065s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11186348 124.46580418 + layer.39.0 28.46096218 2602.42857143 + ------------------------------------------------------------------------------------- + TOTAL 14.28641283 1363.44718780 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 688928 +BPFP 0.6538 bits/point +EBPFP 0.6538 equivalent bits/point +MSE 1363.447188 +---------------------- -------------------------------------------------------- +Time: 1.745s Load: 0.070s, Pack+Encode: 0.609s, Decode+Unpack: 1.065s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1363.4472 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03133878-ILSVRC2012_val_00000534.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03133878-ILSVRC2012_val_00000534.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03134739-ILSVRC2012_val_00000249.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03134739-ILSVRC2012_val_00000249.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.079s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 206,084B, BPFP=0.3912 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 482,944B, BPFP=0.9167 +⌛️ [2/4] FRONTEND: Frontend time: 0.529s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.003s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09967384 24.93240555 + layer.39.0 372.24465500 3322.87074830 + ------------------------------------------------------------------------------------- + TOTAL 186.17216442 1673.90157693 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 689028 +BPFP 0.6539 bits/point +EBPFP 0.6539 equivalent bits/point +MSE 1673.901577 +---------------------- -------------------------------------------------------- +Time: 1.611s Load: 0.079s, Pack+Encode: 0.529s, Decode+Unpack: 1.003s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1673.9016 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03134739-ILSVRC2012_val_00000249.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03134739-ILSVRC2012_val_00000249.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03141823-ILSVRC2012_val_00001337.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03141823-ILSVRC2012_val_00001337.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 239,564B, BPFP=0.4547 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 549,320B, BPFP=1.0427 +⌛️ [2/4] FRONTEND: Frontend time: 0.523s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.990s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10422104 38.11893373 + layer.39.0 29.45558301 2469.03911565 + ------------------------------------------------------------------------------------- + TOTAL 14.77990203 1253.57902469 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 788884 +BPFP 0.7487 bits/point +EBPFP 0.7487 equivalent bits/point +MSE 1253.579025 +---------------------- -------------------------------------------------------- +Time: 1.563s Load: 0.050s, Pack+Encode: 0.523s, Decode+Unpack: 0.990s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1253.5790 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03141823-ILSVRC2012_val_00001337.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03141823-ILSVRC2012_val_00001337.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03160309-ILSVRC2012_val_00000330.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03160309-ILSVRC2012_val_00000330.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 195,528B, BPFP=0.3711 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 266,316B, BPFP=0.5055 +⌛️ [2/4] FRONTEND: Frontend time: 0.548s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.992s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09980877 37.88770196 + layer.39.0 30.04123011 1487.91083576 + ------------------------------------------------------------------------------------- + TOTAL 15.07051944 762.89926886 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 461844 +BPFP 0.4383 bits/point +EBPFP 0.4383 equivalent bits/point +MSE 762.899269 +---------------------- -------------------------------------------------------- +Time: 1.592s Load: 0.052s, Pack+Encode: 0.548s, Decode+Unpack: 0.992s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 762.8993 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03160309-ILSVRC2012_val_00000330.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03160309-ILSVRC2012_val_00000330.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03187595-ILSVRC2012_val_00000137.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03187595-ILSVRC2012_val_00000137.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 212,492B, BPFP=0.4033 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 505,632B, BPFP=0.9597 +⌛️ [2/4] FRONTEND: Frontend time: 0.525s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.989s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10716813 25.18007281 + layer.39.0 12.39187394 2159.81365403 + ------------------------------------------------------------------------------------- + TOTAL 6.24952103 1092.49686342 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 718124 +BPFP 0.6815 bits/point +EBPFP 0.6815 equivalent bits/point +MSE 1092.496863 +---------------------- -------------------------------------------------------- +Time: 1.565s Load: 0.051s, Pack+Encode: 0.525s, Decode+Unpack: 0.989s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1092.4969 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03187595-ILSVRC2012_val_00000137.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03187595-ILSVRC2012_val_00000137.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03188531-ILSVRC2012_val_00000493.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03188531-ILSVRC2012_val_00000493.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 189,008B, BPFP=0.3588 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 408,856B, BPFP=0.7760 +⌛️ [2/4] FRONTEND: Frontend time: 0.591s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.057s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09509044 12.73963363 + layer.39.0 10.77256154 2063.63508260 + ------------------------------------------------------------------------------------- + TOTAL 5.43382599 1038.18735812 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 597864 +BPFP 0.5674 bits/point +EBPFP 0.5674 equivalent bits/point +MSE 1038.187358 +---------------------- -------------------------------------------------------- +Time: 1.698s Load: 0.050s, Pack+Encode: 0.591s, Decode+Unpack: 1.057s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1038.1874 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03188531-ILSVRC2012_val_00000493.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03188531-ILSVRC2012_val_00000493.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03196217-ILSVRC2012_val_00003643.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03196217-ILSVRC2012_val_00003643.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 188,540B, BPFP=0.3579 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 422,328B, BPFP=0.8016 +⌛️ [2/4] FRONTEND: Frontend time: 0.569s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.062s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09478207 12.80482891 + layer.39.0 65.57403274 2672.88994169 + ------------------------------------------------------------------------------------- + TOTAL 32.83440740 1342.84738530 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 610868 +BPFP 0.5797 bits/point +EBPFP 0.5797 equivalent bits/point +MSE 1342.847385 +---------------------- -------------------------------------------------------- +Time: 1.701s Load: 0.070s, Pack+Encode: 0.569s, Decode+Unpack: 1.062s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1342.8474 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03196217-ILSVRC2012_val_00003643.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03196217-ILSVRC2012_val_00003643.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03201208-ILSVRC2012_val_00000241.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03201208-ILSVRC2012_val_00000241.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 225,884B, BPFP=0.4287 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 477,008B, BPFP=0.9054 +⌛️ [2/4] FRONTEND: Frontend time: 0.582s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.055s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10331685 173.70802964 + layer.39.0 136.59314261 1848.82798834 + ------------------------------------------------------------------------------------- + TOTAL 68.34822973 1011.26800899 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 702892 +BPFP 0.6671 bits/point +EBPFP 0.6671 equivalent bits/point +MSE 1011.268009 +---------------------- -------------------------------------------------------- +Time: 1.717s Load: 0.080s, Pack+Encode: 0.582s, Decode+Unpack: 1.055s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1011.2680 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03201208-ILSVRC2012_val_00000241.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03201208-ILSVRC2012_val_00000241.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03207743-ILSVRC2012_val_00000256.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03207743-ILSVRC2012_val_00000256.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.079s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 281,628B, BPFP=0.5346 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 389,448B, BPFP=0.7392 +⌛️ [2/4] FRONTEND: Frontend time: 0.592s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.072s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09674843 520.03887269 + layer.39.0 189.63590258 2590.13362488 + ------------------------------------------------------------------------------------- + TOTAL 94.86632550 1555.08624879 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 671076 +BPFP 0.6369 bits/point +EBPFP 0.6369 equivalent bits/point +MSE 1555.086249 +---------------------- -------------------------------------------------------- +Time: 1.743s Load: 0.079s, Pack+Encode: 0.592s, Decode+Unpack: 1.072s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1555.0862 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03207743-ILSVRC2012_val_00000256.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03207743-ILSVRC2012_val_00000256.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03216828-ILSVRC2012_val_00001729.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03216828-ILSVRC2012_val_00001729.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 231,776B, BPFP=0.4399 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 409,400B, BPFP=0.7771 +⌛️ [2/4] FRONTEND: Frontend time: 0.572s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.056s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10800209 210.11332301 + layer.39.0 31.30713223 1829.53656463 + ------------------------------------------------------------------------------------- + TOTAL 15.70756716 1019.82494382 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 641176 +BPFP 0.6085 bits/point +EBPFP 0.6085 equivalent bits/point +MSE 1019.824944 +---------------------- -------------------------------------------------------- +Time: 1.698s Load: 0.070s, Pack+Encode: 0.572s, Decode+Unpack: 1.056s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1019.8249 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03216828-ILSVRC2012_val_00001729.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03216828-ILSVRC2012_val_00001729.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03218198-ILSVRC2012_val_00002266.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03218198-ILSVRC2012_val_00002266.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 259,948B, BPFP=0.4934 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 433,592B, BPFP=0.8230 +⌛️ [2/4] FRONTEND: Frontend time: 0.601s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.053s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11617067 176.73196064 + layer.39.0 195.83184524 2375.62633625 + ------------------------------------------------------------------------------------- + TOTAL 97.97400795 1276.17914845 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 693540 +BPFP 0.6582 bits/point +EBPFP 0.6582 equivalent bits/point +MSE 1276.179148 +---------------------- -------------------------------------------------------- +Time: 1.734s Load: 0.080s, Pack+Encode: 0.601s, Decode+Unpack: 1.053s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1276.1791 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03218198-ILSVRC2012_val_00002266.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03218198-ILSVRC2012_val_00002266.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03220513-ILSVRC2012_val_00001868.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03220513-ILSVRC2012_val_00001868.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 392,460B, BPFP=0.7449 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 457,404B, BPFP=0.8682 +⌛️ [2/4] FRONTEND: Frontend time: 0.633s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.040s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.20032125 1269.08612731 + layer.39.0 377.00176142 3740.36175899 + ------------------------------------------------------------------------------------- + TOTAL 188.60104134 2504.72394315 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 849864 +BPFP 0.8066 bits/point +EBPFP 0.8066 equivalent bits/point +MSE 2504.723943 +---------------------- -------------------------------------------------------- +Time: 1.753s Load: 0.080s, Pack+Encode: 0.633s, Decode+Unpack: 1.040s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2504.7239 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03220513-ILSVRC2012_val_00001868.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03220513-ILSVRC2012_val_00001868.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03223299-ILSVRC2012_val_00001893.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03223299-ILSVRC2012_val_00001893.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 181,508B, BPFP=0.3445 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 354,884B, BPFP=0.6736 +⌛️ [2/4] FRONTEND: Frontend time: 0.621s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.053s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10735053 86.63968355 + layer.39.0 354.51621720 1972.56741983 + ------------------------------------------------------------------------------------- + TOTAL 177.31178386 1029.60355169 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 536392 +BPFP 0.5091 bits/point +EBPFP 0.5091 equivalent bits/point +MSE 1029.603552 +---------------------- -------------------------------------------------------- +Time: 1.744s Load: 0.070s, Pack+Encode: 0.621s, Decode+Unpack: 1.053s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1029.6036 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03223299-ILSVRC2012_val_00001893.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03223299-ILSVRC2012_val_00001893.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03240683-ILSVRC2012_val_00000504.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03240683-ILSVRC2012_val_00000504.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.053s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 210,228B, BPFP=0.3990 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 429,340B, BPFP=0.8149 +⌛️ [2/4] FRONTEND: Frontend time: 0.520s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.009s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10065408 25.02193991 + layer.39.0 443.53838678 2785.06171040 + ------------------------------------------------------------------------------------- + TOTAL 221.81952043 1405.04182516 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 639568 +BPFP 0.6070 bits/point +EBPFP 0.6070 equivalent bits/point +MSE 1405.041825 +---------------------- -------------------------------------------------------- +Time: 1.581s Load: 0.053s, Pack+Encode: 0.520s, Decode+Unpack: 1.009s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1405.0418 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03240683-ILSVRC2012_val_00000504.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03240683-ILSVRC2012_val_00000504.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03250847-ILSVRC2012_val_00000542.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03250847-ILSVRC2012_val_00000542.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 228,424B, BPFP=0.4336 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 478,572B, BPFP=0.9084 +⌛️ [2/4] FRONTEND: Frontend time: 0.537s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.011s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10136319 25.49955585 + layer.39.0 140.24735787 2882.48615160 + ------------------------------------------------------------------------------------- + TOTAL 70.17436053 1453.99285373 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 706996 +BPFP 0.6710 bits/point +EBPFP 0.6710 equivalent bits/point +MSE 1453.992854 +---------------------- -------------------------------------------------------- +Time: 1.609s Load: 0.062s, Pack+Encode: 0.537s, Decode+Unpack: 1.011s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1453.9929 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03250847-ILSVRC2012_val_00000542.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03250847-ILSVRC2012_val_00000542.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03255030-ILSVRC2012_val_00001045.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03255030-ILSVRC2012_val_00001045.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 201,764B, BPFP=0.3830 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 427,252B, BPFP=0.8110 +⌛️ [2/4] FRONTEND: Frontend time: 0.589s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.053s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10050351 12.68947211 + layer.39.0 12.06722622 1892.79907677 + ------------------------------------------------------------------------------------- + TOTAL 6.08386487 952.74427444 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 629016 +BPFP 0.5970 bits/point +EBPFP 0.5970 equivalent bits/point +MSE 952.744274 +---------------------- -------------------------------------------------------- +Time: 1.713s Load: 0.070s, Pack+Encode: 0.589s, Decode+Unpack: 1.053s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 952.7443 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03255030-ILSVRC2012_val_00001045.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03255030-ILSVRC2012_val_00001045.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03271574-ILSVRC2012_val_00000942.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03271574-ILSVRC2012_val_00000942.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.079s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 203,916B, BPFP=0.3870 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 361,620B, BPFP=0.6864 +⌛️ [2/4] FRONTEND: Frontend time: 0.589s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.068s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10164264 61.56309600 + layer.39.0 660.63544704 3290.32604470 + ------------------------------------------------------------------------------------- + TOTAL 330.36854484 1675.94457035 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 565536 +BPFP 0.5367 bits/point +EBPFP 0.5367 equivalent bits/point +MSE 1675.944570 +---------------------- -------------------------------------------------------- +Time: 1.736s Load: 0.079s, Pack+Encode: 0.589s, Decode+Unpack: 1.068s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1675.9446 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03271574-ILSVRC2012_val_00000942.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03271574-ILSVRC2012_val_00000942.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272010-ILSVRC2012_val_00000374.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272010-ILSVRC2012_val_00000374.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 206,680B, BPFP=0.3923 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 512,492B, BPFP=0.9728 +⌛️ [2/4] FRONTEND: Frontend time: 0.497s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.986s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10420663 62.84810040 + layer.39.0 9.63653369 1846.04956268 + ------------------------------------------------------------------------------------- + TOTAL 4.87037016 954.44883154 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 719172 +BPFP 0.6825 bits/point +EBPFP 0.6825 equivalent bits/point +MSE 954.448832 +---------------------- -------------------------------------------------------- +Time: 1.535s Load: 0.052s, Pack+Encode: 0.497s, Decode+Unpack: 0.986s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 954.4488 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272010-ILSVRC2012_val_00000374.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03272010-ILSVRC2012_val_00000374.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272562-ILSVRC2012_val_00001699.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272562-ILSVRC2012_val_00001699.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 241,568B, BPFP=0.4585 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 321,820B, BPFP=0.6108 +⌛️ [2/4] FRONTEND: Frontend time: 0.585s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.064s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11399285 330.13927357 + layer.39.0 12.79457642 1879.91848882 + ------------------------------------------------------------------------------------- + TOTAL 6.45428464 1105.02888120 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 563388 +BPFP 0.5347 bits/point +EBPFP 0.5347 equivalent bits/point +MSE 1105.028881 +---------------------- -------------------------------------------------------- +Time: 1.728s Load: 0.080s, Pack+Encode: 0.585s, Decode+Unpack: 1.064s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1105.0289 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272562-ILSVRC2012_val_00001699.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03272562-ILSVRC2012_val_00001699.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03290653-ILSVRC2012_val_00000199.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03290653-ILSVRC2012_val_00000199.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 193,112B, BPFP=0.3665 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 428,732B, BPFP=0.8138 +⌛️ [2/4] FRONTEND: Frontend time: 0.607s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.050s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09581849 12.93134168 + layer.39.0 9.30266794 2121.26530612 + ------------------------------------------------------------------------------------- + TOTAL 4.69924322 1067.09832390 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 621844 +BPFP 0.5902 bits/point +EBPFP 0.5902 equivalent bits/point +MSE 1067.098324 +---------------------- -------------------------------------------------------- +Time: 1.708s Load: 0.052s, Pack+Encode: 0.607s, Decode+Unpack: 1.050s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1067.0983 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03290653-ILSVRC2012_val_00000199.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03290653-ILSVRC2012_val_00000199.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03291819-ILSVRC2012_val_00000419.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03291819-ILSVRC2012_val_00000419.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 184,732B, BPFP=0.3506 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 333,344B, BPFP=0.6327 +⌛️ [2/4] FRONTEND: Frontend time: 0.533s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.078s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10621172 36.83670812 + layer.39.0 31.36357166 1798.32616618 + ------------------------------------------------------------------------------------- + TOTAL 15.73489169 917.58143715 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 518076 +BPFP 0.4917 bits/point +EBPFP 0.4917 equivalent bits/point +MSE 917.581437 +---------------------- -------------------------------------------------------- +Time: 1.662s Load: 0.051s, Pack+Encode: 0.533s, Decode+Unpack: 1.078s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 917.5814 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03291819-ILSVRC2012_val_00000419.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03291819-ILSVRC2012_val_00000419.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03314780-ILSVRC2012_val_00000624.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03314780-ILSVRC2012_val_00000624.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.081s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 214,376B, BPFP=0.4069 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 482,568B, BPFP=0.9160 +⌛️ [2/4] FRONTEND: Frontend time: 0.589s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.072s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10172509 24.75904246 + layer.39.0 35.60390853 2756.14868805 + ------------------------------------------------------------------------------------- + TOTAL 17.85281681 1390.45386525 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 696944 +BPFP 0.6614 bits/point +EBPFP 0.6614 equivalent bits/point +MSE 1390.453865 +---------------------- -------------------------------------------------------- +Time: 1.742s Load: 0.081s, Pack+Encode: 0.589s, Decode+Unpack: 1.072s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1390.4539 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03314780-ILSVRC2012_val_00000624.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03314780-ILSVRC2012_val_00000624.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03325584-ILSVRC2012_val_00001256.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03325584-ILSVRC2012_val_00001256.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 252,268B, BPFP=0.4788 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 480,260B, BPFP=0.9116 +⌛️ [2/4] FRONTEND: Frontend time: 0.503s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.001s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11348933 126.36698251 + layer.39.0 26.85401292 2325.83867833 + ------------------------------------------------------------------------------------- + TOTAL 13.48375113 1226.10283042 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 732528 +BPFP 0.6952 bits/point +EBPFP 0.6952 equivalent bits/point +MSE 1226.102830 +---------------------- -------------------------------------------------------- +Time: 1.556s Load: 0.052s, Pack+Encode: 0.503s, Decode+Unpack: 1.001s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1226.1028 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03325584-ILSVRC2012_val_00001256.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03325584-ILSVRC2012_val_00001256.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03337140-ILSVRC2012_val_00000132.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03337140-ILSVRC2012_val_00000132.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 188,160B, BPFP=0.3571 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 403,564B, BPFP=0.7660 +⌛️ [2/4] FRONTEND: Frontend time: 0.508s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.999s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09852950 37.09155202 + layer.39.0 10.39905343 1827.73129252 + ------------------------------------------------------------------------------------- + TOTAL 5.24879146 932.41142227 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 591724 +BPFP 0.5616 bits/point +EBPFP 0.5616 equivalent bits/point +MSE 932.411422 +---------------------- -------------------------------------------------------- +Time: 1.558s Load: 0.051s, Pack+Encode: 0.508s, Decode+Unpack: 0.999s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 932.4114 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03337140-ILSVRC2012_val_00000132.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03337140-ILSVRC2012_val_00000132.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03344393-ILSVRC2012_val_00000288.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03344393-ILSVRC2012_val_00000288.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 188,368B, BPFP=0.3575 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 370,480B, BPFP=0.7032 +⌛️ [2/4] FRONTEND: Frontend time: 0.505s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.987s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09830858 12.74982633 + layer.39.0 109.00505649 2657.57337221 + ------------------------------------------------------------------------------------- + TOTAL 54.55168253 1335.16159927 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 558848 +BPFP 0.5304 bits/point +EBPFP 0.5304 equivalent bits/point +MSE 1335.161599 +---------------------- -------------------------------------------------------- +Time: 1.543s Load: 0.051s, Pack+Encode: 0.505s, Decode+Unpack: 0.987s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1335.1616 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03344393-ILSVRC2012_val_00000288.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03344393-ILSVRC2012_val_00000288.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03345487-ILSVRC2012_val_00000764.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03345487-ILSVRC2012_val_00000764.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 228,964B, BPFP=0.4346 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 425,268B, BPFP=0.8072 +⌛️ [2/4] FRONTEND: Frontend time: 0.535s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.015s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10639974 61.90290558 + layer.39.0 14.55993569 2526.05952381 + ------------------------------------------------------------------------------------- + TOTAL 7.33316771 1293.98121470 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 654232 +BPFP 0.6209 bits/point +EBPFP 0.6209 equivalent bits/point +MSE 1293.981215 +---------------------- -------------------------------------------------------- +Time: 1.601s Load: 0.051s, Pack+Encode: 0.535s, Decode+Unpack: 1.015s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1293.9812 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03345487-ILSVRC2012_val_00000764.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03345487-ILSVRC2012_val_00000764.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03347037-ILSVRC2012_val_00000743.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03347037-ILSVRC2012_val_00000743.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.079s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 279,688B, BPFP=0.5309 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 470,924B, BPFP=0.8939 +⌛️ [2/4] FRONTEND: Frontend time: 0.598s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.056s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14351733 430.24404762 + layer.39.0 355.98426871 2529.38799806 + ------------------------------------------------------------------------------------- + TOTAL 178.06389302 1479.81602284 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 750612 +BPFP 0.7124 bits/point +EBPFP 0.7124 equivalent bits/point +MSE 1479.816023 +---------------------- -------------------------------------------------------- +Time: 1.733s Load: 0.079s, Pack+Encode: 0.598s, Decode+Unpack: 1.056s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1479.8160 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03347037-ILSVRC2012_val_00000743.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03347037-ILSVRC2012_val_00000743.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03355925-ILSVRC2012_val_00000445.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03355925-ILSVRC2012_val_00000445.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 183,628B, BPFP=0.3485 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 408,896B, BPFP=0.7761 +⌛️ [2/4] FRONTEND: Frontend time: 0.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.053s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09979894 37.69212752 + layer.39.0 9.06502540 1703.87269193 + ------------------------------------------------------------------------------------- + TOTAL 4.58241217 870.78240973 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 592524 +BPFP 0.5623 bits/point +EBPFP 0.5623 equivalent bits/point +MSE 870.782410 +---------------------- -------------------------------------------------------- +Time: 1.704s Load: 0.070s, Pack+Encode: 0.581s, Decode+Unpack: 1.053s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 870.7824 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03355925-ILSVRC2012_val_00000445.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03355925-ILSVRC2012_val_00000445.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03376595-ILSVRC2012_val_00001616.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03376595-ILSVRC2012_val_00001616.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 247,228B, BPFP=0.4693 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 431,884B, BPFP=0.8198 +⌛️ [2/4] FRONTEND: Frontend time: 0.593s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.056s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09988844 75.34976312 + layer.39.0 1408.20760447 3940.52308066 + ------------------------------------------------------------------------------------- + TOTAL 704.15374646 2007.93642189 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 679112 +BPFP 0.6445 bits/point +EBPFP 0.6445 equivalent bits/point +MSE 2007.936422 +---------------------- -------------------------------------------------------- +Time: 1.719s Load: 0.070s, Pack+Encode: 0.593s, Decode+Unpack: 1.056s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2007.9364 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03376595-ILSVRC2012_val_00001616.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03376595-ILSVRC2012_val_00001616.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03379051-ILSVRC2012_val_00002562.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03379051-ILSVRC2012_val_00002562.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 239,920B, BPFP=0.4554 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 421,528B, BPFP=0.8001 +⌛️ [2/4] FRONTEND: Frontend time: 0.528s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.998s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10889592 26.42324352 + layer.39.0 102.95462828 3137.08867833 + ------------------------------------------------------------------------------------- + TOTAL 51.53176210 1581.75596092 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 661448 +BPFP 0.6277 bits/point +EBPFP 0.6277 equivalent bits/point +MSE 1581.755961 +---------------------- -------------------------------------------------------- +Time: 1.578s Load: 0.052s, Pack+Encode: 0.528s, Decode+Unpack: 0.998s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1581.7560 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03379051-ILSVRC2012_val_00002562.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03379051-ILSVRC2012_val_00002562.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388043-ILSVRC2012_val_00001018.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388043-ILSVRC2012_val_00001018.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 198,524B, BPFP=0.3768 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 363,152B, BPFP=0.6893 +⌛️ [2/4] FRONTEND: Frontend time: 0.595s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.064s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09747427 61.87630208 + layer.39.0 21.12933142 2178.90986395 + ------------------------------------------------------------------------------------- + TOTAL 10.61340285 1120.39308301 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 561676 +BPFP 0.5331 bits/point +EBPFP 0.5331 equivalent bits/point +MSE 1120.393083 +---------------------- -------------------------------------------------------- +Time: 1.730s Load: 0.071s, Pack+Encode: 0.595s, Decode+Unpack: 1.064s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1120.3931 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388043-ILSVRC2012_val_00001018.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03388043-ILSVRC2012_val_00001018.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388183-ILSVRC2012_val_00002799.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388183-ILSVRC2012_val_00002799.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 238,124B, BPFP=0.4520 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 410,276B, BPFP=0.7787 +⌛️ [2/4] FRONTEND: Frontend time: 0.573s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.072s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10066175 27.62911884 + layer.39.0 786.68810739 3581.12269193 + ------------------------------------------------------------------------------------- + TOTAL 393.39438457 1804.37590538 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 648400 +BPFP 0.6154 bits/point +EBPFP 0.6154 equivalent bits/point +MSE 1804.375905 +---------------------- -------------------------------------------------------- +Time: 1.714s Load: 0.070s, Pack+Encode: 0.573s, Decode+Unpack: 1.072s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1804.3759 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388183-ILSVRC2012_val_00002799.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03388183-ILSVRC2012_val_00002799.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388549-ILSVRC2012_val_00002945.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388549-ILSVRC2012_val_00002945.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.079s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 214,400B, BPFP=0.4069 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 382,164B, BPFP=0.7254 +⌛️ [2/4] FRONTEND: Frontend time: 0.519s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.012s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09849939 37.61844023 + layer.39.0 10.79426799 1905.77332362 + ------------------------------------------------------------------------------------- + TOTAL 5.44638369 971.69588192 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 596564 +BPFP 0.5662 bits/point +EBPFP 0.5662 equivalent bits/point +MSE 971.695882 +---------------------- -------------------------------------------------------- +Time: 1.611s Load: 0.079s, Pack+Encode: 0.519s, Decode+Unpack: 1.012s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 971.6959 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388549-ILSVRC2012_val_00002945.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03388549-ILSVRC2012_val_00002945.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03393912-ILSVRC2012_val_00000047.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03393912-ILSVRC2012_val_00000047.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 202,712B, BPFP=0.3848 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 312,860B, BPFP=0.5938 +⌛️ [2/4] FRONTEND: Frontend time: 0.582s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.063s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09729456 97.96641916 + layer.39.0 38.26720800 2597.91302235 + ------------------------------------------------------------------------------------- + TOTAL 19.18225128 1347.93972075 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 515572 +BPFP 0.4893 bits/point +EBPFP 0.4893 equivalent bits/point +MSE 1347.939721 +---------------------- -------------------------------------------------------- +Time: 1.725s Load: 0.080s, Pack+Encode: 0.582s, Decode+Unpack: 1.063s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1347.9397 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03393912-ILSVRC2012_val_00000047.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03393912-ILSVRC2012_val_00000047.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03394916-ILSVRC2012_val_00000957.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03394916-ILSVRC2012_val_00000957.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 198,056B, BPFP=0.3759 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 446,496B, BPFP=0.8475 +⌛️ [2/4] FRONTEND: Frontend time: 0.588s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.069s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10421823 37.65454552 + layer.39.0 9.72561820 1896.64795918 + ------------------------------------------------------------------------------------- + TOTAL 4.91491822 967.15125235 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 644552 +BPFP 0.6117 bits/point +EBPFP 0.6117 equivalent bits/point +MSE 967.151252 +---------------------- -------------------------------------------------------- +Time: 1.727s Load: 0.071s, Pack+Encode: 0.588s, Decode+Unpack: 1.069s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 967.1513 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03394916-ILSVRC2012_val_00000957.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03394916-ILSVRC2012_val_00000957.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03404251-ILSVRC2012_val_00000641.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03404251-ILSVRC2012_val_00000641.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 212,532B, BPFP=0.4034 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 482,456B, BPFP=0.9157 +⌛️ [2/4] FRONTEND: Frontend time: 0.586s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.077s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10764784 170.57153486 + layer.39.0 585.45553936 2944.20068027 + ------------------------------------------------------------------------------------- + TOTAL 292.78159360 1557.38610757 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 694988 +BPFP 0.6596 bits/point +EBPFP 0.6596 equivalent bits/point +MSE 1557.386108 +---------------------- -------------------------------------------------------- +Time: 1.742s Load: 0.080s, Pack+Encode: 0.586s, Decode+Unpack: 1.077s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1557.3861 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03404251-ILSVRC2012_val_00000641.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03404251-ILSVRC2012_val_00000641.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03417042-ILSVRC2012_val_00001144.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03417042-ILSVRC2012_val_00001144.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 212,180B, BPFP=0.4027 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 345,516B, BPFP=0.6558 +⌛️ [2/4] FRONTEND: Frontend time: 0.572s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.065s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10091509 24.76413501 + layer.39.0 202.93364310 2660.78522838 + ------------------------------------------------------------------------------------- + TOTAL 101.51727910 1342.77468169 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 557696 +BPFP 0.5293 bits/point +EBPFP 0.5293 equivalent bits/point +MSE 1342.774682 +---------------------- -------------------------------------------------------- +Time: 1.689s Load: 0.052s, Pack+Encode: 0.572s, Decode+Unpack: 1.065s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1342.7747 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03417042-ILSVRC2012_val_00001144.zst + to output-fixed/dinov3-total/lambda0.007/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03417042-ILSVRC2012_val_00001144.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 0.6009 bits/point +Avg EBPFP 0.6009 equivalent bits/point +Avg MSE 1294.736897 +Avg Time 1.812s +------------------------ ---------------------------- diff --git a/lambda0.01/elic-featurecoding-8bit-individual/cls_in1ka/dtufc_elic-featurecoding_dinov3-total_individual.log b/lambda0.01/elic-featurecoding-8bit-individual/cls_in1ka/dtufc_elic-featurecoding_dinov3-total_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..dd23967022689d0b39b0acd7602d798245bc3ac5 --- /dev/null +++ b/lambda0.01/elic-featurecoding-8bit-individual/cls_in1ka/dtufc_elic-featurecoding_dinov3-total_individual.log @@ -0,0 +1,9344 @@ +Experiment: dtufc_elic-featurecoding_dinov3-total_individual +Log file: output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/dtufc_elic-featurecoding_dinov3-total_individual.log +DTUFCCodecConfig: + arch: elic-featurecoding + handler: dinov3-total + checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.01_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.01_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 506 +Loaded elic-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.9' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.19' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.29' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.39' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json +Loaded per-key mappings: model=dinov3-total + Keys: ['layer.9', 'layer.19', 'layer.29', 'layer.39'] +---------------- ------------------------------------------------------------------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +Checkpoint codec_weights/elic_hybrid/elic2022-official_lambda0.01_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-DINOv3/imagenet1k-a +Output output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka +---------------- ------------------------------------------------------------------------------------------------------------------- +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01498041-0.006639_koala _ American bullfrog_0.6658246.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01498041-0.006639_koala _ American bullfrog_0.6658246.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 332,512B, BPFP=0.6311 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 445,388B, BPFP=0.8454 +⌛️ [2/4] FRONTEND: Frontend time: 3.131s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.537s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09594801 12.21202225 + layer.39.0 58.94484178 3637.54689018 + ------------------------------------------------------------------------------------- + TOTAL 29.52039490 1824.87945622 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 777900 +BPFP 0.7383 bits/point +EBPFP 0.7383 equivalent bits/point +MSE 1824.879456 +---------------------- -------------------------------------------------------- +Time: 5.740s Load: 0.071s, Pack+Encode: 3.131s, Decode+Unpack: 2.537s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1824.8795 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01498041-0.006639_koala _ American bullfrog_0.6658246.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n01498041-0.006639_koala _ American bullfrog_0.6658246.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01531178-0.000765_fox squirrel _ ant_0.9486805.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01531178-0.000765_fox squirrel _ ant_0.9486805.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.078s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 318,372B, BPFP=0.6043 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 396,600B, BPFP=0.7528 +⌛️ [2/4] FRONTEND: Frontend time: 2.631s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.529s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09773727 8.88293018 + layer.39.0 17.17825445 2238.91861030 + ------------------------------------------------------------------------------------- + TOTAL 8.63799586 1123.90077024 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 714972 +BPFP 0.6785 bits/point +EBPFP 0.6785 equivalent bits/point +MSE 1123.900770 +---------------------- -------------------------------------------------------- +Time: 5.239s Load: 0.078s, Pack+Encode: 2.631s, Decode+Unpack: 2.529s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1123.9008 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01531178-0.000765_fox squirrel _ ant_0.9486805.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n01531178-0.000765_fox squirrel _ ant_0.9486805.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01534433-0.004573_stingray _ stingray_0.97124094.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01534433-0.004573_stingray _ stingray_0.97124094.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 224,220B, BPFP=0.4256 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 280,220B, BPFP=0.5319 +⌛️ [2/4] FRONTEND: Frontend time: 2.620s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.504s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09515371 0.83764109 + layer.39.0 6.87362484 1129.65403304 + ------------------------------------------------------------------------------------- + TOTAL 3.48438928 565.24583706 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 504440 +BPFP 0.4787 bits/point +EBPFP 0.4787 equivalent bits/point +MSE 565.245837 +---------------------- -------------------------------------------------------- +Time: 5.194s Load: 0.070s, Pack+Encode: 2.620s, Decode+Unpack: 2.504s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 565.2458 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01534433-0.004573_stingray _ stingray_0.97124094.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n01534433-0.004573_stingray _ stingray_0.97124094.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01558993-0.000522_bow _ bow_0.9033333.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01558993-0.000522_bow _ bow_0.9033333.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 358,320B, BPFP=0.6801 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 290,612B, BPFP=0.5516 +⌛️ [2/4] FRONTEND: Frontend time: 2.622s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.516s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09874929 38.37968446 + layer.39.0 7.31778236 1210.98117104 + ------------------------------------------------------------------------------------- + TOTAL 3.70826583 624.68042775 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 648932 +BPFP 0.6159 bits/point +EBPFP 0.6159 equivalent bits/point +MSE 624.680428 +---------------------- -------------------------------------------------------- +Time: 5.200s Load: 0.061s, Pack+Encode: 2.622s, Decode+Unpack: 2.516s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 624.6804 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01558993-0.000522_bow _ bow_0.9033333.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n01558993-0.000522_bow _ bow_0.9033333.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01580077-0.004583_African bush elephant _ African bush elephant_0.59939015.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01580077-0.004583_African bush elephant _ African bush elephant_0.59939015.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 325,952B, BPFP=0.6187 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 365,768B, BPFP=0.6943 +⌛️ [2/4] FRONTEND: Frontend time: 2.623s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.531s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10720986 26.92765276 + layer.39.0 24.46209533 2373.74003887 + ------------------------------------------------------------------------------------- + TOTAL 12.28465260 1200.33384582 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 691720 +BPFP 0.6565 bits/point +EBPFP 0.6565 equivalent bits/point +MSE 1200.333846 +---------------------- -------------------------------------------------------- +Time: 5.203s Load: 0.050s, Pack+Encode: 2.623s, Decode+Unpack: 2.531s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1200.3338 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01580077-0.004583_African bush elephant _ African bush elephant_0.59939015.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n01580077-0.004583_African bush elephant _ African bush elephant_0.59939015.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01614925-0.049724_white-headed capuchin _ white-headed capuchin_0.9309422.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01614925-0.049724_white-headed capuchin _ white-headed capuchin_0.9309422.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 368,768B, BPFP=0.7000 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 298,672B, BPFP=0.5669 +⌛️ [2/4] FRONTEND: Frontend time: 2.608s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.506s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09739119 73.18721908 + layer.39.0 8.81423010 1275.25218659 + ------------------------------------------------------------------------------------- + TOTAL 4.45581065 674.21970284 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 667440 +BPFP 0.6334 bits/point +EBPFP 0.6334 equivalent bits/point +MSE 674.219703 +---------------------- -------------------------------------------------------- +Time: 5.185s Load: 0.071s, Pack+Encode: 2.608s, Decode+Unpack: 2.506s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 674.2197 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01614925-0.049724_white-headed capuchin _ white-headed capuchin_0.9309422.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n01614925-0.049724_white-headed capuchin _ white-headed capuchin_0.9309422.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01631663-0.004606_American bullfrog _ American bullfrog_0.8789855.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01631663-0.004606_American bullfrog _ American bullfrog_0.8789855.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.058s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 288,772B, BPFP=0.5481 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 352,732B, BPFP=0.6695 +⌛️ [2/4] FRONTEND: Frontend time: 2.604s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.506s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09716670 12.55600667 + layer.39.0 20.45897868 1589.05259961 + ------------------------------------------------------------------------------------- + TOTAL 10.27807269 800.80430314 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 641504 +BPFP 0.6088 bits/point +EBPFP 0.6088 equivalent bits/point +MSE 800.804303 +---------------------- -------------------------------------------------------- +Time: 5.168s Load: 0.058s, Pack+Encode: 2.604s, Decode+Unpack: 2.506s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 800.8043 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01631663-0.004606_American bullfrog _ American bullfrog_0.8789855.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n01631663-0.004606_American bullfrog _ American bullfrog_0.8789855.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01669191-0.029754_sandal _ sandal_0.38198605.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01669191-0.029754_sandal _ sandal_0.38198605.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 439,260B, BPFP=0.8338 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 316,196B, BPFP=0.6002 +⌛️ [2/4] FRONTEND: Frontend time: 2.635s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.542s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10877632 429.33230078 + layer.39.0 13.16500205 1584.88982021 + ------------------------------------------------------------------------------------- + TOTAL 6.63688918 1007.11106050 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 755456 +BPFP 0.7170 bits/point +EBPFP 0.7170 equivalent bits/point +MSE 1007.111060 +---------------------- -------------------------------------------------------- +Time: 5.237s Load: 0.060s, Pack+Encode: 2.635s, Decode+Unpack: 2.542s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1007.1111 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01669191-0.029754_sandal _ sandal_0.38198605.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n01669191-0.029754_sandal _ sandal_0.38198605.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770081-0.000571_syringe _ syringe_0.7369336.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770081-0.000571_syringe _ syringe_0.7369336.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 252,528B, BPFP=0.4793 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 433,344B, BPFP=0.8225 +⌛️ [2/4] FRONTEND: Frontend time: 2.667s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.527s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09508557 12.23007775 + layer.39.0 60.03878538 2546.35204082 + ------------------------------------------------------------------------------------- + TOTAL 30.06693547 1279.29105928 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 685872 +BPFP 0.6509 bits/point +EBPFP 0.6509 equivalent bits/point +MSE 1279.291059 +---------------------- -------------------------------------------------------- +Time: 5.245s Load: 0.050s, Pack+Encode: 2.667s, Decode+Unpack: 2.527s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1279.2911 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770081-0.000571_syringe _ syringe_0.7369336.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n01770081-0.000571_syringe _ syringe_0.7369336.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770393-0.000908_harvestman _ harvestman_0.8782497.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770393-0.000908_harvestman _ harvestman_0.8782497.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.057s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 328,496B, BPFP=0.6235 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 372,696B, BPFP=0.7074 +⌛️ [2/4] FRONTEND: Frontend time: 2.627s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.510s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11350316 24.91765746 + layer.39.0 19.73148992 1979.80138484 + ------------------------------------------------------------------------------------- + TOTAL 9.92249654 1002.35952115 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 701192 +BPFP 0.6655 bits/point +EBPFP 0.6655 equivalent bits/point +MSE 1002.359521 +---------------------- -------------------------------------------------------- +Time: 5.194s Load: 0.057s, Pack+Encode: 2.627s, Decode+Unpack: 2.510s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1002.3595 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770393-0.000908_harvestman _ harvestman_0.8782497.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n01770393-0.000908_harvestman _ harvestman_0.8782497.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01784675-0.027853_syringe _ syringe_0.9584382.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01784675-0.027853_syringe _ syringe_0.9584382.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.053s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 417,436B, BPFP=0.7923 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 700,824B, BPFP=1.3302 +⌛️ [2/4] FRONTEND: Frontend time: 2.630s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.522s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11002613 401.34107750 + layer.39.0 26.08665877 7556.42274052 + ------------------------------------------------------------------------------------- + TOTAL 13.09834245 3978.88190901 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1118260 +BPFP 1.0613 bits/point +EBPFP 1.0613 equivalent bits/point +MSE 3978.881909 +---------------------- -------------------------------------------------------- +Time: 5.205s Load: 0.053s, Pack+Encode: 2.630s, Decode+Unpack: 2.522s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3978.8819 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01784675-0.027853_syringe _ syringe_0.9584382.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n01784675-0.027853_syringe _ syringe_0.9584382.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01819313-0.053742_koala _ koala_0.98647016.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01819313-0.053742_koala _ koala_0.98647016.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.057s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 400,296B, BPFP=0.7598 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 462,848B, BPFP=0.8785 +⌛️ [2/4] FRONTEND: Frontend time: 2.646s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.540s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14565475 201.96937257 + layer.39.0 25.01023445 3946.94460641 + ------------------------------------------------------------------------------------- + TOTAL 12.57794460 2074.45698949 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 863144 +BPFP 0.8192 bits/point +EBPFP 0.8192 equivalent bits/point +MSE 2074.456989 +---------------------- -------------------------------------------------------- +Time: 5.243s Load: 0.057s, Pack+Encode: 2.646s, Decode+Unpack: 2.540s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2074.4570 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01819313-0.053742_koala _ koala_0.98647016.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n01819313-0.053742_koala _ koala_0.98647016.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01820546-0.012522_toucan _ toucan_0.63882655.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01820546-0.012522_toucan _ toucan_0.63882655.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 310,648B, BPFP=0.5896 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 384,228B, BPFP=0.7293 +⌛️ [2/4] FRONTEND: Frontend time: 2.615s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.521s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09696376 12.20218450 + layer.39.0 16.65489097 2102.36151603 + ------------------------------------------------------------------------------------- + TOTAL 8.37592737 1057.28185027 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 694876 +BPFP 0.6595 bits/point +EBPFP 0.6595 equivalent bits/point +MSE 1057.281850 +---------------------- -------------------------------------------------------- +Time: 5.188s Load: 0.052s, Pack+Encode: 2.615s, Decode+Unpack: 2.521s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1057.2819 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01820546-0.012522_toucan _ toucan_0.63882655.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n01820546-0.012522_toucan _ toucan_0.63882655.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01833805-0.013248_toucan _ lorikeet_0.77773976.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01833805-0.013248_toucan _ lorikeet_0.77773976.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 293,084B, BPFP=0.5563 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 367,440B, BPFP=0.6974 +⌛️ [2/4] FRONTEND: Frontend time: 2.619s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.513s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09866240 12.18340394 + layer.39.0 7.67772963 1599.18865403 + ------------------------------------------------------------------------------------- + TOTAL 3.88819601 805.68602899 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 660524 +BPFP 0.6269 bits/point +EBPFP 0.6269 equivalent bits/point +MSE 805.686029 +---------------------- -------------------------------------------------------- +Time: 5.204s Load: 0.071s, Pack+Encode: 2.619s, Decode+Unpack: 2.513s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 805.6860 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01833805-0.013248_toucan _ lorikeet_0.77773976.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n01833805-0.013248_toucan _ lorikeet_0.77773976.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01843383-0.013605_white-headed capuchin _ white-headed capuchin_0.9984768.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01843383-0.013605_white-headed capuchin _ white-headed capuchin_0.9984768.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 377,920B, BPFP=0.7173 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 417,488B, BPFP=0.7924 +⌛️ [2/4] FRONTEND: Frontend time: 2.633s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.517s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11910487 20.96525374 + layer.39.0 9.20068692 2595.69727891 + ------------------------------------------------------------------------------------- + TOTAL 4.65989589 1308.33126632 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 795408 +BPFP 0.7549 bits/point +EBPFP 0.7549 equivalent bits/point +MSE 1308.331266 +---------------------- -------------------------------------------------------- +Time: 5.202s Load: 0.052s, Pack+Encode: 2.633s, Decode+Unpack: 2.517s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1308.3313 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01843383-0.013605_white-headed capuchin _ white-headed capuchin_0.9984768.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n01843383-0.013605_white-headed capuchin _ white-headed capuchin_0.9984768.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01924916-0.000644_jay _ jay_0.82223135.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01924916-0.000644_jay _ jay_0.82223135.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 392,428B, BPFP=0.7449 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 310,128B, BPFP=0.5886 +⌛️ [2/4] FRONTEND: Frontend time: 2.611s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.527s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11488669 203.34865464 + layer.39.0 141.08750911 1699.05308552 + ------------------------------------------------------------------------------------- + TOTAL 70.60119790 951.20087008 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 702556 +BPFP 0.6668 bits/point +EBPFP 0.6668 equivalent bits/point +MSE 951.200870 +---------------------- -------------------------------------------------------- +Time: 5.200s Load: 0.062s, Pack+Encode: 2.611s, Decode+Unpack: 2.527s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 951.2009 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01924916-0.000644_jay _ jay_0.82223135.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n01924916-0.000644_jay _ jay_0.82223135.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01944390-0.002567_American robin _ American robin_0.5629079.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01944390-0.002567_American robin _ American robin_0.5629079.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 334,396B, BPFP=0.6347 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 347,672B, BPFP=0.6599 +⌛️ [2/4] FRONTEND: Frontend time: 2.611s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.509s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10732387 2.87714151 + layer.39.0 16.74672581 1479.13010204 + ------------------------------------------------------------------------------------- + TOTAL 8.42702484 741.00362178 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 682068 +BPFP 0.6473 bits/point +EBPFP 0.6473 equivalent bits/point +MSE 741.003622 +---------------------- -------------------------------------------------------- +Time: 5.172s Load: 0.052s, Pack+Encode: 2.611s, Decode+Unpack: 2.509s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 741.0036 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01944390-0.002567_American robin _ American robin_0.5629079.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n01944390-0.002567_American robin _ American robin_0.5629079.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01985128-0.001579_centipede _ centipede_0.85936093.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01985128-0.001579_centipede _ centipede_0.85936093.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 329,252B, BPFP=0.6249 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 340,708B, BPFP=0.6467 +⌛️ [2/4] FRONTEND: Frontend time: 2.622s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.513s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09645609 25.23151269 + layer.39.0 23.47999613 3096.56413994 + ------------------------------------------------------------------------------------- + TOTAL 11.78822611 1560.89782632 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 669960 +BPFP 0.6358 bits/point +EBPFP 0.6358 equivalent bits/point +MSE 1560.897826 +---------------------- -------------------------------------------------------- +Time: 5.197s Load: 0.062s, Pack+Encode: 2.622s, Decode+Unpack: 2.513s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1560.8978 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01985128-0.001579_centipede _ centipede_0.85936093.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n01985128-0.001579_centipede _ centipede_0.85936093.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02037110-0.003503_snowmobile _ snowmobile_0.5200631.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02037110-0.003503_snowmobile _ snowmobile_0.5200631.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 179,676B, BPFP=0.3410 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 313,916B, BPFP=0.5958 +⌛️ [2/4] FRONTEND: Frontend time: 2.600s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.501s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09471867 12.38111182 + layer.39.0 17.04498261 1358.71987366 + ------------------------------------------------------------------------------------- + TOTAL 8.56985064 685.55049274 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 493592 +BPFP 0.4684 bits/point +EBPFP 0.4684 equivalent bits/point +MSE 685.550493 +---------------------- -------------------------------------------------------- +Time: 5.153s Load: 0.051s, Pack+Encode: 2.600s, Decode+Unpack: 2.501s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 685.5505 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02037110-0.003503_snowmobile _ snowmobile_0.5200631.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n02037110-0.003503_snowmobile _ snowmobile_0.5200631.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02123394-0.015363_marmot _ marmot_0.82052565.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02123394-0.015363_marmot _ marmot_0.82052565.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 281,724B, BPFP=0.5347 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 378,520B, BPFP=0.7185 +⌛️ [2/4] FRONTEND: Frontend time: 2.598s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.509s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10209646 24.39016187 + layer.39.0 11.38238543 1932.85957240 + ------------------------------------------------------------------------------------- + TOTAL 5.74224095 978.62486713 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 660244 +BPFP 0.6266 bits/point +EBPFP 0.6266 equivalent bits/point +MSE 978.624867 +---------------------- -------------------------------------------------------- +Time: 5.169s Load: 0.062s, Pack+Encode: 2.598s, Decode+Unpack: 2.509s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 978.6249 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02123394-0.015363_marmot _ marmot_0.82052565.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n02123394-0.015363_marmot _ marmot_0.82052565.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02165456-0.000157_corn _ corn_0.9868978.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02165456-0.000157_corn _ corn_0.9868978.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 346,996B, BPFP=0.6586 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 391,724B, BPFP=0.7435 +⌛️ [2/4] FRONTEND: Frontend time: 2.628s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.509s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10346756 14.66736136 + layer.39.0 776.17699223 2975.28474247 + ------------------------------------------------------------------------------------- + TOTAL 388.14022989 1494.97605192 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 738720 +BPFP 0.7011 bits/point +EBPFP 0.7011 equivalent bits/point +MSE 1494.976052 +---------------------- -------------------------------------------------------- +Time: 5.190s Load: 0.052s, Pack+Encode: 2.628s, Decode+Unpack: 2.509s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1494.9761 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02165456-0.000157_corn _ corn_0.9868978.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n02165456-0.000157_corn _ corn_0.9868978.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02219486-0.000060_cliff _ cliff_0.99684334.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02219486-0.000060_cliff _ cliff_0.99684334.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 293,624B, BPFP=0.5573 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 279,036B, BPFP=0.5296 +⌛️ [2/4] FRONTEND: Frontend time: 2.624s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.505s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09584527 24.87801605 + layer.39.0 31.94620460 1630.52587464 + ------------------------------------------------------------------------------------- + TOTAL 16.02102494 827.70194534 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 572660 +BPFP 0.5435 bits/point +EBPFP 0.5435 equivalent bits/point +MSE 827.701945 +---------------------- -------------------------------------------------------- +Time: 5.190s Load: 0.062s, Pack+Encode: 2.624s, Decode+Unpack: 2.505s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 827.7019 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02219486-0.000060_cliff _ cliff_0.99684334.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n02219486-0.000060_cliff _ cliff_0.99684334.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02226429-0.000085_stick insect _ stick insect_0.99900275.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02226429-0.000085_stick insect _ stick insect_0.99900275.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.053s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 304,744B, BPFP=0.5784 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 581,892B, BPFP=1.1045 +⌛️ [2/4] FRONTEND: Frontend time: 2.632s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.510s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09547379 0.84849627 + layer.39.0 19.16722850 3854.39067055 + ------------------------------------------------------------------------------------- + TOTAL 9.63135114 1927.61958341 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 886636 +BPFP 0.8415 bits/point +EBPFP 0.8415 equivalent bits/point +MSE 1927.619583 +---------------------- -------------------------------------------------------- +Time: 5.195s Load: 0.053s, Pack+Encode: 2.632s, Decode+Unpack: 2.510s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1927.6196 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02226429-0.000085_stick insect _ stick insect_0.99900275.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n02226429-0.000085_stick insect _ stick insect_0.99900275.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02231487-0.000080_manhole cover _ manhole cover_0.7554716.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02231487-0.000080_manhole cover _ manhole cover_0.7554716.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 286,728B, BPFP=0.5442 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 415,604B, BPFP=0.7888 +⌛️ [2/4] FRONTEND: Frontend time: 2.610s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.527s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09512618 12.38624803 + layer.39.0 210.79875790 2314.79008746 + ------------------------------------------------------------------------------------- + TOTAL 105.44694204 1163.58816774 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 702332 +BPFP 0.6665 bits/point +EBPFP 0.6665 equivalent bits/point +MSE 1163.588168 +---------------------- -------------------------------------------------------- +Time: 5.197s Load: 0.061s, Pack+Encode: 2.610s, Decode+Unpack: 2.527s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1163.5882 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02231487-0.000080_manhole cover _ manhole cover_0.7554716.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n02231487-0.000080_manhole cover _ manhole cover_0.7554716.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02233338-0.002901_manhole cover _ manhole cover_0.69458175.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02233338-0.002901_manhole cover _ manhole cover_0.69458175.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.058s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 255,636B, BPFP=0.4852 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 416,704B, BPFP=0.7909 +⌛️ [2/4] FRONTEND: Frontend time: 2.622s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.506s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09539769 12.22462171 + layer.39.0 58.97704841 3127.22691934 + ------------------------------------------------------------------------------------- + TOTAL 29.53622305 1569.72577053 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 672340 +BPFP 0.6381 bits/point +EBPFP 0.6381 equivalent bits/point +MSE 1569.725771 +---------------------- -------------------------------------------------------- +Time: 5.186s Load: 0.058s, Pack+Encode: 2.622s, Decode+Unpack: 2.506s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1569.7258 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02233338-0.002901_manhole cover _ manhole cover_0.69458175.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n02233338-0.002901_manhole cover _ manhole cover_0.69458175.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02236044-0.000522_sundial _ sundial_0.96381366.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02236044-0.000522_sundial _ sundial_0.96381366.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 343,248B, BPFP=0.6515 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 420,080B, BPFP=0.7973 +⌛️ [2/4] FRONTEND: Frontend time: 2.633s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.522s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09795647 37.09189368 + layer.39.0 53.12385356 2246.29494655 + ------------------------------------------------------------------------------------- + TOTAL 26.61090502 1141.69342011 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 763328 +BPFP 0.7244 bits/point +EBPFP 0.7244 equivalent bits/point +MSE 1141.693420 +---------------------- -------------------------------------------------------- +Time: 5.216s Load: 0.061s, Pack+Encode: 2.633s, Decode+Unpack: 2.522s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1141.6934 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02236044-0.000522_sundial _ sundial_0.96381366.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n02236044-0.000522_sundial _ sundial_0.96381366.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02259212-0.000032_chain _ chain_0.6590295.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02259212-0.000032_chain _ chain_0.6590295.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 291,068B, BPFP=0.5525 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 498,728B, BPFP=0.9466 +⌛️ [2/4] FRONTEND: Frontend time: 2.625s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.524s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09523673 1.25349959 + layer.39.0 80.66082058 4489.20019436 + ------------------------------------------------------------------------------------- + TOTAL 40.37802865 2245.22684697 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 789796 +BPFP 0.7495 bits/point +EBPFP 0.7495 equivalent bits/point +MSE 2245.226847 +---------------------- -------------------------------------------------------- +Time: 5.211s Load: 0.062s, Pack+Encode: 2.625s, Decode+Unpack: 2.524s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2245.2268 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02259212-0.000032_chain _ chain_0.6590295.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n02259212-0.000032_chain _ chain_0.6590295.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02279972-0.000576_apron _ apron_0.7661352.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02279972-0.000576_apron _ apron_0.7661352.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 340,008B, BPFP=0.6454 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 513,776B, BPFP=0.9752 +⌛️ [2/4] FRONTEND: Frontend time: 2.629s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.517s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12772729 25.95709578 + layer.39.0 1038.59135083 3626.87317784 + ------------------------------------------------------------------------------------- + TOTAL 519.35953906 1826.41513681 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 853784 +BPFP 0.8103 bits/point +EBPFP 0.8103 equivalent bits/point +MSE 1826.415137 +---------------------- -------------------------------------------------------- +Time: 5.208s Load: 0.061s, Pack+Encode: 2.629s, Decode+Unpack: 2.517s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1826.4151 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02279972-0.000576_apron _ apron_0.7661352.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n02279972-0.000576_apron _ apron_0.7661352.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02280649-0.001599_custard apple _ leafhopper_0.9128452.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02280649-0.001599_custard apple _ leafhopper_0.9128452.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 275,356B, BPFP=0.5226 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 547,136B, BPFP=1.0385 +⌛️ [2/4] FRONTEND: Frontend time: 2.633s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.514s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09488542 0.83666707 + layer.39.0 1031.59973275 4704.95286686 + ------------------------------------------------------------------------------------- + TOTAL 515.84730909 2352.89476697 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 822492 +BPFP 0.7806 bits/point +EBPFP 0.7806 equivalent bits/point +MSE 2352.894767 +---------------------- -------------------------------------------------------- +Time: 5.199s Load: 0.051s, Pack+Encode: 2.633s, Decode+Unpack: 2.514s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2352.8948 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02280649-0.001599_custard apple _ leafhopper_0.9128452.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n02280649-0.001599_custard apple _ leafhopper_0.9128452.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02317335-0.033681_flatworm _ flatworm_0.6070924.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02317335-0.033681_flatworm _ flatworm_0.6070924.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 274,316B, BPFP=0.5207 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 377,156B, BPFP=0.7159 +⌛️ [2/4] FRONTEND: Frontend time: 2.605s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.505s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09575805 8.69072294 + layer.39.0 62.35741238 2343.63216715 + ------------------------------------------------------------------------------------- + TOTAL 31.22658522 1176.16144505 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 651472 +BPFP 0.6183 bits/point +EBPFP 0.6183 equivalent bits/point +MSE 1176.161445 +---------------------- -------------------------------------------------------- +Time: 5.162s Load: 0.051s, Pack+Encode: 2.605s, Decode+Unpack: 2.505s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1176.1614 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02317335-0.033681_flatworm _ flatworm_0.6070924.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n02317335-0.033681_flatworm _ flatworm_0.6070924.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02325366-0.000350_flatworm _ flatworm_0.87634724.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02325366-0.000350_flatworm _ flatworm_0.87634724.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 222,072B, BPFP=0.4215 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 299,696B, BPFP=0.5688 +⌛️ [2/4] FRONTEND: Frontend time: 2.603s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.507s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09712043 8.82363129 + layer.39.0 30.59439155 1568.63411079 + ------------------------------------------------------------------------------------- + TOTAL 15.34575599 788.72887104 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 521768 +BPFP 0.4952 bits/point +EBPFP 0.4952 equivalent bits/point +MSE 788.728871 +---------------------- -------------------------------------------------------- +Time: 5.161s Load: 0.051s, Pack+Encode: 2.603s, Decode+Unpack: 2.507s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 788.7289 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02325366-0.000350_flatworm _ flatworm_0.87634724.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n02325366-0.000350_flatworm _ flatworm_0.87634724.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02346627-0.011107_fountain _ skunk_0.28641737.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02346627-0.011107_fountain _ skunk_0.28641737.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 200,316B, BPFP=0.3802 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 261,600B, BPFP=0.4965 +⌛️ [2/4] FRONTEND: Frontend time: 2.603s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.519s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09705289 12.51722888 + layer.39.0 9.52721088 1151.01190476 + ------------------------------------------------------------------------------------- + TOTAL 4.81213189 581.76456682 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 461916 +BPFP 0.4384 bits/point +EBPFP 0.4384 equivalent bits/point +MSE 581.764567 +---------------------- -------------------------------------------------------- +Time: 5.183s Load: 0.061s, Pack+Encode: 2.603s, Decode+Unpack: 2.519s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 581.7646 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02346627-0.011107_fountain _ skunk_0.28641737.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n02346627-0.011107_fountain _ skunk_0.28641737.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02445715-0.036432_shovel _ tarantula_0.26785344.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02445715-0.036432_shovel _ tarantula_0.26785344.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 351,620B, BPFP=0.6674 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 267,816B, BPFP=0.5083 +⌛️ [2/4] FRONTEND: Frontend time: 2.615s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.504s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09708806 13.30141426 + layer.39.0 8.00606437 2584.40281827 + ------------------------------------------------------------------------------------- + TOTAL 4.05157622 1298.85211627 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 619436 +BPFP 0.5879 bits/point +EBPFP 0.5879 equivalent bits/point +MSE 1298.852116 +---------------------- -------------------------------------------------------- +Time: 5.181s Load: 0.061s, Pack+Encode: 2.615s, Decode+Unpack: 2.504s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1298.8521 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02445715-0.036432_shovel _ tarantula_0.26785344.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n02445715-0.036432_shovel _ tarantula_0.26785344.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02454379-0.082010_koala _ koala_0.7052893.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02454379-0.082010_koala _ koala_0.7052893.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 456,476B, BPFP=0.8664 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 315,488B, BPFP=0.5988 +⌛️ [2/4] FRONTEND: Frontend time: 2.610s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.525s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14585212 283.37533406 + layer.39.0 44.19989826 1462.92116132 + ------------------------------------------------------------------------------------- + TOTAL 22.17287519 873.14824769 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 771964 +BPFP 0.7326 bits/point +EBPFP 0.7326 equivalent bits/point +MSE 873.148248 +---------------------- -------------------------------------------------------- +Time: 5.197s Load: 0.061s, Pack+Encode: 2.610s, Decode+Unpack: 2.525s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 873.1482 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02454379-0.082010_koala _ koala_0.7052893.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n02454379-0.082010_koala _ koala_0.7052893.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02782093-0.007542_American alligator _ American alligator_0.49872985.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02782093-0.007542_American alligator _ American alligator_0.49872985.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 230,008B, BPFP=0.4366 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 341,188B, BPFP=0.6476 +⌛️ [2/4] FRONTEND: Frontend time: 2.619s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.490s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09848133 12.65380053 + layer.39.0 9.18780844 1811.85981535 + ------------------------------------------------------------------------------------- + TOTAL 4.64314488 912.25680794 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 571196 +BPFP 0.5421 bits/point +EBPFP 0.5421 equivalent bits/point +MSE 912.256808 +---------------------- -------------------------------------------------------- +Time: 5.161s Load: 0.052s, Pack+Encode: 2.619s, Decode+Unpack: 2.490s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 912.2568 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02782093-0.007542_American alligator _ American alligator_0.49872985.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n02782093-0.007542_American alligator _ American alligator_0.49872985.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02787622-0.004599_marimba _ accordion_0.25991488.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02787622-0.004599_marimba _ accordion_0.25991488.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 356,556B, BPFP=0.6768 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 638,140B, BPFP=1.2112 +⌛️ [2/4] FRONTEND: Frontend time: 2.642s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.510s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12856446 98.35329355 + layer.39.0 1004.59450923 5434.89504373 + ------------------------------------------------------------------------------------- + TOTAL 502.36153685 2766.62416864 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 994696 +BPFP 0.9440 bits/point +EBPFP 0.9440 equivalent bits/point +MSE 2766.624169 +---------------------- -------------------------------------------------------- +Time: 5.203s Load: 0.051s, Pack+Encode: 2.642s, Decode+Unpack: 2.510s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2766.6242 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02787622-0.004599_marimba _ accordion_0.25991488.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n02787622-0.004599_marimba _ accordion_0.25991488.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02793495-0.000130_flatworm _ stingray_0.5528234.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02793495-0.000130_flatworm _ stingray_0.5528234.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 244,032B, BPFP=0.4632 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 256,680B, BPFP=0.4872 +⌛️ [2/4] FRONTEND: Frontend time: 2.585s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.493s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09706621 12.69288296 + layer.39.0 8.05872662 873.71616861 + ------------------------------------------------------------------------------------- + TOTAL 4.07789641 443.20452578 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 500712 +BPFP 0.4752 bits/point +EBPFP 0.4752 equivalent bits/point +MSE 443.204526 +---------------------- -------------------------------------------------------- +Time: 5.140s Load: 0.062s, Pack+Encode: 2.585s, Decode+Unpack: 2.493s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 443.2045 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02793495-0.000130_flatworm _ stingray_0.5528234.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n02793495-0.000130_flatworm _ stingray_0.5528234.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02797295-0.002775_hair dryer _ box turtle_0.2407761.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02797295-0.002775_hair dryer _ box turtle_0.2407761.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 352,208B, BPFP=0.6685 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 579,948B, BPFP=1.1008 +⌛️ [2/4] FRONTEND: Frontend time: 2.637s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.522s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11161610 13.40241872 + layer.39.0 373.09438776 3917.34693878 + ------------------------------------------------------------------------------------- + TOTAL 186.60300193 1965.37467875 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 932156 +BPFP 0.8847 bits/point +EBPFP 0.8847 equivalent bits/point +MSE 1965.374679 +---------------------- -------------------------------------------------------- +Time: 5.210s Load: 0.051s, Pack+Encode: 2.637s, Decode+Unpack: 2.522s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1965.3747 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02797295-0.002775_hair dryer _ box turtle_0.2407761.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n02797295-0.002775_hair dryer _ box turtle_0.2407761.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02814860-0.006340_fountain _ fountain_0.7891514.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02814860-0.006340_fountain _ fountain_0.7891514.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 173,256B, BPFP=0.3289 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 306,016B, BPFP=0.5808 +⌛️ [2/4] FRONTEND: Frontend time: 2.587s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.508s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.04615183 12.77629032 + layer.39.0 7.48662090 1406.59766764 + ------------------------------------------------------------------------------------- + TOTAL 7.76638637 709.68697898 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 479272 +BPFP 0.4548 bits/point +EBPFP 0.4548 equivalent bits/point +MSE 709.686979 +---------------------- -------------------------------------------------------- +Time: 5.156s Load: 0.061s, Pack+Encode: 2.587s, Decode+Unpack: 2.508s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 709.6870 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02814860-0.006340_fountain _ fountain_0.7891514.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n02814860-0.006340_fountain _ fountain_0.7891514.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02879718-0.003578_maraca _ maraca_0.6809677.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02879718-0.003578_maraca _ maraca_0.6809677.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 306,268B, BPFP=0.5813 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 547,880B, BPFP=1.0399 +⌛️ [2/4] FRONTEND: Frontend time: 2.644s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.528s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10989876 12.76952461 + layer.39.0 33.03751367 3227.14771623 + ------------------------------------------------------------------------------------- + TOTAL 16.57370621 1619.95862042 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 854148 +BPFP 0.8106 bits/point +EBPFP 0.8106 equivalent bits/point +MSE 1619.958620 +---------------------- -------------------------------------------------------- +Time: 5.233s Load: 0.061s, Pack+Encode: 2.644s, Decode+Unpack: 2.528s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1619.9586 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02879718-0.003578_maraca _ maraca_0.6809677.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n02879718-0.003578_maraca _ maraca_0.6809677.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02883205-0.000262_syringe _ syringe_0.7098205.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02883205-0.000262_syringe _ syringe_0.7098205.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 246,396B, BPFP=0.4677 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 376,716B, BPFP=0.7150 +⌛️ [2/4] FRONTEND: Frontend time: 2.632s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.500s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09610580 12.47690415 + layer.39.0 8.14318931 1519.64212828 + ------------------------------------------------------------------------------------- + TOTAL 4.11964755 766.05951622 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 623112 +BPFP 0.5914 bits/point +EBPFP 0.5914 equivalent bits/point +MSE 766.059516 +---------------------- -------------------------------------------------------- +Time: 5.193s Load: 0.061s, Pack+Encode: 2.632s, Decode+Unpack: 2.500s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 766.0595 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02883205-0.000262_syringe _ syringe_0.7098205.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n02883205-0.000262_syringe _ syringe_0.7098205.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02906734-0.000163_stethoscope _ stethoscope_0.9290994.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02906734-0.000163_stethoscope _ stethoscope_0.9290994.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 319,812B, BPFP=0.6070 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 499,676B, BPFP=0.9484 +⌛️ [2/4] FRONTEND: Frontend time: 2.619s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.534s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12024398 12.52429069 + layer.39.0 47.23105336 3697.07191448 + ------------------------------------------------------------------------------------- + TOTAL 23.67564867 1854.79810258 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 819488 +BPFP 0.7777 bits/point +EBPFP 0.7777 equivalent bits/point +MSE 1854.798103 +---------------------- -------------------------------------------------------- +Time: 5.205s Load: 0.052s, Pack+Encode: 2.619s, Decode+Unpack: 2.534s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1854.7981 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02906734-0.000163_stethoscope _ stethoscope_0.9290994.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n02906734-0.000163_stethoscope _ stethoscope_0.9290994.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02948072-0.002303_digital clock _ digital clock_0.928374.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02948072-0.002303_digital clock _ digital clock_0.928374.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 277,352B, BPFP=0.5264 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 487,224B, BPFP=0.9248 +⌛️ [2/4] FRONTEND: Frontend time: 2.630s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.521s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09670976 12.56877031 + layer.39.0 81.62974520 3549.60447036 + ------------------------------------------------------------------------------------- + TOTAL 40.86322748 1781.08662033 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 764576 +BPFP 0.7256 bits/point +EBPFP 0.7256 equivalent bits/point +MSE 1781.086620 +---------------------- -------------------------------------------------------- +Time: 5.212s Load: 0.061s, Pack+Encode: 2.630s, Decode+Unpack: 2.521s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1781.0866 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02948072-0.002303_digital clock _ digital clock_0.928374.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n02948072-0.002303_digital clock _ digital clock_0.928374.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02999410-0.000148_chest _ chest_0.9948565.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02999410-0.000148_chest _ chest_0.9948565.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.058s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 269,248B, BPFP=0.5111 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 331,968B, BPFP=0.6301 +⌛️ [2/4] FRONTEND: Frontend time: 2.624s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.505s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10256943 8.64077495 + layer.39.0 13.72598738 1637.99477648 + ------------------------------------------------------------------------------------- + TOTAL 6.91427841 823.31777572 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 601216 +BPFP 0.5706 bits/point +EBPFP 0.5706 equivalent bits/point +MSE 823.317776 +---------------------- -------------------------------------------------------- +Time: 5.187s Load: 0.058s, Pack+Encode: 2.624s, Decode+Unpack: 2.505s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 823.3178 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02999410-0.000148_chest _ chest_0.9948565.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n02999410-0.000148_chest _ chest_0.9948565.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03026506-0.001828_basketball _ basketball_0.6904969.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03026506-0.001828_basketball _ basketball_0.6904969.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 262,812B, BPFP=0.4988 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 440,532B, BPFP=0.8362 +⌛️ [2/4] FRONTEND: Frontend time: 2.624s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.513s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09484169 0.83222604 + layer.39.0 87.31533194 2150.32288630 + ------------------------------------------------------------------------------------- + TOTAL 43.70508681 1075.57755617 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 703344 +BPFP 0.6675 bits/point +EBPFP 0.6675 equivalent bits/point +MSE 1075.577556 +---------------------- -------------------------------------------------------- +Time: 5.188s Load: 0.052s, Pack+Encode: 2.624s, Decode+Unpack: 2.513s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1075.5776 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03026506-0.001828_basketball _ basketball_0.6904969.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n03026506-0.001828_basketball _ basketball_0.6904969.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03124043-0.001154_bow tie _ bow tie_0.9622156.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03124043-0.001154_bow tie _ bow tie_0.9622156.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.057s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 263,396B, BPFP=0.4999 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 421,332B, BPFP=0.7997 +⌛️ [2/4] FRONTEND: Frontend time: 2.630s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.504s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09893820 24.75810480 + layer.39.0 13.24554141 2184.72691934 + ------------------------------------------------------------------------------------- + TOTAL 6.67223981 1104.74251207 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 684728 +BPFP 0.6498 bits/point +EBPFP 0.6498 equivalent bits/point +MSE 1104.742512 +---------------------- -------------------------------------------------------- +Time: 5.192s Load: 0.057s, Pack+Encode: 2.630s, Decode+Unpack: 2.504s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1104.7425 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03124043-0.001154_bow tie _ bow tie_0.9622156.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n03124043-0.001154_bow tie _ bow tie_0.9622156.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03196217-0.015958_soap dispenser _ envelope_0.80274254.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03196217-0.015958_soap dispenser _ envelope_0.80274254.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 205,176B, BPFP=0.3894 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 385,104B, BPFP=0.7310 +⌛️ [2/4] FRONTEND: Frontend time: 2.630s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.519s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10340443 12.44922255 + layer.39.0 8.70910111 2155.45116618 + ------------------------------------------------------------------------------------- + TOTAL 4.40625277 1083.95019436 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 590280 +BPFP 0.5602 bits/point +EBPFP 0.5602 equivalent bits/point +MSE 1083.950194 +---------------------- -------------------------------------------------------- +Time: 5.201s Load: 0.052s, Pack+Encode: 2.630s, Decode+Unpack: 2.519s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1083.9502 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03196217-0.015958_soap dispenser _ envelope_0.80274254.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n03196217-0.015958_soap dispenser _ envelope_0.80274254.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03223299-0.001528_hot dog _ hot dog_0.9147216.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03223299-0.001528_hot dog _ hot dog_0.9147216.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 312,960B, BPFP=0.5940 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 401,576B, BPFP=0.7622 +⌛️ [2/4] FRONTEND: Frontend time: 2.614s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.530s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10130972 24.47101631 + layer.39.0 352.09596696 2527.88581147 + ------------------------------------------------------------------------------------- + TOTAL 176.09863834 1276.17841389 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 714536 +BPFP 0.6781 bits/point +EBPFP 0.6781 equivalent bits/point +MSE 1276.178414 +---------------------- -------------------------------------------------------- +Time: 5.196s Load: 0.051s, Pack+Encode: 2.614s, Decode+Unpack: 2.530s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1276.1784 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03223299-0.001528_hot dog _ hot dog_0.9147216.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n03223299-0.001528_hot dog _ hot dog_0.9147216.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03255030-0.005469_bubble _ bubble_0.9381716.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03255030-0.005469_bubble _ bubble_0.9381716.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 290,912B, BPFP=0.5522 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 550,728B, BPFP=1.0453 +⌛️ [2/4] FRONTEND: Frontend time: 2.612s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.519s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09675161 12.54536603 + layer.39.0 42.23478499 2691.80296404 + ------------------------------------------------------------------------------------- + TOTAL 21.16576830 1352.17416503 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 841640 +BPFP 0.7988 bits/point +EBPFP 0.7988 equivalent bits/point +MSE 1352.174165 +---------------------- -------------------------------------------------------- +Time: 5.193s Load: 0.062s, Pack+Encode: 2.612s, Decode+Unpack: 2.519s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1352.1742 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03255030-0.005469_bubble _ bubble_0.9381716.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n03255030-0.005469_bubble _ bubble_0.9381716.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03325584-0.000773_candle _ candle_0.810919.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03325584-0.000773_candle _ candle_0.810919.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 334,012B, BPFP=0.6340 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 648,096B, BPFP=1.2301 +⌛️ [2/4] FRONTEND: Frontend time: 2.615s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.525s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10394677 50.00267250 + layer.39.0 140.58187561 3696.73663751 + ------------------------------------------------------------------------------------- + TOTAL 70.34291119 1873.36965500 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 982108 +BPFP 0.9321 bits/point +EBPFP 0.9321 equivalent bits/point +MSE 1873.369655 +---------------------- -------------------------------------------------------- +Time: 5.192s Load: 0.052s, Pack+Encode: 2.615s, Decode+Unpack: 2.525s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1873.3697 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03325584-0.000773_candle _ candle_0.810919.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n03325584-0.000773_candle _ candle_0.810919.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03355925-0.004997_spider web _ spider web_0.9142101.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03355925-0.004997_spider web _ spider web_0.9142101.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 169,320B, BPFP=0.3214 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 246,296B, BPFP=0.4675 +⌛️ [2/4] FRONTEND: Frontend time: 2.603s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.494s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09873271 12.85980966 + layer.39.0 6.60211199 1027.51627794 + ------------------------------------------------------------------------------------- + TOTAL 3.35042235 520.18804380 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 415616 +BPFP 0.3944 bits/point +EBPFP 0.3944 equivalent bits/point +MSE 520.188044 +---------------------- -------------------------------------------------------- +Time: 5.149s Load: 0.052s, Pack+Encode: 2.603s, Decode+Unpack: 2.494s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 520.1880 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03355925-0.004997_spider web _ spider web_0.9142101.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n03355925-0.004997_spider web _ spider web_0.9142101.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03384352-0.002273_viaduct _ viaduct_0.6161024.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03384352-0.002273_viaduct _ viaduct_0.6161024.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 289,916B, BPFP=0.5503 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 413,936B, BPFP=0.7857 +⌛️ [2/4] FRONTEND: Frontend time: 2.625s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.517s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09647940 0.86515756 + layer.39.0 175.50411504 2258.20796890 + ------------------------------------------------------------------------------------- + TOTAL 87.80029722 1129.53656323 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 703852 +BPFP 0.6680 bits/point +EBPFP 0.6680 equivalent bits/point +MSE 1129.536563 +---------------------- -------------------------------------------------------- +Time: 5.194s Load: 0.052s, Pack+Encode: 2.625s, Decode+Unpack: 2.517s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1129.5366 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03384352-0.002273_viaduct _ viaduct_0.6161024.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n03384352-0.002273_viaduct _ viaduct_0.6161024.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03388043-0.005154_candle _ candle_0.9636924.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03388043-0.005154_candle _ candle_0.9636924.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 274,716B, BPFP=0.5214 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 387,356B, BPFP=0.7352 +⌛️ [2/4] FRONTEND: Frontend time: 2.600s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.500s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09640297 12.26286045 + layer.39.0 7.87377147 2541.00315841 + ------------------------------------------------------------------------------------- + TOTAL 3.98508722 1276.63300943 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 662072 +BPFP 0.6283 bits/point +EBPFP 0.6283 equivalent bits/point +MSE 1276.633009 +---------------------- -------------------------------------------------------- +Time: 5.162s Load: 0.062s, Pack+Encode: 2.600s, Decode+Unpack: 2.500s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1276.6330 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03388043-0.005154_candle _ candle_0.9636924.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n03388043-0.005154_candle _ candle_0.9636924.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03417042-0.001187_tank _ tank_0.70379025.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03417042-0.001187_tank _ tank_0.70379025.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 249,640B, BPFP=0.4738 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 437,116B, BPFP=0.8297 +⌛️ [2/4] FRONTEND: Frontend time: 2.631s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.501s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09848782 13.87916249 + layer.39.0 16.63742104 2796.66180758 + ------------------------------------------------------------------------------------- + TOTAL 8.36795443 1405.27048504 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 686756 +BPFP 0.6518 bits/point +EBPFP 0.6518 equivalent bits/point +MSE 1405.270485 +---------------------- -------------------------------------------------------- +Time: 5.194s Load: 0.062s, Pack+Encode: 2.631s, Decode+Unpack: 2.501s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1405.2705 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03417042-0.001187_tank _ tank_0.70379025.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n03417042-0.001187_tank _ tank_0.70379025.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03444034-0.002100_maraca _ maraca_0.502369.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03444034-0.002100_maraca _ maraca_0.502369.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 350,652B, BPFP=0.6656 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 663,052B, BPFP=1.2585 +⌛️ [2/4] FRONTEND: Frontend time: 2.634s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.531s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11197850 49.19844813 + layer.39.0 347.54634354 4269.60009718 + ------------------------------------------------------------------------------------- + TOTAL 173.82916102 2159.39927266 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1013704 +BPFP 0.9620 bits/point +EBPFP 0.9620 equivalent bits/point +MSE 2159.399273 +---------------------- -------------------------------------------------------- +Time: 5.226s Load: 0.061s, Pack+Encode: 2.634s, Decode+Unpack: 2.531s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2159.3993 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03444034-0.002100_maraca _ maraca_0.502369.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n03444034-0.002100_maraca _ maraca_0.502369.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03445924-0.003505_baseball player _ baseball player_0.6723684.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03445924-0.003505_baseball player _ baseball player_0.6723684.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 262,008B, BPFP=0.4973 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 470,164B, BPFP=0.8924 +⌛️ [2/4] FRONTEND: Frontend time: 2.629s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.510s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09665277 0.84614882 + layer.39.0 26.28463618 2705.66034985 + ------------------------------------------------------------------------------------- + TOTAL 13.19064447 1353.25324934 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 732172 +BPFP 0.6949 bits/point +EBPFP 0.6949 equivalent bits/point +MSE 1353.253249 +---------------------- -------------------------------------------------------- +Time: 5.201s Load: 0.062s, Pack+Encode: 2.629s, Decode+Unpack: 2.510s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1353.2532 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03445924-0.003505_baseball player _ baseball player_0.6723684.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n03445924-0.003505_baseball player _ baseball player_0.6723684.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03452741-0.002771_chain _ chain_0.9575044.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03452741-0.002771_chain _ chain_0.9575044.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 326,820B, BPFP=0.6203 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 570,888B, BPFP=1.0836 +⌛️ [2/4] FRONTEND: Frontend time: 2.634s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.527s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12351380 12.61283900 + layer.39.0 42.82565370 3424.01409135 + ------------------------------------------------------------------------------------- + TOTAL 21.47458375 1718.31346517 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 897708 +BPFP 0.8520 bits/point +EBPFP 0.8520 equivalent bits/point +MSE 1718.313465 +---------------------- -------------------------------------------------------- +Time: 5.222s Load: 0.061s, Pack+Encode: 2.634s, Decode+Unpack: 2.527s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1718.3135 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03452741-0.002771_chain _ chain_0.9575044.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n03452741-0.002771_chain _ chain_0.9575044.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03483316-0.004974_lighter _ lighter_0.27796906.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03483316-0.004974_lighter _ lighter_0.27796906.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 372,684B, BPFP=0.7074 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 548,376B, BPFP=1.0409 +⌛️ [2/4] FRONTEND: Frontend time: 2.651s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.526s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12993333 71.79250182 + layer.39.0 87.07173986 2795.23542274 + ------------------------------------------------------------------------------------- + TOTAL 43.60083660 1433.51396228 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 921060 +BPFP 0.8741 bits/point +EBPFP 0.8741 equivalent bits/point +MSE 1433.513962 +---------------------- -------------------------------------------------------- +Time: 5.229s Load: 0.052s, Pack+Encode: 2.651s, Decode+Unpack: 2.526s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1433.5140 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03483316-0.004974_lighter _ lighter_0.27796906.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n03483316-0.004974_lighter _ lighter_0.27796906.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03584829-0.104805_golf cart _ golf cart_0.73568964.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03584829-0.104805_golf cart _ golf cart_0.73568964.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 299,532B, BPFP=0.5685 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 524,028B, BPFP=0.9946 +⌛️ [2/4] FRONTEND: Frontend time: 2.635s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.533s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09917131 12.52128792 + layer.39.0 24.34873246 3747.78547133 + ------------------------------------------------------------------------------------- + TOTAL 12.22395189 1880.15337963 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 823560 +BPFP 0.7816 bits/point +EBPFP 0.7816 equivalent bits/point +MSE 1880.153380 +---------------------- -------------------------------------------------------- +Time: 5.229s Load: 0.061s, Pack+Encode: 2.635s, Decode+Unpack: 2.533s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1880.1534 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03584829-0.104805_golf cart _ golf cart_0.73568964.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n03584829-0.104805_golf cart _ golf cart_0.73568964.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03590841-0.001115_rocking chair _ rocking chair_0.2192492.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03590841-0.001115_rocking chair _ rocking chair_0.2192492.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 312,320B, BPFP=0.5928 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 369,568B, BPFP=0.7015 +⌛️ [2/4] FRONTEND: Frontend time: 2.632s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.537s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11329899 60.82927903 + layer.39.0 19.97532495 1835.98566569 + ------------------------------------------------------------------------------------- + TOTAL 10.04431197 948.40747236 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 681888 +BPFP 0.6471 bits/point +EBPFP 0.6471 equivalent bits/point +MSE 948.407472 +---------------------- -------------------------------------------------------- +Time: 5.221s Load: 0.052s, Pack+Encode: 2.632s, Decode+Unpack: 2.537s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 948.4075 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03590841-0.001115_rocking chair _ rocking chair_0.2192492.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n03590841-0.001115_rocking chair _ rocking chair_0.2192492.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03617480-0.003238_basketball _ basketball_0.67568874.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03617480-0.003238_basketball _ basketball_0.67568874.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 382,024B, BPFP=0.7251 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 640,272B, BPFP=1.2153 +⌛️ [2/4] FRONTEND: Frontend time: 2.648s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.543s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12967051 39.30955038 + layer.39.0 57.10576865 3996.81899903 + ------------------------------------------------------------------------------------- + TOTAL 28.61771958 2018.06427471 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1022296 +BPFP 0.9702 bits/point +EBPFP 0.9702 equivalent bits/point +MSE 2018.064275 +---------------------- -------------------------------------------------------- +Time: 5.252s Load: 0.061s, Pack+Encode: 2.648s, Decode+Unpack: 2.543s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2018.0643 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03617480-0.003238_basketball _ basketball_0.67568874.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n03617480-0.003238_basketball _ basketball_0.67568874.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03666591-0.004622_torch _ torch_0.99906796.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03666591-0.004622_torch _ torch_0.99906796.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 210,836B, BPFP=0.4002 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 320,300B, BPFP=0.6080 +⌛️ [2/4] FRONTEND: Frontend time: 2.611s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.506s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.05477861 12.74522728 + layer.39.0 7.78975672 1356.45383868 + ------------------------------------------------------------------------------------- + TOTAL 7.92226767 684.59953298 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 531136 +BPFP 0.5041 bits/point +EBPFP 0.5041 equivalent bits/point +MSE 684.599533 +---------------------- -------------------------------------------------------- +Time: 5.169s Load: 0.052s, Pack+Encode: 2.611s, Decode+Unpack: 2.506s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 684.5995 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03666591-0.004622_torch _ torch_0.99906796.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n03666591-0.004622_torch _ torch_0.99906796.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03670208-0.003899_hair dryer _ grand piano_0.7359066.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03670208-0.003899_hair dryer _ grand piano_0.7359066.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 299,940B, BPFP=0.5693 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 573,512B, BPFP=1.0886 +⌛️ [2/4] FRONTEND: Frontend time: 2.639s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.542s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11232473 12.76091112 + layer.39.0 36.60432231 3043.30272109 + ------------------------------------------------------------------------------------- + TOTAL 18.35832352 1528.03181610 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 873452 +BPFP 0.8289 bits/point +EBPFP 0.8289 equivalent bits/point +MSE 1528.031816 +---------------------- -------------------------------------------------------- +Time: 5.242s Load: 0.062s, Pack+Encode: 2.639s, Decode+Unpack: 2.542s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1528.0318 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03670208-0.003899_hair dryer _ grand piano_0.7359066.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n03670208-0.003899_hair dryer _ grand piano_0.7359066.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03717622-0.001175_sundial _ sundial_0.9998197.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03717622-0.001175_sundial _ sundial_0.9998197.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 360,460B, BPFP=0.6842 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 640,960B, BPFP=1.2166 +⌛️ [2/4] FRONTEND: Frontend time: 2.672s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.532s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13381931 50.06234056 + layer.39.0 773.52204810 5240.05685131 + ------------------------------------------------------------------------------------- + TOTAL 386.82793371 2645.05959594 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1001420 +BPFP 0.9504 bits/point +EBPFP 0.9504 equivalent bits/point +MSE 2645.059596 +---------------------- -------------------------------------------------------- +Time: 5.265s Load: 0.061s, Pack+Encode: 2.672s, Decode+Unpack: 2.532s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2645.0596 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03717622-0.001175_sundial _ sundial_0.9998197.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n03717622-0.001175_sundial _ sundial_0.9998197.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03720891-0.001854_African bush elephant _ African bush elephant_0.722115.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03720891-0.001854_African bush elephant _ African bush elephant_0.722115.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 294,640B, BPFP=0.5593 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 675,504B, BPFP=1.2822 +⌛️ [2/4] FRONTEND: Frontend time: 2.621s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.526s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09642763 0.87280991 + layer.39.0 155.23232507 3854.42784257 + ------------------------------------------------------------------------------------- + TOTAL 77.66437635 1927.65032624 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 970144 +BPFP 0.9207 bits/point +EBPFP 0.9207 equivalent bits/point +MSE 1927.650326 +---------------------- -------------------------------------------------------- +Time: 5.199s Load: 0.052s, Pack+Encode: 2.621s, Decode+Unpack: 2.526s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1927.6503 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03720891-0.001854_African bush elephant _ African bush elephant_0.722115.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n03720891-0.001854_African bush elephant _ African bush elephant_0.722115.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03721384-0.003327_chain _ chain_0.5599652.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03721384-0.003327_chain _ chain_0.5599652.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 230,208B, BPFP=0.4370 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 494,996B, BPFP=0.9395 +⌛️ [2/4] FRONTEND: Frontend time: 2.608s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.534s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09561452 24.68181715 + layer.39.0 742.66502672 3290.59863946 + ------------------------------------------------------------------------------------- + TOTAL 371.38032062 1657.64022830 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 725204 +BPFP 0.6882 bits/point +EBPFP 0.6882 equivalent bits/point +MSE 1657.640228 +---------------------- -------------------------------------------------------- +Time: 5.194s Load: 0.052s, Pack+Encode: 2.608s, Decode+Unpack: 2.534s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1657.6402 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03721384-0.003327_chain _ chain_0.5599652.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n03721384-0.003327_chain _ chain_0.5599652.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03724870-0.001366_Christmas stocking _ Christmas stocking_0.5709616.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03724870-0.001366_Christmas stocking _ Christmas stocking_0.5709616.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 315,732B, BPFP=0.5993 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 504,624B, BPFP=0.9578 +⌛️ [2/4] FRONTEND: Frontend time: 2.621s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.512s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10329660 1.27479166 + layer.39.0 513.92243683 3398.80563654 + ------------------------------------------------------------------------------------- + TOTAL 257.01286671 1700.04021410 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 820356 +BPFP 0.7786 bits/point +EBPFP 0.7786 equivalent bits/point +MSE 1700.040214 +---------------------- -------------------------------------------------------- +Time: 5.194s Load: 0.061s, Pack+Encode: 2.621s, Decode+Unpack: 2.512s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1700.0402 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03724870-0.001366_Christmas stocking _ Christmas stocking_0.5709616.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n03724870-0.001366_Christmas stocking _ Christmas stocking_0.5709616.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03775071-0.000145_Christmas stocking _ Christmas stocking_0.9986047.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03775071-0.000145_Christmas stocking _ Christmas stocking_0.9986047.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.058s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 300,872B, BPFP=0.5711 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 509,984B, BPFP=0.9680 +⌛️ [2/4] FRONTEND: Frontend time: 2.635s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.545s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09700392 12.50874446 + layer.39.0 284.92189018 3547.31754130 + ------------------------------------------------------------------------------------- + TOTAL 142.50944705 1779.91314288 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 810856 +BPFP 0.7695 bits/point +EBPFP 0.7695 equivalent bits/point +MSE 1779.913143 +---------------------- -------------------------------------------------------- +Time: 5.238s Load: 0.058s, Pack+Encode: 2.635s, Decode+Unpack: 2.545s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1779.9131 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03775071-0.000145_Christmas stocking _ Christmas stocking_0.9986047.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n03775071-0.000145_Christmas stocking _ Christmas stocking_0.9986047.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03788195-0.005975_steam locomotive _ steam locomotive_0.42419377.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03788195-0.005975_steam locomotive _ steam locomotive_0.42419377.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 356,640B, BPFP=0.6769 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 401,720B, BPFP=0.7625 +⌛️ [2/4] FRONTEND: Frontend time: 2.614s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.519s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10790903 13.74309858 + layer.39.0 10.34781284 2162.51457726 + ------------------------------------------------------------------------------------- + TOTAL 5.22786094 1088.12883792 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 758360 +BPFP 0.7197 bits/point +EBPFP 0.7197 equivalent bits/point +MSE 1088.128838 +---------------------- -------------------------------------------------------- +Time: 5.186s Load: 0.052s, Pack+Encode: 2.614s, Decode+Unpack: 2.519s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1088.1288 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03788195-0.005975_steam locomotive _ steam locomotive_0.42419377.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n03788195-0.005975_steam locomotive _ steam locomotive_0.42419377.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03837869-0.002023_lighthouse _ lighthouse_0.5898798.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03837869-0.002023_lighthouse _ lighthouse_0.5898798.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 277,256B, BPFP=0.5263 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 324,292B, BPFP=0.6155 +⌛️ [2/4] FRONTEND: Frontend time: 2.618s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.522s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12703056 12.91893772 + layer.39.0 141.21340500 1316.36345967 + ------------------------------------------------------------------------------------- + TOTAL 70.67021778 664.64119869 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 601548 +BPFP 0.5709 bits/point +EBPFP 0.5709 equivalent bits/point +MSE 664.641199 +---------------------- -------------------------------------------------------- +Time: 5.193s Load: 0.052s, Pack+Encode: 2.618s, Decode+Unpack: 2.522s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 664.6412 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03837869-0.002023_lighthouse _ lighthouse_0.5898798.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n03837869-0.002023_lighthouse _ lighthouse_0.5898798.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03840681-0.002188_ladybug _ ladybug_0.9972365.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03840681-0.002188_ladybug _ ladybug_0.9972365.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 215,196B, BPFP=0.4085 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 283,540B, BPFP=0.5382 +⌛️ [2/4] FRONTEND: Frontend time: 2.596s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.532s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09487485 12.58882448 + layer.39.0 29.40353574 1437.44533528 + ------------------------------------------------------------------------------------- + TOTAL 14.74920530 725.01707988 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 498736 +BPFP 0.4733 bits/point +EBPFP 0.4733 equivalent bits/point +MSE 725.017080 +---------------------- -------------------------------------------------------- +Time: 5.179s Load: 0.051s, Pack+Encode: 2.596s, Decode+Unpack: 2.532s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 725.0171 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03840681-0.002188_ladybug _ ladybug_0.9972365.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n03840681-0.002188_ladybug _ ladybug_0.9972365.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03888257-0.004083_rugby ball _ snail_0.41588444.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03888257-0.004083_rugby ball _ snail_0.41588444.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 250,792B, BPFP=0.4760 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 294,456B, BPFP=0.5589 +⌛️ [2/4] FRONTEND: Frontend time: 2.610s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.528s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10005040 8.75348488 + layer.39.0 7.47115060 1135.17784257 + ------------------------------------------------------------------------------------- + TOTAL 3.78560050 571.96566372 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 545248 +BPFP 0.5175 bits/point +EBPFP 0.5175 equivalent bits/point +MSE 571.965664 +---------------------- -------------------------------------------------------- +Time: 5.199s Load: 0.061s, Pack+Encode: 2.610s, Decode+Unpack: 2.528s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 571.9657 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03888257-0.004083_rugby ball _ snail_0.41588444.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n03888257-0.004083_rugby ball _ snail_0.41588444.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03891332-0.003727_syringe _ syringe_0.93799996.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03891332-0.003727_syringe _ syringe_0.93799996.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 264,784B, BPFP=0.5026 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 556,004B, BPFP=1.0553 +⌛️ [2/4] FRONTEND: Frontend time: 2.630s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.516s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09617506 8.60687713 + layer.39.0 18.45312310 3921.93634597 + ------------------------------------------------------------------------------------- + TOTAL 9.27464908 1965.27161155 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 820788 +BPFP 0.7790 bits/point +EBPFP 0.7790 equivalent bits/point +MSE 1965.271612 +---------------------- -------------------------------------------------------- +Time: 5.199s Load: 0.052s, Pack+Encode: 2.630s, Decode+Unpack: 2.516s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1965.2716 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03891332-0.003727_syringe _ syringe_0.93799996.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n03891332-0.003727_syringe _ syringe_0.93799996.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03982430-0.005102_couch _ couch_0.9976859.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03982430-0.005102_couch _ couch_0.9976859.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 185,256B, BPFP=0.3516 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 413,812B, BPFP=0.7854 +⌛️ [2/4] FRONTEND: Frontend time: 2.620s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.516s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09691652 8.64084802 + layer.39.0 169.89398081 3101.00728863 + ------------------------------------------------------------------------------------- + TOTAL 84.99544866 1554.82406833 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 599068 +BPFP 0.5685 bits/point +EBPFP 0.5685 equivalent bits/point +MSE 1554.824068 +---------------------- -------------------------------------------------------- +Time: 5.188s Load: 0.052s, Pack+Encode: 2.620s, Decode+Unpack: 2.516s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1554.8241 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03982430-0.005102_couch _ couch_0.9976859.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n03982430-0.005102_couch _ couch_0.9976859.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04033901-0.007476_envelope _ envelope_0.9990971.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04033901-0.007476_envelope _ envelope_0.9990971.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 227,612B, BPFP=0.4320 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 340,052B, BPFP=0.6454 +⌛️ [2/4] FRONTEND: Frontend time: 2.623s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.519s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10364226 8.73371257 + layer.39.0 7.34252906 1930.71890185 + ------------------------------------------------------------------------------------- + TOTAL 3.72308566 969.72630721 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 567664 +BPFP 0.5387 bits/point +EBPFP 0.5387 equivalent bits/point +MSE 969.726307 +---------------------- -------------------------------------------------------- +Time: 5.204s Load: 0.062s, Pack+Encode: 2.623s, Decode+Unpack: 2.519s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 969.7263 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04033901-0.007476_envelope _ envelope_0.9990971.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n04033901-0.007476_envelope _ envelope_0.9990971.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04039381-0.000054_hair dryer _ hair dryer_0.5667548.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04039381-0.000054_hair dryer _ hair dryer_0.5667548.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 245,716B, BPFP=0.4664 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 456,860B, BPFP=0.8672 +⌛️ [2/4] FRONTEND: Frontend time: 2.630s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.521s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09588603 0.85931675 + layer.39.0 26.21653304 2180.39115646 + ------------------------------------------------------------------------------------- + TOTAL 13.15620954 1090.62523661 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 702576 +BPFP 0.6668 bits/point +EBPFP 0.6668 equivalent bits/point +MSE 1090.625237 +---------------------- -------------------------------------------------------- +Time: 5.212s Load: 0.061s, Pack+Encode: 2.630s, Decode+Unpack: 2.521s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1090.6252 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04039381-0.000054_hair dryer _ hair dryer_0.5667548.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n04039381-0.000054_hair dryer _ hair dryer_0.5667548.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04118538-0.005804_cowboy boot _ cowboy boot_0.21208441.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04118538-0.005804_cowboy boot _ cowboy boot_0.21208441.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 300,676B, BPFP=0.5707 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 377,704B, BPFP=0.7169 +⌛️ [2/4] FRONTEND: Frontend time: 2.627s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.515s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09664223 12.76300660 + layer.39.0 8.64007266 2236.35058309 + ------------------------------------------------------------------------------------- + TOTAL 4.36835744 1124.55679484 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 678380 +BPFP 0.6438 bits/point +EBPFP 0.6438 equivalent bits/point +MSE 1124.556795 +---------------------- -------------------------------------------------------- +Time: 5.203s Load: 0.061s, Pack+Encode: 2.627s, Decode+Unpack: 2.515s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1124.5568 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04118538-0.005804_cowboy boot _ cowboy boot_0.21208441.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n04118538-0.005804_cowboy boot _ cowboy boot_0.21208441.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04146614-0.008793_marimba _ marimba_0.54555196.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04146614-0.008793_marimba _ marimba_0.54555196.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 300,564B, BPFP=0.5705 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 677,128B, BPFP=1.2852 +⌛️ [2/4] FRONTEND: Frontend time: 2.637s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.528s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09774729 25.23305014 + layer.39.0 155.07908163 5979.59280855 + ------------------------------------------------------------------------------------- + TOTAL 77.58841446 3002.41292935 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 977692 +BPFP 0.9279 bits/point +EBPFP 0.9279 equivalent bits/point +MSE 3002.412929 +---------------------- -------------------------------------------------------- +Time: 5.226s Load: 0.061s, Pack+Encode: 2.637s, Decode+Unpack: 2.528s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3002.4129 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04146614-0.008793_marimba _ marimba_0.54555196.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n04146614-0.008793_marimba _ marimba_0.54555196.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04179913-0.006024_guacamole _ custard apple_0.5550306.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04179913-0.006024_guacamole _ custard apple_0.5550306.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 330,640B, BPFP=0.6276 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 494,832B, BPFP=0.9392 +⌛️ [2/4] FRONTEND: Frontend time: 2.635s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.558s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11409367 1.32611137 + layer.39.0 68.43204871 2967.50971817 + ------------------------------------------------------------------------------------- + TOTAL 34.27307119 1484.41791477 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 825472 +BPFP 0.7834 bits/point +EBPFP 0.7834 equivalent bits/point +MSE 1484.417915 +---------------------- -------------------------------------------------------- +Time: 5.245s Load: 0.052s, Pack+Encode: 2.635s, Decode+Unpack: 2.558s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1484.4179 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04179913-0.006024_guacamole _ custard apple_0.5550306.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n04179913-0.006024_guacamole _ custard apple_0.5550306.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04208210-0.007213_doormat _ doormat_0.81736016.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04208210-0.007213_doormat _ doormat_0.81736016.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 359,760B, BPFP=0.6829 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 441,752B, BPFP=0.8385 +⌛️ [2/4] FRONTEND: Frontend time: 2.648s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.532s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10601767 183.05080782 + layer.39.0 349.44518343 4327.17735666 + ------------------------------------------------------------------------------------- + TOTAL 174.77560055 2255.11408224 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 801512 +BPFP 0.7607 bits/point +EBPFP 0.7607 equivalent bits/point +MSE 2255.114082 +---------------------- -------------------------------------------------------- +Time: 5.242s Load: 0.062s, Pack+Encode: 2.648s, Decode+Unpack: 2.532s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2255.1141 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04208210-0.007213_doormat _ doormat_0.81736016.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n04208210-0.007213_doormat _ doormat_0.81736016.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04270147-0.000435_rotary dial telephone _ rotary dial telephone_0.9268941.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04270147-0.000435_rotary dial telephone _ rotary dial telephone_0.9268941.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 242,912B, BPFP=0.4611 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 472,684B, BPFP=0.8972 +⌛️ [2/4] FRONTEND: Frontend time: 2.621s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.514s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09464848 8.54070339 + layer.39.0 229.78908528 2777.58965015 + ------------------------------------------------------------------------------------- + TOTAL 114.94186688 1393.06517677 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 715596 +BPFP 0.6791 bits/point +EBPFP 0.6791 equivalent bits/point +MSE 1393.065177 +---------------------- -------------------------------------------------------- +Time: 5.197s Load: 0.062s, Pack+Encode: 2.621s, Decode+Unpack: 2.514s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1393.0652 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04270147-0.000435_rotary dial telephone _ rotary dial telephone_0.9268941.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n04270147-0.000435_rotary dial telephone _ rotary dial telephone_0.9268941.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04317175-0.006894_bow tie _ bow tie_0.95877784.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04317175-0.006894_bow tie _ bow tie_0.95877784.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 272,788B, BPFP=0.5178 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 512,912B, BPFP=0.9735 +⌛️ [2/4] FRONTEND: Frontend time: 2.621s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.514s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09706025 12.66814148 + layer.39.0 10.87108806 2688.34936832 + ------------------------------------------------------------------------------------- + TOTAL 5.48407415 1350.50875490 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 785700 +BPFP 0.7457 bits/point +EBPFP 0.7457 equivalent bits/point +MSE 1350.508755 +---------------------- -------------------------------------------------------- +Time: 5.187s Load: 0.051s, Pack+Encode: 2.621s, Decode+Unpack: 2.514s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1350.5088 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04317175-0.006894_bow tie _ bow tie_0.95877784.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n04317175-0.006894_bow tie _ bow tie_0.95877784.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04347754-0.001405_viaduct _ viaduct_0.8445672.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04347754-0.001405_viaduct _ viaduct_0.8445672.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 193,724B, BPFP=0.3677 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 463,712B, BPFP=0.8802 +⌛️ [2/4] FRONTEND: Frontend time: 2.636s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.516s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09586499 0.83551553 + layer.39.0 267.55718537 3256.42371234 + ------------------------------------------------------------------------------------- + TOTAL 133.82652518 1628.62961394 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 657436 +BPFP 0.6239 bits/point +EBPFP 0.6239 equivalent bits/point +MSE 1628.629614 +---------------------- -------------------------------------------------------- +Time: 5.211s Load: 0.059s, Pack+Encode: 2.636s, Decode+Unpack: 2.516s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1628.6296 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04347754-0.001405_viaduct _ viaduct_0.8445672.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n04347754-0.001405_viaduct _ viaduct_0.8445672.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04355338-0.000791_envelope _ envelope_0.9969598.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04355338-0.000791_envelope _ envelope_0.9969598.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 275,228B, BPFP=0.5224 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 483,780B, BPFP=0.9183 +⌛️ [2/4] FRONTEND: Frontend time: 2.625s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.532s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10273007 12.87644634 + layer.39.0 331.89978134 3796.13921283 + ------------------------------------------------------------------------------------- + TOTAL 166.00125571 1904.50782958 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 759008 +BPFP 0.7203 bits/point +EBPFP 0.7203 equivalent bits/point +MSE 1904.507830 +---------------------- -------------------------------------------------------- +Time: 5.219s Load: 0.062s, Pack+Encode: 2.625s, Decode+Unpack: 2.532s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1904.5078 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04355338-0.000791_envelope _ envelope_0.9969598.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n04355338-0.000791_envelope _ envelope_0.9969598.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04366367-0.002021_parachute _ parachute_0.9226023.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04366367-0.002021_parachute _ parachute_0.9226023.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 214,296B, BPFP=0.4068 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 281,784B, BPFP=0.5348 +⌛️ [2/4] FRONTEND: Frontend time: 2.608s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.509s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09577132 0.85608936 + layer.39.0 47.60657343 2337.08527697 + ------------------------------------------------------------------------------------- + TOTAL 23.85117238 1168.97068317 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 496080 +BPFP 0.4708 bits/point +EBPFP 0.4708 equivalent bits/point +MSE 1168.970683 +---------------------- -------------------------------------------------------- +Time: 5.178s Load: 0.061s, Pack+Encode: 2.608s, Decode+Unpack: 2.509s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1168.9707 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04366367-0.002021_parachute _ parachute_0.9226023.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n04366367-0.002021_parachute _ parachute_0.9226023.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04376876-0.004615_lighter _ lighter_0.69176143.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04376876-0.004615_lighter _ lighter_0.69176143.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 266,788B, BPFP=0.5064 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 441,592B, BPFP=0.8382 +⌛️ [2/4] FRONTEND: Frontend time: 2.627s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.522s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09912059 12.60411732 + layer.39.0 173.01079628 2565.09718173 + ------------------------------------------------------------------------------------- + TOTAL 86.55495844 1288.85064952 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 708380 +BPFP 0.6723 bits/point +EBPFP 0.6723 equivalent bits/point +MSE 1288.850650 +---------------------- -------------------------------------------------------- +Time: 5.211s Load: 0.061s, Pack+Encode: 2.627s, Decode+Unpack: 2.522s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1288.8506 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04376876-0.004615_lighter _ lighter_0.69176143.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n04376876-0.004615_lighter _ lighter_0.69176143.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04442312-0.001690_washing machine _ washing machine_0.8494714.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04442312-0.001690_washing machine _ washing machine_0.8494714.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 193,252B, BPFP=0.3668 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 368,996B, BPFP=0.7004 +⌛️ [2/4] FRONTEND: Frontend time: 2.602s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.508s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.08302300 12.74714244 + layer.39.0 28.24609944 1777.41703110 + ------------------------------------------------------------------------------------- + TOTAL 18.16456122 895.08208677 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 562248 +BPFP 0.5336 bits/point +EBPFP 0.5336 equivalent bits/point +MSE 895.082087 +---------------------- -------------------------------------------------------- +Time: 5.171s Load: 0.061s, Pack+Encode: 2.602s, Decode+Unpack: 2.508s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 895.0821 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04442312-0.001690_washing machine _ washing machine_0.8494714.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n04442312-0.001690_washing machine _ washing machine_0.8494714.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04456115-0.002573_hair dryer _ envelope_0.34823084.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04456115-0.002573_hair dryer _ envelope_0.34823084.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 217,280B, BPFP=0.4124 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 415,676B, BPFP=0.7890 +⌛️ [2/4] FRONTEND: Frontend time: 2.591s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.499s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09444211 12.12367134 + layer.39.0 8.80792942 2252.09037901 + ------------------------------------------------------------------------------------- + TOTAL 4.45118577 1132.10702518 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 632956 +BPFP 0.6007 bits/point +EBPFP 0.6007 equivalent bits/point +MSE 1132.107025 +---------------------- -------------------------------------------------------- +Time: 5.141s Load: 0.051s, Pack+Encode: 2.591s, Decode+Unpack: 2.499s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1132.1070 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04456115-0.002573_hair dryer _ envelope_0.34823084.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n04456115-0.002573_hair dryer _ envelope_0.34823084.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04509417-0.000350_sundial _ sundial_0.99936897.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04509417-0.000350_sundial _ sundial_0.99936897.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 272,508B, BPFP=0.5172 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 387,920B, BPFP=0.7363 +⌛️ [2/4] FRONTEND: Frontend time: 2.612s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.517s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10319057 9.05452901 + layer.39.0 8.14296913 2092.01020408 + ------------------------------------------------------------------------------------- + TOTAL 4.12307985 1050.53236655 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 660428 +BPFP 0.6268 bits/point +EBPFP 0.6268 equivalent bits/point +MSE 1050.532367 +---------------------- -------------------------------------------------------- +Time: 5.190s Load: 0.061s, Pack+Encode: 2.612s, Decode+Unpack: 2.517s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1050.5324 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04509417-0.000350_sundial _ sundial_0.99936897.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n04509417-0.000350_sundial _ sundial_0.99936897.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04532670-0.000670_spider web _ spider web_0.70711935.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04532670-0.000670_spider web _ spider web_0.70711935.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 325,680B, BPFP=0.6182 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 431,344B, BPFP=0.8187 +⌛️ [2/4] FRONTEND: Frontend time: 2.638s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.511s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09618602 25.14369609 + layer.39.0 175.41615039 2061.70505345 + ------------------------------------------------------------------------------------- + TOTAL 87.75616821 1043.42437477 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 757024 +BPFP 0.7184 bits/point +EBPFP 0.7184 equivalent bits/point +MSE 1043.424375 +---------------------- -------------------------------------------------------- +Time: 5.201s Load: 0.052s, Pack+Encode: 2.638s, Decode+Unpack: 2.511s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1043.4244 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04532670-0.000670_spider web _ spider web_0.70711935.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n04532670-0.000670_spider web _ spider web_0.70711935.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04540053-0.000734_balance beam _ balance beam_0.99765223.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04540053-0.000734_balance beam _ balance beam_0.99765223.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 213,680B, BPFP=0.4056 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 338,136B, BPFP=0.6418 +⌛️ [2/4] FRONTEND: Frontend time: 2.611s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.514s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09941827 12.64079962 + layer.39.0 8.11341412 1459.00425170 + ------------------------------------------------------------------------------------- + TOTAL 4.10641619 735.82252566 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 551816 +BPFP 0.5237 bits/point +EBPFP 0.5237 equivalent bits/point +MSE 735.822526 +---------------------- -------------------------------------------------------- +Time: 5.185s Load: 0.061s, Pack+Encode: 2.611s, Decode+Unpack: 2.514s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 735.8225 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04540053-0.000734_balance beam _ balance beam_0.99765223.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n04540053-0.000734_balance beam _ balance beam_0.99765223.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04606251-0.003483_cliff _ cliff_0.9972174.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04606251-0.003483_cliff _ cliff_0.9972174.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 304,844B, BPFP=0.5786 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 423,736B, BPFP=0.8043 +⌛️ [2/4] FRONTEND: Frontend time: 2.644s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.531s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09940710 37.52238217 + layer.39.0 906.86880466 4189.69533528 + ------------------------------------------------------------------------------------- + TOTAL 453.48410588 2113.60885872 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 728580 +BPFP 0.6915 bits/point +EBPFP 0.6915 equivalent bits/point +MSE 2113.608859 +---------------------- -------------------------------------------------------- +Time: 5.227s Load: 0.052s, Pack+Encode: 2.644s, Decode+Unpack: 2.531s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2113.6089 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04606251-0.003483_cliff _ cliff_0.9972174.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n04606251-0.003483_cliff _ cliff_0.9972174.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07583066-0.003935_maraca _ maraca_0.5370013.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07583066-0.003935_maraca _ maraca_0.5370013.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 253,912B, BPFP=0.4819 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 420,604B, BPFP=0.7983 +⌛️ [2/4] FRONTEND: Frontend time: 2.627s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.499s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12045678 24.97712243 + layer.39.0 38.29438092 3817.37706511 + ------------------------------------------------------------------------------------- + TOTAL 19.20741885 1921.17709377 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 674516 +BPFP 0.6401 bits/point +EBPFP 0.6401 equivalent bits/point +MSE 1921.177094 +---------------------- -------------------------------------------------------- +Time: 5.187s Load: 0.061s, Pack+Encode: 2.627s, Decode+Unpack: 2.499s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1921.1771 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07583066-0.003935_maraca _ maraca_0.5370013.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n07583066-0.003935_maraca _ maraca_0.5370013.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07695742-0.003226_ant _ ant_0.64413536.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07695742-0.003226_ant _ ant_0.64413536.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.063s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 425,440B, BPFP=0.8075 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 472,996B, BPFP=0.8978 +⌛️ [2/4] FRONTEND: Frontend time: 2.618s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.524s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.16263347 289.57962828 + layer.39.0 172.10254191 3110.81681244 + ------------------------------------------------------------------------------------- + TOTAL 86.13258769 1700.19822036 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 898436 +BPFP 0.8527 bits/point +EBPFP 0.8527 equivalent bits/point +MSE 1700.198220 +---------------------- -------------------------------------------------------- +Time: 5.205s Load: 0.063s, Pack+Encode: 2.618s, Decode+Unpack: 2.524s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1700.1982 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07695742-0.003226_ant _ ant_0.64413536.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n07695742-0.003226_ant _ ant_0.64413536.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07718472-0.001330_spatula _ spatula_0.5388231.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07718472-0.001330_spatula _ spatula_0.5388231.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.058s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 275,456B, BPFP=0.5228 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 569,160B, BPFP=1.0803 +⌛️ [2/4] FRONTEND: Frontend time: 2.618s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.531s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09672572 8.60008769 + layer.39.0 34.52145211 4997.87463557 + ------------------------------------------------------------------------------------- + TOTAL 17.30908891 2503.23736163 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 844616 +BPFP 0.8016 bits/point +EBPFP 0.8016 equivalent bits/point +MSE 2503.237362 +---------------------- -------------------------------------------------------- +Time: 5.208s Load: 0.058s, Pack+Encode: 2.618s, Decode+Unpack: 2.531s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2503.2374 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07718472-0.001330_spatula _ spatula_0.5388231.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n07718472-0.001330_spatula _ spatula_0.5388231.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07831146-0.002710_guacamole _ guacamole_0.99510396.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07831146-0.002710_guacamole _ guacamole_0.99510396.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 307,600B, BPFP=0.5838 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 565,188B, BPFP=1.0728 +⌛️ [2/4] FRONTEND: Frontend time: 2.633s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.525s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09717902 8.58034101 + layer.39.0 26.55584533 4968.39358601 + ------------------------------------------------------------------------------------- + TOTAL 13.32651218 2488.48696351 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 872788 +BPFP 0.8283 bits/point +EBPFP 0.8283 equivalent bits/point +MSE 2488.486964 +---------------------- -------------------------------------------------------- +Time: 5.219s Load: 0.062s, Pack+Encode: 2.633s, Decode+Unpack: 2.525s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2488.4870 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07831146-0.002710_guacamole _ guacamole_0.99510396.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n07831146-0.002710_guacamole _ guacamole_0.99510396.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n09229709-0.002628_stethoscope _ stethoscope_0.9726458.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n09229709-0.002628_stethoscope _ stethoscope_0.9726458.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 267,952B, BPFP=0.5086 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 369,944B, BPFP=0.7022 +⌛️ [2/4] FRONTEND: Frontend time: 2.621s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.523s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10247729 12.48575965 + layer.39.0 58.71458181 2233.07191448 + ------------------------------------------------------------------------------------- + TOTAL 29.40852955 1122.77883706 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 637896 +BPFP 0.6054 bits/point +EBPFP 0.6054 equivalent bits/point +MSE 1122.778837 +---------------------- -------------------------------------------------------- +Time: 5.206s Load: 0.062s, Pack+Encode: 2.621s, Decode+Unpack: 2.523s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1122.7788 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n09229709-0.002628_stethoscope _ stethoscope_0.9726458.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n09229709-0.002628_stethoscope _ stethoscope_0.9726458.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12057211-0.000404_nail _ newt_0.31321314.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12057211-0.000404_nail _ newt_0.31321314.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 426,516B, BPFP=0.8096 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 330,396B, BPFP=0.6271 +⌛️ [2/4] FRONTEND: Frontend time: 2.633s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.519s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11577855 412.11008868 + layer.39.0 8.72387956 1474.78219145 + ------------------------------------------------------------------------------------- + TOTAL 4.41982905 943.44614006 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 756912 +BPFP 0.7183 bits/point +EBPFP 0.7183 equivalent bits/point +MSE 943.446140 +---------------------- -------------------------------------------------------- +Time: 5.214s Load: 0.061s, Pack+Encode: 2.633s, Decode+Unpack: 2.519s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 943.4461 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12057211-0.000404_nail _ newt_0.31321314.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n12057211-0.000404_nail _ newt_0.31321314.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12144580-0.002806_banana _ banana_0.999156.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12144580-0.002806_banana _ banana_0.999156.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.057s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 308,924B, BPFP=0.5864 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 597,176B, BPFP=1.1335 +⌛️ [2/4] FRONTEND: Frontend time: 2.622s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.521s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09629347 0.87908953 + layer.39.0 105.38953930 5348.03887269 + ------------------------------------------------------------------------------------- + TOTAL 52.74291638 2674.45898111 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 906100 +BPFP 0.8599 bits/point +EBPFP 0.8599 equivalent bits/point +MSE 2674.458981 +---------------------- -------------------------------------------------------- +Time: 5.200s Load: 0.057s, Pack+Encode: 2.622s, Decode+Unpack: 2.521s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2674.4590 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12144580-0.002806_banana _ banana_0.999156.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n12144580-0.002806_banana _ banana_0.999156.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12267677-0.004617_pomegranate _ pomegranate_0.9159373.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12267677-0.004617_pomegranate _ pomegranate_0.9159373.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 309,728B, BPFP=0.5879 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 463,352B, BPFP=0.8795 +⌛️ [2/4] FRONTEND: Frontend time: 2.619s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.521s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10323383 12.87658680 + layer.39.0 78.12042942 2921.59426628 + ------------------------------------------------------------------------------------- + TOTAL 39.11183162 1467.23542654 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 773080 +BPFP 0.7337 bits/point +EBPFP 0.7337 equivalent bits/point +MSE 1467.235427 +---------------------- -------------------------------------------------------- +Time: 5.203s Load: 0.062s, Pack+Encode: 2.619s, Decode+Unpack: 2.521s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1467.2354 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12267677-0.004617_pomegranate _ pomegranate_0.9159373.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1ka/n12267677-0.004617_pomegranate _ pomegranate_0.9159373.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 0.6896 bits/point +Avg EBPFP 0.6896 equivalent bits/point +Avg MSE 1397.100977 +Avg Time 5.205s +------------------------ ---------------------------- diff --git a/lambda0.01/elic-featurecoding-8bit-individual/cls_in1kr/dtufc_elic-featurecoding_dinov3-total_individual.log b/lambda0.01/elic-featurecoding-8bit-individual/cls_in1kr/dtufc_elic-featurecoding_dinov3-total_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..adab9a9b402d3c582127f6f3d6ae614ce54890a3 --- /dev/null +++ b/lambda0.01/elic-featurecoding-8bit-individual/cls_in1kr/dtufc_elic-featurecoding_dinov3-total_individual.log @@ -0,0 +1,9344 @@ +Experiment: dtufc_elic-featurecoding_dinov3-total_individual +Log file: output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/dtufc_elic-featurecoding_dinov3-total_individual.log +DTUFCCodecConfig: + arch: elic-featurecoding + handler: dinov3-total + checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.01_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.01_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 506 +Loaded elic-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.9' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.19' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.29' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.39' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json +Loaded per-key mappings: model=dinov3-total + Keys: ['layer.9', 'layer.19', 'layer.29', 'layer.39'] +---------------- ------------------------------------------------------------------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +Checkpoint codec_weights/elic_hybrid/elic2022-official_lambda0.01_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-DINOv3/imagenet1k-r +Output output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr +---------------- ------------------------------------------------------------------------------------------------------------------- +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01443537-graffiti_3.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01443537-graffiti_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 281,456B, BPFP=0.5342 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 375,428B, BPFP=0.7126 +⌛️ [2/4] FRONTEND: Frontend time: 3.103s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.546s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09690064 12.64483399 + layer.39.0 23.14008974 3167.07361516 + ------------------------------------------------------------------------------------- + TOTAL 11.61849519 1589.85922458 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 656884 +BPFP 0.6234 bits/point +EBPFP 0.6234 equivalent bits/point +MSE 1589.859225 +---------------------- -------------------------------------------------------- +Time: 5.721s Load: 0.072s, Pack+Encode: 3.103s, Decode+Unpack: 2.546s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1589.8592 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01443537-graffiti_3.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n01443537-graffiti_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01494475-misc_31.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01494475-misc_31.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 267,476B, BPFP=0.5077 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 530,788B, BPFP=1.0075 +⌛️ [2/4] FRONTEND: Frontend time: 2.614s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.524s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09558801 0.83808168 + layer.39.0 281.54433916 3939.27016521 + ------------------------------------------------------------------------------------- + TOTAL 140.81996359 1970.05412344 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 798264 +BPFP 0.7576 bits/point +EBPFP 0.7576 equivalent bits/point +MSE 1970.054123 +---------------------- -------------------------------------------------------- +Time: 5.207s Load: 0.069s, Pack+Encode: 2.614s, Decode+Unpack: 2.524s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1970.0541 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01494475-misc_31.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n01494475-misc_31.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01531178-cartoon_22.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01531178-cartoon_22.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 291,432B, BPFP=0.5532 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 369,924B, BPFP=0.7021 +⌛️ [2/4] FRONTEND: Frontend time: 2.627s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.510s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10319715 12.48614116 + layer.39.0 12.97479918 1540.61115160 + ------------------------------------------------------------------------------------- + TOTAL 6.53899817 776.54864638 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 661356 +BPFP 0.6277 bits/point +EBPFP 0.6277 equivalent bits/point +MSE 776.548646 +---------------------- -------------------------------------------------------- +Time: 5.186s Load: 0.050s, Pack+Encode: 2.627s, Decode+Unpack: 2.510s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 776.5486 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01531178-cartoon_22.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n01531178-cartoon_22.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01534433-painting_9.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01534433-painting_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 344,276B, BPFP=0.6535 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 328,492B, BPFP=0.6235 +⌛️ [2/4] FRONTEND: Frontend time: 2.607s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.515s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10660143 13.81667008 + layer.39.0 8.42910859 2316.90597668 + ------------------------------------------------------------------------------------- + TOTAL 4.26785501 1165.36132338 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 672768 +BPFP 0.6385 bits/point +EBPFP 0.6385 equivalent bits/point +MSE 1165.361323 +---------------------- -------------------------------------------------------- +Time: 5.183s Load: 0.061s, Pack+Encode: 2.607s, Decode+Unpack: 2.515s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1165.3613 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01534433-painting_9.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n01534433-painting_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01632777-toy_21.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01632777-toy_21.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 258,912B, BPFP=0.4914 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 688,236B, BPFP=1.3063 +⌛️ [2/4] FRONTEND: Frontend time: 2.627s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.515s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09516629 12.41101039 + layer.39.0 31.73491595 4745.38629738 + ------------------------------------------------------------------------------------- + TOTAL 15.91504112 2378.89865388 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 947148 +BPFP 0.8989 bits/point +EBPFP 0.8989 equivalent bits/point +MSE 2378.898654 +---------------------- -------------------------------------------------------- +Time: 5.203s Load: 0.061s, Pack+Encode: 2.627s, Decode+Unpack: 2.515s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2378.8987 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01632777-toy_21.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n01632777-toy_21.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01748264-misc_18.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01748264-misc_18.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 349,960B, BPFP=0.6643 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 411,504B, BPFP=0.7811 +⌛️ [2/4] FRONTEND: Frontend time: 2.620s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.521s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.16139180 138.78563836 + layer.39.0 362.83485180 3092.82555879 + ------------------------------------------------------------------------------------- + TOTAL 181.49812180 1615.80559858 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 761464 +BPFP 0.7227 bits/point +EBPFP 0.7227 equivalent bits/point +MSE 1615.805599 +---------------------- -------------------------------------------------------- +Time: 5.192s Load: 0.051s, Pack+Encode: 2.620s, Decode+Unpack: 2.521s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1615.8056 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01748264-misc_18.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n01748264-misc_18.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01784675-painting_2.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01784675-painting_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 373,180B, BPFP=0.7083 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 693,476B, BPFP=1.3163 +⌛️ [2/4] FRONTEND: Frontend time: 2.668s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.532s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13866578 61.91873557 + layer.39.0 232.10166120 5404.28522838 + ------------------------------------------------------------------------------------- + TOTAL 116.12016349 2733.10198198 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1066656 +BPFP 1.0123 bits/point +EBPFP 1.0123 equivalent bits/point +MSE 2733.101982 +---------------------- -------------------------------------------------------- +Time: 5.251s Load: 0.052s, Pack+Encode: 2.668s, Decode+Unpack: 2.532s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2733.1020 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01784675-painting_2.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n01784675-painting_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01820546-painting_29.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01820546-painting_29.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 355,408B, BPFP=0.6746 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 469,528B, BPFP=0.8912 +⌛️ [2/4] FRONTEND: Frontend time: 2.631s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.531s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10398871 16.21621986 + layer.39.0 202.99580904 4507.14480078 + ------------------------------------------------------------------------------------- + TOTAL 101.54989888 2261.68051032 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 824936 +BPFP 0.7829 bits/point +EBPFP 0.7829 equivalent bits/point +MSE 2261.680510 +---------------------- -------------------------------------------------------- +Time: 5.214s Load: 0.051s, Pack+Encode: 2.631s, Decode+Unpack: 2.531s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2261.6805 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01820546-painting_29.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n01820546-painting_29.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01833805-painting_23.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01833805-painting_23.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 296,184B, BPFP=0.5622 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 384,996B, BPFP=0.7308 +⌛️ [2/4] FRONTEND: Frontend time: 2.617s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.523s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09675035 12.43811972 + layer.39.0 56.43029868 2345.96501458 + ------------------------------------------------------------------------------------- + TOTAL 28.26352451 1179.20156715 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 681180 +BPFP 0.6465 bits/point +EBPFP 0.6465 equivalent bits/point +MSE 1179.201567 +---------------------- -------------------------------------------------------- +Time: 5.192s Load: 0.052s, Pack+Encode: 2.617s, Decode+Unpack: 2.523s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1179.2016 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01833805-painting_23.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n01833805-painting_23.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01860187-painting_2.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01860187-painting_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 300,168B, BPFP=0.5697 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 363,432B, BPFP=0.6898 +⌛️ [2/4] FRONTEND: Frontend time: 2.615s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.504s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09532418 0.87160718 + layer.39.0 11.39113179 2140.48590865 + ------------------------------------------------------------------------------------- + TOTAL 5.74322799 1070.67875791 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 663600 +BPFP 0.6298 bits/point +EBPFP 0.6298 equivalent bits/point +MSE 1070.678758 +---------------------- -------------------------------------------------------- +Time: 5.170s Load: 0.051s, Pack+Encode: 2.615s, Decode+Unpack: 2.504s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1070.6788 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01860187-painting_2.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n01860187-painting_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01944390-deviantart_6.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01944390-deviantart_6.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.053s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 253,024B, BPFP=0.4803 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 501,028B, BPFP=0.9510 +⌛️ [2/4] FRONTEND: Frontend time: 2.628s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.511s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10713051 0.89689098 + layer.39.0 82.30322218 4333.91302235 + ------------------------------------------------------------------------------------- + TOTAL 41.20517635 2167.40495667 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 754052 +BPFP 0.7156 bits/point +EBPFP 0.7156 equivalent bits/point +MSE 2167.404957 +---------------------- -------------------------------------------------------- +Time: 5.192s Load: 0.053s, Pack+Encode: 2.628s, Decode+Unpack: 2.511s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2167.4050 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01944390-deviantart_6.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n01944390-deviantart_6.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01983481-misc_6.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01983481-misc_6.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.053s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 323,888B, BPFP=0.6148 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 467,692B, BPFP=0.8877 +⌛️ [2/4] FRONTEND: Frontend time: 2.627s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.525s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10315659 12.74586693 + layer.39.0 236.29731535 4644.50340136 + ------------------------------------------------------------------------------------- + TOTAL 118.20023597 2328.62463414 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 791580 +BPFP 0.7512 bits/point +EBPFP 0.7512 equivalent bits/point +MSE 2328.624634 +---------------------- -------------------------------------------------------- +Time: 5.205s Load: 0.053s, Pack+Encode: 2.627s, Decode+Unpack: 2.525s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2328.6246 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01983481-misc_6.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n01983481-misc_6.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02051845-cartoon_2.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02051845-cartoon_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 285,156B, BPFP=0.5412 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 380,588B, BPFP=0.7224 +⌛️ [2/4] FRONTEND: Frontend time: 2.622s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.535s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11657756 12.31362366 + layer.39.0 123.57765428 2116.46987366 + ------------------------------------------------------------------------------------- + TOTAL 61.84711592 1064.39174866 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 665744 +BPFP 0.6318 bits/point +EBPFP 0.6318 equivalent bits/point +MSE 1064.391749 +---------------------- -------------------------------------------------------- +Time: 5.209s Load: 0.052s, Pack+Encode: 2.622s, Decode+Unpack: 2.535s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1064.3917 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02051845-cartoon_2.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n02051845-cartoon_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02056570-art_2.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02056570-art_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 247,712B, BPFP=0.4702 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 405,908B, BPFP=0.7704 +⌛️ [2/4] FRONTEND: Frontend time: 2.635s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.510s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09569211 12.57710004 + layer.39.0 33.39981930 3869.16350826 + ------------------------------------------------------------------------------------- + TOTAL 16.74775571 1940.87030415 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 653620 +BPFP 0.6203 bits/point +EBPFP 0.6203 equivalent bits/point +MSE 1940.870304 +---------------------- -------------------------------------------------------- +Time: 5.205s Load: 0.060s, Pack+Encode: 2.635s, Decode+Unpack: 2.510s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1940.8703 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02056570-art_2.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n02056570-art_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02085620-misc_90.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02085620-misc_90.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 296,348B, BPFP=0.5625 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 473,192B, BPFP=0.8982 +⌛️ [2/4] FRONTEND: Frontend time: 2.665s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.535s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09843166 13.20617996 + layer.39.0 72.76188958 3279.73785228 + ------------------------------------------------------------------------------------- + TOTAL 36.43016062 1646.47201612 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 769540 +BPFP 0.7303 bits/point +EBPFP 0.7303 equivalent bits/point +MSE 1646.472016 +---------------------- -------------------------------------------------------- +Time: 5.251s Load: 0.050s, Pack+Encode: 2.665s, Decode+Unpack: 2.535s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1646.4720 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02085620-misc_90.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n02085620-misc_90.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088094-misc_39.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088094-misc_39.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 305,140B, BPFP=0.5792 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 382,548B, BPFP=0.7261 +⌛️ [2/4] FRONTEND: Frontend time: 2.641s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.520s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09820385 12.29459920 + layer.39.0 12.32374423 2459.09936832 + ------------------------------------------------------------------------------------- + TOTAL 6.21097404 1235.69698376 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 687688 +BPFP 0.6526 bits/point +EBPFP 0.6526 equivalent bits/point +MSE 1235.696984 +---------------------- -------------------------------------------------------- +Time: 5.230s Load: 0.070s, Pack+Encode: 2.641s, Decode+Unpack: 2.520s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1235.6970 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088094-misc_39.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n02088094-misc_39.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088466-sketch_11.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088466-sketch_11.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.081s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 241,520B, BPFP=0.4584 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 423,528B, BPFP=0.8039 +⌛️ [2/4] FRONTEND: Frontend time: 2.642s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.519s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09459993 0.82956974 + layer.39.0 16.33682960 2347.56000972 + ------------------------------------------------------------------------------------- + TOTAL 8.21571477 1174.19478973 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 665048 +BPFP 0.6312 bits/point +EBPFP 0.6312 equivalent bits/point +MSE 1174.194790 +---------------------- -------------------------------------------------------- +Time: 5.243s Load: 0.081s, Pack+Encode: 2.642s, Decode+Unpack: 2.519s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1174.1948 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088466-sketch_11.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n02088466-sketch_11.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02094433-misc_20.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02094433-misc_20.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 296,792B, BPFP=0.5633 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 444,776B, BPFP=0.8442 +⌛️ [2/4] FRONTEND: Frontend time: 2.659s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.521s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09538842 12.47459324 + layer.39.0 94.83275632 3413.66229349 + ------------------------------------------------------------------------------------- + TOTAL 47.46407237 1713.06844337 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 741568 +BPFP 0.7038 bits/point +EBPFP 0.7038 equivalent bits/point +MSE 1713.068443 +---------------------- -------------------------------------------------------- +Time: 5.251s Load: 0.071s, Pack+Encode: 2.659s, Decode+Unpack: 2.521s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1713.0684 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02094433-misc_20.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n02094433-misc_20.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02097298-misc_9.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02097298-misc_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 394,736B, BPFP=0.7492 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 455,768B, BPFP=0.8651 +⌛️ [2/4] FRONTEND: Frontend time: 2.651s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.545s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11199322 121.00025814 + layer.39.0 26.16675018 3948.09450923 + ------------------------------------------------------------------------------------- + TOTAL 13.13937170 2034.54738369 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 850504 +BPFP 0.8072 bits/point +EBPFP 0.8072 equivalent bits/point +MSE 2034.547384 +---------------------- -------------------------------------------------------- +Time: 5.247s Load: 0.051s, Pack+Encode: 2.651s, Decode+Unpack: 2.545s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2034.5474 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02097298-misc_9.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n02097298-misc_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02106662-misc_55.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02106662-misc_55.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 308,496B, BPFP=0.5856 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 403,596B, BPFP=0.7661 +⌛️ [2/4] FRONTEND: Frontend time: 2.640s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.524s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09642073 24.42545098 + layer.39.0 14.86428154 2091.85034014 + ------------------------------------------------------------------------------------- + TOTAL 7.48035113 1058.13789556 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 712092 +BPFP 0.6758 bits/point +EBPFP 0.6758 equivalent bits/point +MSE 1058.137896 +---------------------- -------------------------------------------------------- +Time: 5.235s Load: 0.070s, Pack+Encode: 2.640s, Decode+Unpack: 2.524s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1058.1379 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02106662-misc_55.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n02106662-misc_55.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02109525-sketch_23.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02109525-sketch_23.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.058s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 248,768B, BPFP=0.4722 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 443,628B, BPFP=0.8420 +⌛️ [2/4] FRONTEND: Frontend time: 2.624s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.537s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09568003 0.84336226 + layer.39.0 14.01675815 2612.38386783 + ------------------------------------------------------------------------------------- + TOTAL 7.05621909 1306.61361505 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 692396 +BPFP 0.6571 bits/point +EBPFP 0.6571 equivalent bits/point +MSE 1306.613615 +---------------------- -------------------------------------------------------- +Time: 5.219s Load: 0.058s, Pack+Encode: 2.624s, Decode+Unpack: 2.537s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1306.6136 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02109525-sketch_23.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n02109525-sketch_23.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110185-painting_33.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110185-painting_33.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 279,948B, BPFP=0.5314 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 517,772B, BPFP=0.9828 +⌛️ [2/4] FRONTEND: Frontend time: 2.637s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.529s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09599521 0.86061806 + layer.39.0 22.05506522 4142.75607386 + ------------------------------------------------------------------------------------- + TOTAL 11.07553021 2071.80834596 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 797720 +BPFP 0.7571 bits/point +EBPFP 0.7571 equivalent bits/point +MSE 2071.808346 +---------------------- -------------------------------------------------------- +Time: 5.218s Load: 0.052s, Pack+Encode: 2.637s, Decode+Unpack: 2.529s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2071.8083 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110185-painting_33.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n02110185-painting_33.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110341-misc_162.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110341-misc_162.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 249,396B, BPFP=0.4734 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 342,792B, BPFP=0.6506 +⌛️ [2/4] FRONTEND: Frontend time: 2.617s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.526s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11124049 12.66396000 + layer.39.0 14.33747210 1580.66229349 + ------------------------------------------------------------------------------------- + TOTAL 7.22435629 796.66312675 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 592188 +BPFP 0.5620 bits/point +EBPFP 0.5620 equivalent bits/point +MSE 796.663127 +---------------------- -------------------------------------------------------- +Time: 5.213s Load: 0.070s, Pack+Encode: 2.617s, Decode+Unpack: 2.526s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 796.6631 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110341-misc_162.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n02110341-misc_162.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02165456-tattoo_37.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02165456-tattoo_37.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 336,252B, BPFP=0.6382 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 499,332B, BPFP=0.9478 +⌛️ [2/4] FRONTEND: Frontend time: 2.651s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.531s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09780899 0.87086112 + layer.39.0 88.96013271 4164.25558795 + ------------------------------------------------------------------------------------- + TOTAL 44.52897085 2082.56322453 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 835584 +BPFP 0.7930 bits/point +EBPFP 0.7930 equivalent bits/point +MSE 2082.563225 +---------------------- -------------------------------------------------------- +Time: 5.254s Load: 0.071s, Pack+Encode: 2.651s, Decode+Unpack: 2.531s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2082.5632 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02165456-tattoo_37.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n02165456-tattoo_37.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02219486-misc_3.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02219486-misc_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.057s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 246,772B, BPFP=0.4684 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 312,516B, BPFP=0.5932 +⌛️ [2/4] FRONTEND: Frontend time: 2.617s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.534s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10021695 0.84417203 + layer.39.0 75.73793580 1230.92140428 + ------------------------------------------------------------------------------------- + TOTAL 37.91907638 615.88278815 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 559288 +BPFP 0.5308 bits/point +EBPFP 0.5308 equivalent bits/point +MSE 615.882788 +---------------------- -------------------------------------------------------- +Time: 5.208s Load: 0.057s, Pack+Encode: 2.617s, Decode+Unpack: 2.534s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 615.8828 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02219486-misc_3.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n02219486-misc_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02226429-tattoo_0.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02226429-tattoo_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 284,116B, BPFP=0.5393 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 432,440B, BPFP=0.8208 +⌛️ [2/4] FRONTEND: Frontend time: 2.642s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.523s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09506506 12.33963022 + layer.39.0 201.13660107 3016.22011662 + ------------------------------------------------------------------------------------- + TOTAL 100.61583306 1514.27987342 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 716556 +BPFP 0.6800 bits/point +EBPFP 0.6800 equivalent bits/point +MSE 1514.279873 +---------------------- -------------------------------------------------------- +Time: 5.236s Load: 0.070s, Pack+Encode: 2.642s, Decode+Unpack: 2.523s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1514.2799 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02226429-tattoo_0.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n02226429-tattoo_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02233338-tattoo_5.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02233338-tattoo_5.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 282,056B, BPFP=0.5354 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 537,100B, BPFP=1.0195 +⌛️ [2/4] FRONTEND: Frontend time: 2.636s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.532s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09502332 12.44255269 + layer.39.0 172.43500972 3580.89795918 + ------------------------------------------------------------------------------------- + TOTAL 86.26501652 1796.67025594 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 819156 +BPFP 0.7774 bits/point +EBPFP 0.7774 equivalent bits/point +MSE 1796.670256 +---------------------- -------------------------------------------------------- +Time: 5.237s Load: 0.070s, Pack+Encode: 2.636s, Decode+Unpack: 2.532s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1796.6703 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02233338-tattoo_5.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n02233338-tattoo_5.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02279972-painting_2.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02279972-painting_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 395,448B, BPFP=0.7506 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 510,488B, BPFP=0.9689 +⌛️ [2/4] FRONTEND: Frontend time: 2.636s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.548s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11337867 323.44800777 + layer.39.0 361.17623299 4696.77842566 + ------------------------------------------------------------------------------------- + TOTAL 180.64480583 2510.11321672 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 905936 +BPFP 0.8598 bits/point +EBPFP 0.8598 equivalent bits/point +MSE 2510.113217 +---------------------- -------------------------------------------------------- +Time: 5.254s Load: 0.071s, Pack+Encode: 2.636s, Decode+Unpack: 2.548s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2510.1132 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02279972-painting_2.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n02279972-painting_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02317335-sculpture_0.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02317335-sculpture_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 283,048B, BPFP=0.5372 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 556,660B, BPFP=1.0566 +⌛️ [2/4] FRONTEND: Frontend time: 2.636s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.524s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09546056 0.84875014 + layer.39.0 1163.18707483 4060.47448980 + ------------------------------------------------------------------------------------- + TOTAL 581.64126769 2030.66161997 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 839708 +BPFP 0.7969 bits/point +EBPFP 0.7969 equivalent bits/point +MSE 2030.661620 +---------------------- -------------------------------------------------------- +Time: 5.231s Load: 0.070s, Pack+Encode: 2.636s, Decode+Unpack: 2.524s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2030.6616 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02317335-sculpture_0.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n02317335-sculpture_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02346627-painting_9.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02346627-painting_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 335,180B, BPFP=0.6362 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 467,168B, BPFP=0.8867 +⌛️ [2/4] FRONTEND: Frontend time: 2.621s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.525s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13205896 221.32003158 + layer.39.0 503.01482021 3489.34086492 + ------------------------------------------------------------------------------------- + TOTAL 251.57343959 1855.33044825 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 802348 +BPFP 0.7615 bits/point +EBPFP 0.7615 equivalent bits/point +MSE 1855.330448 +---------------------- -------------------------------------------------------- +Time: 5.216s Load: 0.070s, Pack+Encode: 2.621s, Decode+Unpack: 2.525s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1855.3304 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02346627-painting_9.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n02346627-painting_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02391049-deviantart_0.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02391049-deviantart_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 258,812B, BPFP=0.4912 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 354,024B, BPFP=0.6720 +⌛️ [2/4] FRONTEND: Frontend time: 2.607s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.499s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10116939 12.39689436 + layer.39.0 17.42674737 1911.99866375 + ------------------------------------------------------------------------------------- + TOTAL 8.76395838 962.19777906 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 612836 +BPFP 0.5816 bits/point +EBPFP 0.5816 equivalent bits/point +MSE 962.197779 +---------------------- -------------------------------------------------------- +Time: 5.177s Load: 0.071s, Pack+Encode: 2.607s, Decode+Unpack: 2.499s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 962.1978 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02391049-deviantart_0.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n02391049-deviantart_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02395406-sculpture_31.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02395406-sculpture_31.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 403,368B, BPFP=0.7656 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 397,408B, BPFP=0.7543 +⌛️ [2/4] FRONTEND: Frontend time: 2.656s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.524s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11469608 76.70621508 + layer.39.0 30.55020044 3610.71404276 + ------------------------------------------------------------------------------------- + TOTAL 15.33244826 1843.71012892 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 800776 +BPFP 0.7600 bits/point +EBPFP 0.7600 equivalent bits/point +MSE 1843.710129 +---------------------- -------------------------------------------------------- +Time: 5.260s Load: 0.080s, Pack+Encode: 2.656s, Decode+Unpack: 2.524s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1843.7101 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02395406-sculpture_31.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n02395406-sculpture_31.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02445715-art_3.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02445715-art_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 283,484B, BPFP=0.5381 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 459,992B, BPFP=0.8731 +⌛️ [2/4] FRONTEND: Frontend time: 2.610s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.521s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09587883 12.67182280 + layer.39.0 77.63827138 4910.50583090 + ------------------------------------------------------------------------------------- + TOTAL 38.86707511 2461.58882685 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 743476 +BPFP 0.7056 bits/point +EBPFP 0.7056 equivalent bits/point +MSE 2461.588827 +---------------------- -------------------------------------------------------- +Time: 5.201s Load: 0.071s, Pack+Encode: 2.610s, Decode+Unpack: 2.521s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2461.5888 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02445715-art_3.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n02445715-art_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02672831-sculpture_2.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02672831-sculpture_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 353,356B, BPFP=0.6707 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 624,520B, BPFP=1.1854 +⌛️ [2/4] FRONTEND: Frontend time: 2.639s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.541s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11638676 46.69877840 + layer.39.0 42.74346681 3358.31316812 + ------------------------------------------------------------------------------------- + TOTAL 21.42992678 1702.50597326 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 977876 +BPFP 0.9280 bits/point +EBPFP 0.9280 equivalent bits/point +MSE 1702.505973 +---------------------- -------------------------------------------------------- +Time: 5.251s Load: 0.071s, Pack+Encode: 2.639s, Decode+Unpack: 2.541s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1702.5060 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02672831-sculpture_2.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n02672831-sculpture_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02701002-videogame_1.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02701002-videogame_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 274,836B, BPFP=0.5217 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 544,324B, BPFP=1.0332 +⌛️ [2/4] FRONTEND: Frontend time: 2.642s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.528s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10320827 12.55036747 + layer.39.0 160.61054422 3268.06292517 + ------------------------------------------------------------------------------------- + TOTAL 80.35687624 1640.30664632 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 819160 +BPFP 0.7774 bits/point +EBPFP 0.7774 equivalent bits/point +MSE 1640.306646 +---------------------- -------------------------------------------------------- +Time: 5.240s Load: 0.070s, Pack+Encode: 2.642s, Decode+Unpack: 2.528s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1640.3066 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02701002-videogame_1.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n02701002-videogame_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02749479-misc_35.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02749479-misc_35.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 225,812B, BPFP=0.4286 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 460,468B, BPFP=0.8740 +⌛️ [2/4] FRONTEND: Frontend time: 2.629s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.529s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09764870 0.86276153 + layer.39.0 172.65676628 3381.19922255 + ------------------------------------------------------------------------------------- + TOTAL 86.37720749 1691.03099204 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 686280 +BPFP 0.6513 bits/point +EBPFP 0.6513 equivalent bits/point +MSE 1691.030992 +---------------------- -------------------------------------------------------- +Time: 5.227s Load: 0.069s, Pack+Encode: 2.629s, Decode+Unpack: 2.529s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1691.0310 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02749479-misc_35.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n02749479-misc_35.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02769748-cartoon_11.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02769748-cartoon_11.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 238,416B, BPFP=0.4525 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 396,568B, BPFP=0.7527 +⌛️ [2/4] FRONTEND: Frontend time: 2.620s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.498s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12263774 12.47449169 + layer.39.0 11.02823964 2075.23445092 + ------------------------------------------------------------------------------------- + TOTAL 5.57543869 1043.85447131 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 634984 +BPFP 0.6026 bits/point +EBPFP 0.6026 equivalent bits/point +MSE 1043.854471 +---------------------- -------------------------------------------------------- +Time: 5.189s Load: 0.071s, Pack+Encode: 2.620s, Decode+Unpack: 2.498s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1043.8545 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02769748-cartoon_11.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n02769748-cartoon_11.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02793495-sketch_11.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02793495-sketch_11.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 243,388B, BPFP=0.4620 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 390,392B, BPFP=0.7410 +⌛️ [2/4] FRONTEND: Frontend time: 2.633s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.517s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09793751 25.18928420 + layer.39.0 182.75789602 3199.22740525 + ------------------------------------------------------------------------------------- + TOTAL 91.42791676 1612.20834472 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 633780 +BPFP 0.6015 bits/point +EBPFP 0.6015 equivalent bits/point +MSE 1612.208345 +---------------------- -------------------------------------------------------- +Time: 5.200s Load: 0.051s, Pack+Encode: 2.633s, Decode+Unpack: 2.517s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1612.2083 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02793495-sketch_11.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n02793495-sketch_11.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02797295-misc_13.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02797295-misc_13.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 442,264B, BPFP=0.8395 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 590,664B, BPFP=1.1211 +⌛️ [2/4] FRONTEND: Frontend time: 2.660s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.534s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.17140635 658.93677114 + layer.39.0 172.50999150 3904.48979592 + ------------------------------------------------------------------------------------- + TOTAL 86.34069892 2281.71328353 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1032928 +BPFP 0.9803 bits/point +EBPFP 0.9803 equivalent bits/point +MSE 2281.713284 +---------------------- -------------------------------------------------------- +Time: 5.263s Load: 0.069s, Pack+Encode: 2.660s, Decode+Unpack: 2.534s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2281.7133 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02797295-misc_13.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n02797295-misc_13.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02802426-art_1.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02802426-art_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 392,224B, BPFP=0.7445 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 577,732B, BPFP=1.0966 +⌛️ [2/4] FRONTEND: Frontend time: 2.658s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.550s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.16523854 197.98205175 + layer.39.0 477.65184645 3490.29494655 + ------------------------------------------------------------------------------------- + TOTAL 238.90854250 1844.13849915 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 969956 +BPFP 0.9205 bits/point +EBPFP 0.9205 equivalent bits/point +MSE 1844.138499 +---------------------- -------------------------------------------------------- +Time: 5.280s Load: 0.072s, Pack+Encode: 2.658s, Decode+Unpack: 2.550s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1844.1385 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02802426-art_1.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n02802426-art_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02814860-sticker_8.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02814860-sticker_8.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 299,840B, BPFP=0.5691 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 387,468B, BPFP=0.7354 +⌛️ [2/4] FRONTEND: Frontend time: 2.639s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.514s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12757226 22.60652598 + layer.39.0 19.27598852 2264.45894072 + ------------------------------------------------------------------------------------- + TOTAL 9.70178039 1143.53273335 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 687308 +BPFP 0.6523 bits/point +EBPFP 0.6523 equivalent bits/point +MSE 1143.532733 +---------------------- -------------------------------------------------------- +Time: 5.204s Load: 0.050s, Pack+Encode: 2.639s, Decode+Unpack: 2.514s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1143.5327 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02814860-sticker_8.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n02814860-sticker_8.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02841315-graphic_4.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02841315-graphic_4.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 328,328B, BPFP=0.6232 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 623,752B, BPFP=1.1839 +⌛️ [2/4] FRONTEND: Frontend time: 2.622s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.528s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11826141 9.03031140 + layer.39.0 55.46440340 3684.67978620 + ------------------------------------------------------------------------------------- + TOTAL 27.79133240 1846.85504880 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 952080 +BPFP 0.9036 bits/point +EBPFP 0.9036 equivalent bits/point +MSE 1846.855049 +---------------------- -------------------------------------------------------- +Time: 5.211s Load: 0.060s, Pack+Encode: 2.622s, Decode+Unpack: 2.528s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1846.8550 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02841315-graphic_4.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n02841315-graphic_4.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02843684-cartoon_9.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02843684-cartoon_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 353,404B, BPFP=0.6708 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 400,204B, BPFP=0.7596 +⌛️ [2/4] FRONTEND: Frontend time: 2.639s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.528s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12386809 12.88904580 + layer.39.0 312.00962707 2239.52721088 + ------------------------------------------------------------------------------------- + TOTAL 156.06674758 1126.20812834 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 753608 +BPFP 0.7152 bits/point +EBPFP 0.7152 equivalent bits/point +MSE 1126.208128 +---------------------- -------------------------------------------------------- +Time: 5.237s Load: 0.070s, Pack+Encode: 2.639s, Decode+Unpack: 2.528s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1126.2081 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02843684-cartoon_9.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n02843684-cartoon_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02883205-sketch_15.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02883205-sketch_15.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 257,212B, BPFP=0.4882 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 458,876B, BPFP=0.8710 +⌛️ [2/4] FRONTEND: Frontend time: 2.621s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.541s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09796664 24.54858517 + layer.39.0 103.64267493 3356.96598639 + ------------------------------------------------------------------------------------- + TOTAL 51.87032078 1690.75728578 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 716088 +BPFP 0.6796 bits/point +EBPFP 0.6796 equivalent bits/point +MSE 1690.757286 +---------------------- -------------------------------------------------------- +Time: 5.234s Load: 0.071s, Pack+Encode: 2.621s, Decode+Unpack: 2.541s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1690.7573 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02883205-sketch_15.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n02883205-sketch_15.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02906734-graffiti_3.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02906734-graffiti_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 462,796B, BPFP=0.8784 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 554,896B, BPFP=1.0532 +⌛️ [2/4] FRONTEND: Frontend time: 2.667s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.527s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.17339475 287.02554057 + layer.39.0 166.12656402 4757.42225462 + ------------------------------------------------------------------------------------- + TOTAL 83.14997939 2522.22389759 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1017692 +BPFP 0.9658 bits/point +EBPFP 0.9658 equivalent bits/point +MSE 2522.223898 +---------------------- -------------------------------------------------------- +Time: 5.246s Load: 0.052s, Pack+Encode: 2.667s, Decode+Unpack: 2.527s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2522.2239 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02906734-graffiti_3.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n02906734-graffiti_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02909870-sketch_0.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02909870-sketch_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 277,948B, BPFP=0.5276 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 384,564B, BPFP=0.7299 +⌛️ [2/4] FRONTEND: Frontend time: 2.648s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.525s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.15317524 24.77183552 + layer.39.0 167.75886783 2631.88775510 + ------------------------------------------------------------------------------------- + TOTAL 83.95602154 1328.32979531 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 662512 +BPFP 0.6288 bits/point +EBPFP 0.6288 equivalent bits/point +MSE 1328.329795 +---------------------- -------------------------------------------------------- +Time: 5.243s Load: 0.070s, Pack+Encode: 2.648s, Decode+Unpack: 2.525s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1328.3298 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02909870-sketch_0.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n02909870-sketch_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02939185-painting_0.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02939185-painting_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 254,452B, BPFP=0.4830 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 435,852B, BPFP=0.8273 +⌛️ [2/4] FRONTEND: Frontend time: 2.624s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.501s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09512242 0.83296380 + layer.39.0 131.28711127 3235.24975705 + ------------------------------------------------------------------------------------- + TOTAL 65.69111684 1618.04136042 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 690304 +BPFP 0.6551 bits/point +EBPFP 0.6551 equivalent bits/point +MSE 1618.041360 +---------------------- -------------------------------------------------------- +Time: 5.196s Load: 0.071s, Pack+Encode: 2.624s, Decode+Unpack: 2.501s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1618.0414 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02939185-painting_0.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n02939185-painting_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02948072-misc_10.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02948072-misc_10.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 261,304B, BPFP=0.4960 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 473,448B, BPFP=0.8986 +⌛️ [2/4] FRONTEND: Frontend time: 2.637s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.528s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09566823 12.56534617 + layer.39.0 102.81622783 3222.52089407 + ------------------------------------------------------------------------------------- + TOTAL 51.45594803 1617.54312012 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 734752 +BPFP 0.6973 bits/point +EBPFP 0.6973 equivalent bits/point +MSE 1617.543120 +---------------------- -------------------------------------------------------- +Time: 5.236s Load: 0.072s, Pack+Encode: 2.637s, Decode+Unpack: 2.528s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1617.5431 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02948072-misc_10.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n02948072-misc_10.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02950826-art_1.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02950826-art_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 288,528B, BPFP=0.5476 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 618,616B, BPFP=1.1742 +⌛️ [2/4] FRONTEND: Frontend time: 2.661s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.533s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09506074 24.37920804 + layer.39.0 1071.96149174 3821.02575316 + ------------------------------------------------------------------------------------- + TOTAL 536.02827624 1922.70248060 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 907144 +BPFP 0.8609 bits/point +EBPFP 0.8609 equivalent bits/point +MSE 1922.702481 +---------------------- -------------------------------------------------------- +Time: 5.265s Load: 0.071s, Pack+Encode: 2.661s, Decode+Unpack: 2.533s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1922.7025 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02950826-art_1.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n02950826-art_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02951358-misc_1.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02951358-misc_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 222,068B, BPFP=0.4215 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 339,916B, BPFP=0.6452 +⌛️ [2/4] FRONTEND: Frontend time: 2.623s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.526s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09568294 0.84131494 + layer.39.0 598.97078474 3360.17565598 + ------------------------------------------------------------------------------------- + TOTAL 299.53323384 1680.50848546 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 561984 +BPFP 0.5333 bits/point +EBPFP 0.5333 equivalent bits/point +MSE 1680.508485 +---------------------- -------------------------------------------------------- +Time: 5.220s Load: 0.071s, Pack+Encode: 2.623s, Decode+Unpack: 2.526s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1680.5085 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02951358-misc_1.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n02951358-misc_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02966193-cartoon_22.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02966193-cartoon_22.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 410,120B, BPFP=0.7784 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 645,708B, BPFP=1.2256 +⌛️ [2/4] FRONTEND: Frontend time: 2.670s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.539s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10376222 201.04234998 + layer.39.0 767.85532070 4031.33284742 + ------------------------------------------------------------------------------------- + TOTAL 383.97954146 2116.18759870 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1055828 +BPFP 1.0020 bits/point +EBPFP 1.0020 equivalent bits/point +MSE 2116.187599 +---------------------- -------------------------------------------------------- +Time: 5.260s Load: 0.051s, Pack+Encode: 2.670s, Decode+Unpack: 2.539s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2116.1876 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02966193-cartoon_22.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n02966193-cartoon_22.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02980441-graphic_3.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02980441-graphic_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 232,440B, BPFP=0.4412 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 267,860B, BPFP=0.5084 +⌛️ [2/4] FRONTEND: Frontend time: 2.610s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.498s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09509088 12.33852553 + layer.39.0 13.13791359 1020.13095238 + ------------------------------------------------------------------------------------- + TOTAL 6.61650224 516.23473896 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 500300 +BPFP 0.4748 bits/point +EBPFP 0.4748 equivalent bits/point +MSE 516.234739 +---------------------- -------------------------------------------------------- +Time: 5.178s Load: 0.070s, Pack+Encode: 2.610s, Decode+Unpack: 2.498s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 516.2347 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02980441-graphic_3.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n02980441-graphic_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03124170-painting_15.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03124170-painting_15.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 339,628B, BPFP=0.6446 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 755,892B, BPFP=1.4347 +⌛️ [2/4] FRONTEND: Frontend time: 2.638s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.524s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10783903 37.44082923 + layer.39.0 326.57091229 5087.28765792 + ------------------------------------------------------------------------------------- + TOTAL 163.33937566 2562.36424358 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1095520 +BPFP 1.0397 bits/point +EBPFP 1.0397 equivalent bits/point +MSE 2562.364244 +---------------------- -------------------------------------------------------- +Time: 5.213s Load: 0.050s, Pack+Encode: 2.638s, Decode+Unpack: 2.524s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2562.3642 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03124170-painting_15.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n03124170-painting_15.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03345487-toy_17.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03345487-toy_17.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 270,480B, BPFP=0.5134 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 492,656B, BPFP=0.9351 +⌛️ [2/4] FRONTEND: Frontend time: 2.615s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.520s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10662318 12.65571664 + layer.39.0 198.63900024 3941.56972789 + ------------------------------------------------------------------------------------- + TOTAL 99.37281171 1977.11272227 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 763136 +BPFP 0.7242 bits/point +EBPFP 0.7242 equivalent bits/point +MSE 1977.112722 +---------------------- -------------------------------------------------------- +Time: 5.207s Load: 0.072s, Pack+Encode: 2.615s, Decode+Unpack: 2.520s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1977.1127 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03345487-toy_17.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n03345487-toy_17.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03372029-sketch_14.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03372029-sketch_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 336,832B, BPFP=0.6393 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 477,064B, BPFP=0.9055 +⌛️ [2/4] FRONTEND: Frontend time: 2.652s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.548s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12162214 57.90283725 + layer.39.0 228.06095117 3878.31802721 + ------------------------------------------------------------------------------------- + TOTAL 114.09128665 1968.11043223 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 813896 +BPFP 0.7724 bits/point +EBPFP 0.7724 equivalent bits/point +MSE 1968.110432 +---------------------- -------------------------------------------------------- +Time: 5.272s Load: 0.071s, Pack+Encode: 2.652s, Decode+Unpack: 2.548s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1968.1104 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03372029-sketch_14.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n03372029-sketch_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03424325-misc_4.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03424325-misc_4.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 329,200B, BPFP=0.6248 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 492,152B, BPFP=0.9341 +⌛️ [2/4] FRONTEND: Frontend time: 2.641s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.519s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10761499 37.07983707 + layer.39.0 21.03287666 2704.81146744 + ------------------------------------------------------------------------------------- + TOTAL 10.57024582 1370.94565226 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 821352 +BPFP 0.7795 bits/point +EBPFP 0.7795 equivalent bits/point +MSE 1370.945652 +---------------------- -------------------------------------------------------- +Time: 5.219s Load: 0.059s, Pack+Encode: 2.641s, Decode+Unpack: 2.519s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1370.9457 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03424325-misc_4.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n03424325-misc_4.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03467068-sketch_17.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03467068-sketch_17.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 267,656B, BPFP=0.5080 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 468,136B, BPFP=0.8886 +⌛️ [2/4] FRONTEND: Frontend time: 2.643s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.520s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09564773 8.58566038 + layer.39.0 208.14688107 2871.43197279 + ------------------------------------------------------------------------------------- + TOTAL 104.12126440 1440.00881658 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 735792 +BPFP 0.6983 bits/point +EBPFP 0.6983 equivalent bits/point +MSE 1440.008817 +---------------------- -------------------------------------------------------- +Time: 5.234s Load: 0.070s, Pack+Encode: 2.643s, Decode+Unpack: 2.520s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1440.0088 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03467068-sketch_17.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n03467068-sketch_17.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03481172-sketch_9.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03481172-sketch_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 259,180B, BPFP=0.4919 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 434,064B, BPFP=0.8239 +⌛️ [2/4] FRONTEND: Frontend time: 2.660s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.527s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14641065 12.82772356 + layer.39.0 516.28267736 2766.67006803 + ------------------------------------------------------------------------------------- + TOTAL 258.21454400 1389.74889579 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 693244 +BPFP 0.6579 bits/point +EBPFP 0.6579 equivalent bits/point +MSE 1389.748896 +---------------------- -------------------------------------------------------- +Time: 5.258s Load: 0.070s, Pack+Encode: 2.660s, Decode+Unpack: 2.527s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1389.7489 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03481172-sketch_9.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n03481172-sketch_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03494278-deviantart_3.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03494278-deviantart_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 195,672B, BPFP=0.3714 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 361,508B, BPFP=0.6862 +⌛️ [2/4] FRONTEND: Frontend time: 2.625s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.536s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09714438 0.90807726 + layer.39.0 11.38600982 1903.33430515 + ------------------------------------------------------------------------------------- + TOTAL 5.74157710 952.12119120 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 557180 +BPFP 0.5288 bits/point +EBPFP 0.5288 equivalent bits/point +MSE 952.121191 +---------------------- -------------------------------------------------------- +Time: 5.230s Load: 0.070s, Pack+Encode: 2.625s, Decode+Unpack: 2.536s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 952.1212 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03494278-deviantart_3.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n03494278-deviantart_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03495258-painting_3.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03495258-painting_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 349,104B, BPFP=0.6626 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 605,280B, BPFP=1.1489 +⌛️ [2/4] FRONTEND: Frontend time: 2.623s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.527s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10398556 56.62799897 + layer.39.0 359.17207240 3503.29883382 + ------------------------------------------------------------------------------------- + TOTAL 179.63802898 1779.96341639 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 954384 +BPFP 0.9057 bits/point +EBPFP 0.9057 equivalent bits/point +MSE 1779.963416 +---------------------- -------------------------------------------------------- +Time: 5.219s Load: 0.069s, Pack+Encode: 2.623s, Decode+Unpack: 2.527s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1779.9634 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03495258-painting_3.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n03495258-painting_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03498962-sketch_8.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03498962-sketch_8.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 349,112B, BPFP=0.6626 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 424,436B, BPFP=0.8056 +⌛️ [2/4] FRONTEND: Frontend time: 2.624s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.536s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.16074808 122.77614037 + layer.39.0 476.99061589 2313.95821186 + ------------------------------------------------------------------------------------- + TOTAL 238.57568198 1218.36717611 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 773548 +BPFP 0.7341 bits/point +EBPFP 0.7341 equivalent bits/point +MSE 1218.367176 +---------------------- -------------------------------------------------------- +Time: 5.230s Load: 0.070s, Pack+Encode: 2.624s, Decode+Unpack: 2.536s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1218.3672 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03498962-sketch_8.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n03498962-sketch_8.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03602883-misc_12.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03602883-misc_12.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.073s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 185,524B, BPFP=0.3521 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 310,108B, BPFP=0.5886 +⌛️ [2/4] FRONTEND: Frontend time: 2.612s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.496s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.09080038 12.56699371 + layer.39.0 100.93773536 1345.23797376 + ------------------------------------------------------------------------------------- + TOTAL 54.51426787 678.90248373 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 495632 +BPFP 0.4704 bits/point +EBPFP 0.4704 equivalent bits/point +MSE 678.902484 +---------------------- -------------------------------------------------------- +Time: 5.180s Load: 0.073s, Pack+Encode: 2.612s, Decode+Unpack: 2.496s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 678.9025 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03602883-misc_12.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n03602883-misc_12.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03630383-toy_1.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03630383-toy_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 223,888B, BPFP=0.4250 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 401,480B, BPFP=0.7620 +⌛️ [2/4] FRONTEND: Frontend time: 2.636s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.521s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09574974 8.60844494 + layer.39.0 14.66923857 1813.43476676 + ------------------------------------------------------------------------------------- + TOTAL 7.38249415 911.02160585 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 625368 +BPFP 0.5935 bits/point +EBPFP 0.5935 equivalent bits/point +MSE 911.021606 +---------------------- -------------------------------------------------------- +Time: 5.227s Load: 0.070s, Pack+Encode: 2.636s, Decode+Unpack: 2.521s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 911.0216 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03630383-toy_1.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n03630383-toy_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03649909-toy_22.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03649909-toy_22.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 230,216B, BPFP=0.4370 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 258,068B, BPFP=0.4898 +⌛️ [2/4] FRONTEND: Frontend time: 2.619s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.493s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09878858 12.35395029 + layer.39.0 29.68475348 1074.74951409 + ------------------------------------------------------------------------------------- + TOTAL 14.89177103 543.55173219 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 488284 +BPFP 0.4634 bits/point +EBPFP 0.4634 equivalent bits/point +MSE 543.551732 +---------------------- -------------------------------------------------------- +Time: 5.171s Load: 0.060s, Pack+Encode: 2.619s, Decode+Unpack: 2.493s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 543.5517 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03649909-toy_22.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n03649909-toy_22.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03676483-sculpture_3.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03676483-sculpture_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 247,380B, BPFP=0.4695 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 575,936B, BPFP=1.0932 +⌛️ [2/4] FRONTEND: Frontend time: 2.636s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.537s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09491264 8.61010671 + layer.39.0 32.22669916 4221.09523810 + ------------------------------------------------------------------------------------- + TOTAL 16.16080590 2114.85267240 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 823316 +BPFP 0.7814 bits/point +EBPFP 0.7814 equivalent bits/point +MSE 2114.852672 +---------------------- -------------------------------------------------------- +Time: 5.243s Load: 0.070s, Pack+Encode: 2.636s, Decode+Unpack: 2.537s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2114.8527 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03676483-sculpture_3.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n03676483-sculpture_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03710193-sketch_14.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03710193-sketch_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 254,128B, BPFP=0.4824 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 401,024B, BPFP=0.7612 +⌛️ [2/4] FRONTEND: Frontend time: 2.635s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.516s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.47394152 12.53090075 + layer.39.0 335.99814747 2443.45335277 + ------------------------------------------------------------------------------------- + TOTAL 168.23604450 1227.99212676 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 655152 +BPFP 0.6218 bits/point +EBPFP 0.6218 equivalent bits/point +MSE 1227.992127 +---------------------- -------------------------------------------------------- +Time: 5.203s Load: 0.052s, Pack+Encode: 2.635s, Decode+Unpack: 2.516s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1227.9921 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03710193-sketch_14.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n03710193-sketch_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03773504-graphic_4.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03773504-graphic_4.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 194,460B, BPFP=0.3691 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 289,484B, BPFP=0.5495 +⌛️ [2/4] FRONTEND: Frontend time: 2.652s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.513s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09681199 8.73552903 + layer.39.0 18.83313593 1265.83588435 + ------------------------------------------------------------------------------------- + TOTAL 9.46497396 637.28570669 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 483944 +BPFP 0.4593 bits/point +EBPFP 0.4593 equivalent bits/point +MSE 637.285707 +---------------------- -------------------------------------------------------- +Time: 5.218s Load: 0.052s, Pack+Encode: 2.652s, Decode+Unpack: 2.513s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 637.2857 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03773504-graphic_4.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n03773504-graphic_4.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03775071-painting_1.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03775071-painting_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.074s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 312,192B, BPFP=0.5926 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 478,328B, BPFP=0.9079 +⌛️ [2/4] FRONTEND: Frontend time: 2.633s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.521s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11048905 13.17890739 + layer.39.0 386.73560496 3896.13216715 + ------------------------------------------------------------------------------------- + TOTAL 193.42304701 1954.65553727 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 790520 +BPFP 0.7502 bits/point +EBPFP 0.7502 equivalent bits/point +MSE 1954.655537 +---------------------- -------------------------------------------------------- +Time: 5.227s Load: 0.074s, Pack+Encode: 2.633s, Decode+Unpack: 2.521s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1954.6555 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03775071-painting_1.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n03775071-painting_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03888257-cartoon_30.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03888257-cartoon_30.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 305,168B, BPFP=0.5792 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 420,160B, BPFP=0.7975 +⌛️ [2/4] FRONTEND: Frontend time: 2.639s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.533s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13203045 25.50775176 + layer.39.0 375.96832483 2392.27016521 + ------------------------------------------------------------------------------------- + TOTAL 188.05017764 1208.88895849 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 725328 +BPFP 0.6884 bits/point +EBPFP 0.6884 equivalent bits/point +MSE 1208.888958 +---------------------- -------------------------------------------------------- +Time: 5.243s Load: 0.072s, Pack+Encode: 2.639s, Decode+Unpack: 2.533s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1208.8890 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03888257-cartoon_30.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n03888257-cartoon_30.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03930630-toy_2.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03930630-toy_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 212,220B, BPFP=0.4028 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 423,896B, BPFP=0.8046 +⌛️ [2/4] FRONTEND: Frontend time: 2.664s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.536s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09699417 12.58685902 + layer.39.0 46.17573949 2829.80077745 + ------------------------------------------------------------------------------------- + TOTAL 23.13636683 1421.19381824 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 636116 +BPFP 0.6037 bits/point +EBPFP 0.6037 equivalent bits/point +MSE 1421.193818 +---------------------- -------------------------------------------------------- +Time: 5.252s Load: 0.052s, Pack+Encode: 2.664s, Decode+Unpack: 2.536s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1421.1938 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03930630-toy_2.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n03930630-toy_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04086273-sticker_5.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04086273-sticker_5.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 244,412B, BPFP=0.4639 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 345,556B, BPFP=0.6559 +⌛️ [2/4] FRONTEND: Frontend time: 2.626s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.528s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10161624 8.97126875 + layer.39.0 24.98063198 1990.44873664 + ------------------------------------------------------------------------------------- + TOTAL 12.54112411 999.71000270 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 589968 +BPFP 0.5599 bits/point +EBPFP 0.5599 equivalent bits/point +MSE 999.710003 +---------------------- -------------------------------------------------------- +Time: 5.225s Load: 0.070s, Pack+Encode: 2.626s, Decode+Unpack: 2.528s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 999.7100 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04086273-sticker_5.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n04086273-sticker_5.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04118538-sculpture_0.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04118538-sculpture_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 299,052B, BPFP=0.5676 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 483,860B, BPFP=0.9184 +⌛️ [2/4] FRONTEND: Frontend time: 2.641s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.514s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09846411 8.55156326 + layer.39.0 11.87055944 2597.27137998 + ------------------------------------------------------------------------------------- + TOTAL 5.98451177 1302.91147162 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 782912 +BPFP 0.7430 bits/point +EBPFP 0.7430 equivalent bits/point +MSE 1302.911472 +---------------------- -------------------------------------------------------- +Time: 5.207s Load: 0.052s, Pack+Encode: 2.641s, Decode+Unpack: 2.514s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1302.9115 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04118538-sculpture_0.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n04118538-sculpture_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04133789-cartoon_7.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04133789-cartoon_7.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 389,796B, BPFP=0.7399 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 556,308B, BPFP=1.0559 +⌛️ [2/4] FRONTEND: Frontend time: 2.659s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.539s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13739287 74.91267310 + layer.39.0 370.52532799 4360.04227405 + ------------------------------------------------------------------------------------- + TOTAL 185.33136043 2217.47747358 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 946104 +BPFP 0.8979 bits/point +EBPFP 0.8979 equivalent bits/point +MSE 2217.477474 +---------------------- -------------------------------------------------------- +Time: 5.269s Load: 0.070s, Pack+Encode: 2.659s, Decode+Unpack: 2.539s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2217.4775 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04133789-cartoon_7.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n04133789-cartoon_7.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04141076-cartoon_14.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04141076-cartoon_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 267,748B, BPFP=0.5082 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 422,812B, BPFP=0.8025 +⌛️ [2/4] FRONTEND: Frontend time: 2.662s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.540s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11960477 25.29028676 + layer.39.0 53.25505649 2400.06025267 + ------------------------------------------------------------------------------------- + TOTAL 26.68733063 1212.67526972 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 690560 +BPFP 0.6554 bits/point +EBPFP 0.6554 equivalent bits/point +MSE 1212.675270 +---------------------- -------------------------------------------------------- +Time: 5.253s Load: 0.051s, Pack+Encode: 2.662s, Decode+Unpack: 2.540s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1212.6753 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04141076-cartoon_14.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n04141076-cartoon_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04146614-misc_4.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04146614-misc_4.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 278,636B, BPFP=0.5289 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 489,928B, BPFP=0.9299 +⌛️ [2/4] FRONTEND: Frontend time: 2.655s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.530s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10047569 12.52056855 + layer.39.0 167.29959305 4938.84742468 + ------------------------------------------------------------------------------------- + TOTAL 83.70003437 2475.68399662 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 768564 +BPFP 0.7294 bits/point +EBPFP 0.7294 equivalent bits/point +MSE 2475.683997 +---------------------- -------------------------------------------------------- +Time: 5.257s Load: 0.072s, Pack+Encode: 2.655s, Decode+Unpack: 2.530s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2475.6840 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04146614-misc_4.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n04146614-misc_4.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04147183-art_9.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04147183-art_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 276,952B, BPFP=0.5257 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 384,712B, BPFP=0.7302 +⌛️ [2/4] FRONTEND: Frontend time: 2.634s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.533s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11332939 59.00890579 + layer.39.0 22.95352360 2339.20262391 + ------------------------------------------------------------------------------------- + TOTAL 11.53342649 1199.10576485 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 661664 +BPFP 0.6279 bits/point +EBPFP 0.6279 equivalent bits/point +MSE 1199.105765 +---------------------- -------------------------------------------------------- +Time: 5.238s Load: 0.071s, Pack+Encode: 2.634s, Decode+Unpack: 2.533s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1199.1058 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04147183-art_9.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n04147183-art_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04192698-videogame_16.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04192698-videogame_16.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.073s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 297,680B, BPFP=0.5650 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 412,380B, BPFP=0.7827 +⌛️ [2/4] FRONTEND: Frontend time: 2.640s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.513s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09706018 12.72423735 + layer.39.0 404.66927843 2449.31195335 + ------------------------------------------------------------------------------------- + TOTAL 202.38316930 1231.01809535 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 710060 +BPFP 0.6739 bits/point +EBPFP 0.6739 equivalent bits/point +MSE 1231.018095 +---------------------- -------------------------------------------------------- +Time: 5.226s Load: 0.073s, Pack+Encode: 2.640s, Decode+Unpack: 2.513s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1231.0181 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04192698-videogame_16.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n04192698-videogame_16.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04254680-deviantart_17.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04254680-deviantart_17.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 255,700B, BPFP=0.4853 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 417,220B, BPFP=0.7919 +⌛️ [2/4] FRONTEND: Frontend time: 2.631s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.533s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10685510 21.40073418 + layer.39.0 151.81593173 2008.12342080 + ------------------------------------------------------------------------------------- + TOTAL 75.96139341 1014.76207749 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 672920 +BPFP 0.6386 bits/point +EBPFP 0.6386 equivalent bits/point +MSE 1014.762077 +---------------------- -------------------------------------------------------- +Time: 5.234s Load: 0.070s, Pack+Encode: 2.631s, Decode+Unpack: 2.533s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1014.7621 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04254680-deviantart_17.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n04254680-deviantart_17.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04266014-painting_13.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04266014-painting_13.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 273,396B, BPFP=0.5189 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 364,888B, BPFP=0.6926 +⌛️ [2/4] FRONTEND: Frontend time: 2.652s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.524s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09568562 13.42372183 + layer.39.0 29.62437363 2107.55903790 + ------------------------------------------------------------------------------------- + TOTAL 14.86002963 1060.49137987 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 638284 +BPFP 0.6058 bits/point +EBPFP 0.6058 equivalent bits/point +MSE 1060.491380 +---------------------- -------------------------------------------------------- +Time: 5.228s Load: 0.052s, Pack+Encode: 2.652s, Decode+Unpack: 2.524s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1060.4914 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04266014-painting_13.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n04266014-painting_13.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04310018-art_3.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04310018-art_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 326,668B, BPFP=0.6200 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 481,220B, BPFP=0.9134 +⌛️ [2/4] FRONTEND: Frontend time: 2.637s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.541s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13375617 244.66159500 + layer.39.0 75.24515610 3785.08284742 + ------------------------------------------------------------------------------------- + TOTAL 37.68945614 2014.87222121 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 807888 +BPFP 0.7667 bits/point +EBPFP 0.7667 equivalent bits/point +MSE 2014.872221 +---------------------- -------------------------------------------------------- +Time: 5.247s Load: 0.069s, Pack+Encode: 2.637s, Decode+Unpack: 2.541s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2014.8722 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04310018-art_3.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n04310018-art_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04347754-sculpture_0.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04347754-sculpture_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 392,276B, BPFP=0.7446 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 359,756B, BPFP=0.6828 +⌛️ [2/4] FRONTEND: Frontend time: 2.660s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.532s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14257451 51.35280005 + layer.39.0 394.23636419 2177.83527697 + ------------------------------------------------------------------------------------- + TOTAL 197.18946935 1114.59403851 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 752032 +BPFP 0.7137 bits/point +EBPFP 0.7137 equivalent bits/point +MSE 1114.594039 +---------------------- -------------------------------------------------------- +Time: 5.243s Load: 0.051s, Pack+Encode: 2.660s, Decode+Unpack: 2.532s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1114.5940 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04347754-sculpture_0.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n04347754-sculpture_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04409515-deviantart_1.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04409515-deviantart_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 239,980B, BPFP=0.4555 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 381,780B, BPFP=0.7246 +⌛️ [2/4] FRONTEND: Frontend time: 2.630s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.516s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09627266 0.87525292 + layer.39.0 9.33068077 2222.02405248 + ------------------------------------------------------------------------------------- + TOTAL 4.71347671 1111.44965270 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 621760 +BPFP 0.5901 bits/point +EBPFP 0.5901 equivalent bits/point +MSE 1111.449653 +---------------------- -------------------------------------------------------- +Time: 5.197s Load: 0.050s, Pack+Encode: 2.630s, Decode+Unpack: 2.516s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1111.4497 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04409515-deviantart_1.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n04409515-deviantart_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04487394-deviantart_8.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04487394-deviantart_8.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 300,276B, BPFP=0.5699 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 449,628B, BPFP=0.8534 +⌛️ [2/4] FRONTEND: Frontend time: 2.623s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.522s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09911632 1.65386008 + layer.39.0 99.63155977 2607.48809524 + ------------------------------------------------------------------------------------- + TOTAL 49.86533804 1304.57097766 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 749904 +BPFP 0.7117 bits/point +EBPFP 0.7117 equivalent bits/point +MSE 1304.570978 +---------------------- -------------------------------------------------------- +Time: 5.215s Load: 0.070s, Pack+Encode: 2.623s, Decode+Unpack: 2.522s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1304.5710 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04487394-deviantart_8.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n04487394-deviantart_8.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04522168-painting_32.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04522168-painting_32.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.056s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 253,976B, BPFP=0.4821 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 317,436B, BPFP=0.6025 +⌛️ [2/4] FRONTEND: Frontend time: 2.632s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.524s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11740584 36.85854364 + layer.39.0 10.95138066 1386.01044704 + ------------------------------------------------------------------------------------- + TOTAL 5.53439325 711.43449534 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 571412 +BPFP 0.5423 bits/point +EBPFP 0.5423 equivalent bits/point +MSE 711.434495 +---------------------- -------------------------------------------------------- +Time: 5.212s Load: 0.056s, Pack+Encode: 2.632s, Decode+Unpack: 2.524s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 711.4345 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04522168-painting_32.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n04522168-painting_32.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04591713-painting_14.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04591713-painting_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 365,508B, BPFP=0.6938 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 436,980B, BPFP=0.8294 +⌛️ [2/4] FRONTEND: Frontend time: 2.649s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.528s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11212821 13.54726695 + layer.39.0 165.22564383 2943.12633625 + ------------------------------------------------------------------------------------- + TOTAL 82.66888602 1478.33680160 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 802488 +BPFP 0.7616 bits/point +EBPFP 0.7616 equivalent bits/point +MSE 1478.336802 +---------------------- -------------------------------------------------------- +Time: 5.228s Load: 0.051s, Pack+Encode: 2.649s, Decode+Unpack: 2.528s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1478.3368 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04591713-painting_14.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n04591713-painting_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07693725-sketch_15.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07693725-sketch_15.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 319,604B, BPFP=0.6066 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 462,764B, BPFP=0.8784 +⌛️ [2/4] FRONTEND: Frontend time: 2.625s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.518s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10569874 25.51960717 + layer.39.0 214.96065658 3482.39844509 + ------------------------------------------------------------------------------------- + TOTAL 107.53317766 1753.95902613 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 782368 +BPFP 0.7425 bits/point +EBPFP 0.7425 equivalent bits/point +MSE 1753.959026 +---------------------- -------------------------------------------------------- +Time: 5.213s Load: 0.070s, Pack+Encode: 2.625s, Decode+Unpack: 2.518s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1753.9590 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07693725-sketch_15.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n07693725-sketch_15.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07695742-videogame_1.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07695742-videogame_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 371,896B, BPFP=0.7059 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 487,112B, BPFP=0.9246 +⌛️ [2/4] FRONTEND: Frontend time: 2.639s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.521s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12460778 112.55076986 + layer.39.0 438.29433916 3539.63945578 + ------------------------------------------------------------------------------------- + TOTAL 219.20947347 1826.09511282 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 859008 +BPFP 0.8152 bits/point +EBPFP 0.8152 equivalent bits/point +MSE 1826.095113 +---------------------- -------------------------------------------------------- +Time: 5.211s Load: 0.051s, Pack+Encode: 2.639s, Decode+Unpack: 2.521s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1826.0951 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07695742-videogame_1.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n07695742-videogame_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697313-deviantart_7.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697313-deviantart_7.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 255,124B, BPFP=0.4842 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 522,280B, BPFP=0.9913 +⌛️ [2/4] FRONTEND: Frontend time: 2.653s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.512s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09520741 8.58466389 + layer.39.0 14.69109212 5177.89261419 + ------------------------------------------------------------------------------------- + TOTAL 7.39314977 2593.23863904 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 777404 +BPFP 0.7378 bits/point +EBPFP 0.7378 equivalent bits/point +MSE 2593.238639 +---------------------- -------------------------------------------------------- +Time: 5.235s Load: 0.069s, Pack+Encode: 2.653s, Decode+Unpack: 2.512s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2593.2386 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697313-deviantart_7.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n07697313-deviantart_7.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697537-deviantart_16.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697537-deviantart_16.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 293,724B, BPFP=0.5575 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 488,460B, BPFP=0.9271 +⌛️ [2/4] FRONTEND: Frontend time: 2.640s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.525s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09755328 12.39423989 + layer.39.0 90.32537658 2669.81413994 + ------------------------------------------------------------------------------------- + TOTAL 45.21146493 1341.10418992 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 782184 +BPFP 0.7423 bits/point +EBPFP 0.7423 equivalent bits/point +MSE 1341.104190 +---------------------- -------------------------------------------------------- +Time: 5.236s Load: 0.071s, Pack+Encode: 2.640s, Decode+Unpack: 2.525s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1341.1042 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697537-deviantart_16.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n07697537-deviantart_16.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714571-deviantart_0.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714571-deviantart_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 282,840B, BPFP=0.5369 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 600,084B, BPFP=1.1390 +⌛️ [2/4] FRONTEND: Frontend time: 2.636s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.529s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09528512 8.48054657 + layer.39.0 45.81401467 4495.55296404 + ------------------------------------------------------------------------------------- + TOTAL 22.95464989 2252.01675531 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 882924 +BPFP 0.8379 bits/point +EBPFP 0.8379 equivalent bits/point +MSE 2252.016755 +---------------------- -------------------------------------------------------- +Time: 5.235s Load: 0.070s, Pack+Encode: 2.636s, Decode+Unpack: 2.529s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2252.0168 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714571-deviantart_0.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n07714571-deviantart_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714990-toy_14.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714990-toy_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 283,644B, BPFP=0.5384 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 611,492B, BPFP=1.1607 +⌛️ [2/4] FRONTEND: Frontend time: 2.643s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.525s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09793257 0.84906302 + layer.39.0 322.50334062 3712.45699708 + ------------------------------------------------------------------------------------- + TOTAL 161.30063660 1856.65303005 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 895136 +BPFP 0.8495 bits/point +EBPFP 0.8495 equivalent bits/point +MSE 1856.653030 +---------------------- -------------------------------------------------------- +Time: 5.227s Load: 0.059s, Pack+Encode: 2.643s, Decode+Unpack: 2.525s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1856.6530 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714990-toy_14.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n07714990-toy_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07718472-cartoon_1.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07718472-cartoon_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 231,396B, BPFP=0.4392 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 344,040B, BPFP=0.6530 +⌛️ [2/4] FRONTEND: Frontend time: 2.643s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.528s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11235230 111.63291879 + layer.39.0 14.49942963 2066.29203110 + ------------------------------------------------------------------------------------- + TOTAL 7.30589096 1088.96247495 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 575436 +BPFP 0.5461 bits/point +EBPFP 0.5461 equivalent bits/point +MSE 1088.962475 +---------------------- -------------------------------------------------------- +Time: 5.241s Load: 0.069s, Pack+Encode: 2.643s, Decode+Unpack: 2.528s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1088.9625 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07718472-cartoon_1.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n07718472-cartoon_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07742313-deviantart_8.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07742313-deviantart_8.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 197,772B, BPFP=0.3754 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 351,368B, BPFP=0.6669 +⌛️ [2/4] FRONTEND: Frontend time: 2.642s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.529s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09669835 0.82446959 + layer.39.0 8.77690150 2822.59645287 + ------------------------------------------------------------------------------------- + TOTAL 4.43679992 1411.71046123 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 549140 +BPFP 0.5212 bits/point +EBPFP 0.5212 equivalent bits/point +MSE 1411.710461 +---------------------- -------------------------------------------------------- +Time: 5.242s Load: 0.071s, Pack+Encode: 2.642s, Decode+Unpack: 2.529s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1411.7105 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07742313-deviantart_8.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n07742313-deviantart_8.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07749582-sticker_0.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07749582-sticker_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 307,096B, BPFP=0.5829 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 538,776B, BPFP=1.0226 +⌛️ [2/4] FRONTEND: Frontend time: 2.647s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.550s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09550123 12.78374503 + layer.39.0 34.64631545 4032.78255588 + ------------------------------------------------------------------------------------- + TOTAL 17.37090834 2022.78315045 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 845872 +BPFP 0.8028 bits/point +EBPFP 0.8028 equivalent bits/point +MSE 2022.783150 +---------------------- -------------------------------------------------------- +Time: 5.268s Load: 0.071s, Pack+Encode: 2.647s, Decode+Unpack: 2.550s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2022.7832 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07749582-sticker_0.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n07749582-sticker_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07753275-painting_2.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07753275-painting_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 428,336B, BPFP=0.8130 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 689,292B, BPFP=1.3083 +⌛️ [2/4] FRONTEND: Frontend time: 2.672s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.556s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10429548 339.55584913 + layer.39.0 540.43106171 5125.92176871 + ------------------------------------------------------------------------------------- + TOTAL 270.26767859 2732.73880892 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1117628 +BPFP 1.0607 bits/point +EBPFP 1.0607 equivalent bits/point +MSE 2732.738809 +---------------------- -------------------------------------------------------- +Time: 5.279s Load: 0.050s, Pack+Encode: 2.672s, Decode+Unpack: 2.556s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2732.7388 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07753275-painting_2.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n07753275-painting_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07768694-painting_12.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07768694-painting_12.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 339,244B, BPFP=0.6439 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 562,904B, BPFP=1.0684 +⌛️ [2/4] FRONTEND: Frontend time: 2.669s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.522s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09821300 25.68315719 + layer.39.0 635.68343052 5585.11564626 + ------------------------------------------------------------------------------------- + TOTAL 317.89082176 2805.39940172 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 902148 +BPFP 0.8562 bits/point +EBPFP 0.8562 equivalent bits/point +MSE 2805.399402 +---------------------- -------------------------------------------------------- +Time: 5.262s Load: 0.071s, Pack+Encode: 2.669s, Decode+Unpack: 2.522s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2805.3994 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07768694-painting_12.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n07768694-painting_12.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07920052-painting_1.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07920052-painting_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 293,336B, BPFP=0.5568 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 509,760B, BPFP=0.9676 +⌛️ [2/4] FRONTEND: Frontend time: 2.657s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.535s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09582097 20.90696368 + layer.39.0 9.59182155 3594.61175899 + ------------------------------------------------------------------------------------- + TOTAL 4.84382126 1807.75936133 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 803096 +BPFP 0.7622 bits/point +EBPFP 0.7622 equivalent bits/point +MSE 1807.759361 +---------------------- -------------------------------------------------------- +Time: 5.262s Load: 0.070s, Pack+Encode: 2.657s, Decode+Unpack: 2.535s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1807.7594 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07920052-painting_1.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n07920052-painting_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09472597-painting_15.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09472597-painting_15.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 214,768B, BPFP=0.4076 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 332,452B, BPFP=0.6310 +⌛️ [2/4] FRONTEND: Frontend time: 2.646s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.519s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09164813 0.84956738 + layer.39.0 9.11265014 2808.68658892 + ------------------------------------------------------------------------------------- + TOTAL 4.60214913 1404.76807815 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 547220 +BPFP 0.5193 bits/point +EBPFP 0.5193 equivalent bits/point +MSE 1404.768078 +---------------------- -------------------------------------------------------- +Time: 5.236s Load: 0.070s, Pack+Encode: 2.646s, Decode+Unpack: 2.519s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1404.7681 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09472597-painting_15.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n09472597-painting_15.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09835506-videogame_3.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09835506-videogame_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 269,324B, BPFP=0.5112 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 523,944B, BPFP=0.9945 +⌛️ [2/4] FRONTEND: Frontend time: 2.644s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.537s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09585661 0.84807893 + layer.39.0 12.34450164 3291.75534500 + ------------------------------------------------------------------------------------- + TOTAL 6.22017912 1646.30171196 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 793268 +BPFP 0.7528 bits/point +EBPFP 0.7528 equivalent bits/point +MSE 1646.301712 +---------------------- -------------------------------------------------------- +Time: 5.253s Load: 0.071s, Pack+Encode: 2.644s, Decode+Unpack: 2.537s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1646.3017 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09835506-videogame_3.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n09835506-videogame_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n12267677-misc_105.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n12267677-misc_105.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 235,924B, BPFP=0.4478 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 395,368B, BPFP=0.7504 +⌛️ [2/4] FRONTEND: Frontend time: 2.651s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.538s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10166193 12.35957904 + layer.39.0 219.41089650 2246.98493683 + ------------------------------------------------------------------------------------- + TOTAL 109.75627921 1129.67225794 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 631292 +BPFP 0.5991 bits/point +EBPFP 0.5991 equivalent bits/point +MSE 1129.672258 +---------------------- -------------------------------------------------------- +Time: 5.259s Load: 0.070s, Pack+Encode: 2.651s, Decode+Unpack: 2.538s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1129.6723 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n12267677-misc_105.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kr/n12267677-misc_105.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 0.7135 bits/point +Avg EBPFP 0.7135 equivalent bits/point +Avg MSE 1593.769090 +Avg Time 5.233s +------------------------ ---------------------------- diff --git a/lambda0.01/elic-featurecoding-8bit-individual/cls_in1kval/dtufc_elic-featurecoding_dinov3-total_individual.log b/lambda0.01/elic-featurecoding-8bit-individual/cls_in1kval/dtufc_elic-featurecoding_dinov3-total_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..8c3e99018fd6c740bfb2480609d61c71c0ae2e12 --- /dev/null +++ b/lambda0.01/elic-featurecoding-8bit-individual/cls_in1kval/dtufc_elic-featurecoding_dinov3-total_individual.log @@ -0,0 +1,9344 @@ +Experiment: dtufc_elic-featurecoding_dinov3-total_individual +Log file: output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/dtufc_elic-featurecoding_dinov3-total_individual.log +DTUFCCodecConfig: + arch: elic-featurecoding + handler: dinov3-total + checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.01_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.01_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 506 +Loaded elic-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.9' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.19' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.29' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.39' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json +Loaded per-key mappings: model=dinov3-total + Keys: ['layer.9', 'layer.19', 'layer.29', 'layer.39'] +---------------- ------------------------------------------------------------------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +Checkpoint codec_weights/elic_hybrid/elic2022-official_lambda0.01_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-DINOv3/imagenet1k-val +Output output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval +---------------- ------------------------------------------------------------------------------------------------------------------- +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02825657-ILSVRC2012_val_00001103.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02825657-ILSVRC2012_val_00001103.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 246,220B, BPFP=0.4673 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 337,808B, BPFP=0.6412 +⌛️ [2/4] FRONTEND: Frontend time: 2.966s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.567s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10264289 12.42516248 + layer.39.0 9.47367932 1424.58138970 + ------------------------------------------------------------------------------------- + TOTAL 4.78816110 718.50327609 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 584028 +BPFP 0.5543 bits/point +EBPFP 0.5543 equivalent bits/point +MSE 718.503276 +---------------------- -------------------------------------------------------- +Time: 5.606s Load: 0.072s, Pack+Encode: 2.966s, Decode+Unpack: 2.567s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 718.5033 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02825657-ILSVRC2012_val_00001103.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n02825657-ILSVRC2012_val_00001103.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02834397-ILSVRC2012_val_00001252.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02834397-ILSVRC2012_val_00001252.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.067s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 398,692B, BPFP=0.7567 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 532,016B, BPFP=1.0098 +⌛️ [2/4] FRONTEND: Frontend time: 2.637s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.546s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14789204 478.44970845 + layer.39.0 415.43227648 3810.32507289 + ------------------------------------------------------------------------------------- + TOTAL 207.79008426 2144.38739067 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 930708 +BPFP 0.8833 bits/point +EBPFP 0.8833 equivalent bits/point +MSE 2144.387391 +---------------------- -------------------------------------------------------- +Time: 5.250s Load: 0.067s, Pack+Encode: 2.637s, Decode+Unpack: 2.546s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2144.3874 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02834397-ILSVRC2012_val_00001252.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n02834397-ILSVRC2012_val_00001252.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02840245-ILSVRC2012_val_00003446.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02840245-ILSVRC2012_val_00003446.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 235,220B, BPFP=0.4465 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 382,152B, BPFP=0.7254 +⌛️ [2/4] FRONTEND: Frontend time: 2.611s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.501s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10761288 12.63552391 + layer.39.0 28.71820525 1786.46209913 + ------------------------------------------------------------------------------------- + TOTAL 14.41290906 899.54881152 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 617372 +BPFP 0.5859 bits/point +EBPFP 0.5859 equivalent bits/point +MSE 899.548812 +---------------------- -------------------------------------------------------- +Time: 5.184s Load: 0.071s, Pack+Encode: 2.611s, Decode+Unpack: 2.501s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 899.5488 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02840245-ILSVRC2012_val_00003446.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n02840245-ILSVRC2012_val_00003446.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02843684-ILSVRC2012_val_00000514.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02843684-ILSVRC2012_val_00000514.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.049s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 287,896B, BPFP=0.5464 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 429,176B, BPFP=0.8146 +⌛️ [2/4] FRONTEND: Frontend time: 2.619s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.500s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11482661 50.15438229 + layer.39.0 84.54469600 2502.16083576 + ------------------------------------------------------------------------------------- + TOTAL 42.32976130 1276.15760903 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 717072 +BPFP 0.6805 bits/point +EBPFP 0.6805 equivalent bits/point +MSE 1276.157609 +---------------------- -------------------------------------------------------- +Time: 5.169s Load: 0.049s, Pack+Encode: 2.619s, Decode+Unpack: 2.500s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1276.1576 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02843684-ILSVRC2012_val_00000514.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n02843684-ILSVRC2012_val_00000514.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02859443-ILSVRC2012_val_00000193.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02859443-ILSVRC2012_val_00000193.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 190,632B, BPFP=0.3618 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 244,372B, BPFP=0.4638 +⌛️ [2/4] FRONTEND: Frontend time: 2.584s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.492s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11417333 36.88748937 + layer.39.0 9.67809406 1356.81887755 + ------------------------------------------------------------------------------------- + TOTAL 4.89613370 696.85318346 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 435004 +BPFP 0.4128 bits/point +EBPFP 0.4128 equivalent bits/point +MSE 696.853183 +---------------------- -------------------------------------------------------- +Time: 5.127s Load: 0.051s, Pack+Encode: 2.584s, Decode+Unpack: 2.492s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 696.8532 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02859443-ILSVRC2012_val_00000193.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n02859443-ILSVRC2012_val_00000193.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02860847-ILSVRC2012_val_00000601.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02860847-ILSVRC2012_val_00000601.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.079s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 342,052B, BPFP=0.6492 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 478,052B, BPFP=0.9074 +⌛️ [2/4] FRONTEND: Frontend time: 2.628s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.514s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12653054 237.19861516 + layer.39.0 266.35249636 3065.59572400 + ------------------------------------------------------------------------------------- + TOTAL 133.23951345 1651.39716958 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 820104 +BPFP 0.7783 bits/point +EBPFP 0.7783 equivalent bits/point +MSE 1651.397170 +---------------------- -------------------------------------------------------- +Time: 5.222s Load: 0.079s, Pack+Encode: 2.628s, Decode+Unpack: 2.514s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1651.3972 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02860847-ILSVRC2012_val_00000601.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n02860847-ILSVRC2012_val_00000601.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02865351-ILSVRC2012_val_00000763.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02865351-ILSVRC2012_val_00000763.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 230,812B, BPFP=0.4381 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 436,884B, BPFP=0.8292 +⌛️ [2/4] FRONTEND: Frontend time: 2.624s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.517s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09467571 12.49041659 + layer.39.0 15.47581086 2674.85204082 + ------------------------------------------------------------------------------------- + TOTAL 7.78524328 1343.67122870 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 667696 +BPFP 0.6337 bits/point +EBPFP 0.6337 equivalent bits/point +MSE 1343.671229 +---------------------- -------------------------------------------------------- +Time: 5.191s Load: 0.050s, Pack+Encode: 2.624s, Decode+Unpack: 2.517s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1343.6712 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02865351-ILSVRC2012_val_00000763.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n02865351-ILSVRC2012_val_00000763.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02869837-ILSVRC2012_val_00000906.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02869837-ILSVRC2012_val_00000906.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 319,208B, BPFP=0.6059 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 523,140B, BPFP=0.9930 +⌛️ [2/4] FRONTEND: Frontend time: 2.630s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.495s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09659988 8.62361345 + layer.39.0 16.39405483 2633.98931001 + ------------------------------------------------------------------------------------- + TOTAL 8.24532736 1321.30646173 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 842348 +BPFP 0.7994 bits/point +EBPFP 0.7994 equivalent bits/point +MSE 1321.306462 +---------------------- -------------------------------------------------------- +Time: 5.185s Load: 0.060s, Pack+Encode: 2.630s, Decode+Unpack: 2.495s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1321.3065 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02869837-ILSVRC2012_val_00000906.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n02869837-ILSVRC2012_val_00000906.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02870880-ILSVRC2012_val_00003274.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02870880-ILSVRC2012_val_00003274.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 361,764B, BPFP=0.6867 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 542,812B, BPFP=1.0303 +⌛️ [2/4] FRONTEND: Frontend time: 2.620s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.512s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10254154 26.69360423 + layer.39.0 9.36513093 2768.51530612 + ------------------------------------------------------------------------------------- + TOTAL 4.73383623 1397.60445517 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 904576 +BPFP 0.8585 bits/point +EBPFP 0.8585 equivalent bits/point +MSE 1397.604455 +---------------------- -------------------------------------------------------- +Time: 5.193s Load: 0.061s, Pack+Encode: 2.620s, Decode+Unpack: 2.512s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1397.6045 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02870880-ILSVRC2012_val_00003274.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n02870880-ILSVRC2012_val_00003274.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02871525-ILSVRC2012_val_00000879.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02871525-ILSVRC2012_val_00000879.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 398,740B, BPFP=0.7568 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 476,728B, BPFP=0.9049 +⌛️ [2/4] FRONTEND: Frontend time: 2.620s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.509s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.17072899 321.89841472 + layer.39.0 20.29403547 2674.59329446 + ------------------------------------------------------------------------------------- + TOTAL 10.23238223 1498.24585459 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 875468 +BPFP 0.8309 bits/point +EBPFP 0.8309 equivalent bits/point +MSE 1498.245855 +---------------------- -------------------------------------------------------- +Time: 5.180s Load: 0.051s, Pack+Encode: 2.620s, Decode+Unpack: 2.509s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1498.2459 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02871525-ILSVRC2012_val_00000879.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n02871525-ILSVRC2012_val_00000879.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02877765-ILSVRC2012_val_00000634.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02877765-ILSVRC2012_val_00000634.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 283,104B, BPFP=0.5374 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 547,864B, BPFP=1.0399 +⌛️ [2/4] FRONTEND: Frontend time: 2.625s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.512s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10908128 12.46815021 + layer.39.0 364.97770894 4242.45821186 + ------------------------------------------------------------------------------------- + TOTAL 182.54339511 2127.46318103 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 830968 +BPFP 0.7886 bits/point +EBPFP 0.7886 equivalent bits/point +MSE 2127.463181 +---------------------- -------------------------------------------------------- +Time: 5.187s Load: 0.050s, Pack+Encode: 2.625s, Decode+Unpack: 2.512s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2127.4632 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02877765-ILSVRC2012_val_00000634.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n02877765-ILSVRC2012_val_00000634.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02879718-ILSVRC2012_val_00001354.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02879718-ILSVRC2012_val_00001354.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 340,252B, BPFP=0.6458 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 510,656B, BPFP=0.9693 +⌛️ [2/4] FRONTEND: Frontend time: 2.602s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.510s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10948122 0.91356414 + layer.39.0 55.92460444 3485.86224490 + ------------------------------------------------------------------------------------- + TOTAL 28.01704283 1743.38790452 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 850908 +BPFP 0.8075 bits/point +EBPFP 0.8075 equivalent bits/point +MSE 1743.387905 +---------------------- -------------------------------------------------------- +Time: 5.184s Load: 0.072s, Pack+Encode: 2.602s, Decode+Unpack: 2.510s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1743.3879 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02879718-ILSVRC2012_val_00001354.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n02879718-ILSVRC2012_val_00001354.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02883205-ILSVRC2012_val_00000126.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02883205-ILSVRC2012_val_00000126.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 197,572B, BPFP=0.3750 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 366,904B, BPFP=0.6964 +⌛️ [2/4] FRONTEND: Frontend time: 2.605s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.495s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.06711708 12.44522993 + layer.39.0 7.82069686 1360.81086006 + ------------------------------------------------------------------------------------- + TOTAL 7.94390697 686.62804500 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 564476 +BPFP 0.5357 bits/point +EBPFP 0.5357 equivalent bits/point +MSE 686.628045 +---------------------- -------------------------------------------------------- +Time: 5.170s Load: 0.070s, Pack+Encode: 2.605s, Decode+Unpack: 2.495s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 686.6280 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02883205-ILSVRC2012_val_00000126.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n02883205-ILSVRC2012_val_00000126.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892201-ILSVRC2012_val_00001145.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892201-ILSVRC2012_val_00001145.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 370,764B, BPFP=0.7037 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 460,260B, BPFP=0.8736 +⌛️ [2/4] FRONTEND: Frontend time: 2.637s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.525s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11297333 38.17496508 + layer.39.0 15.09638643 2536.09572400 + ------------------------------------------------------------------------------------- + TOTAL 7.60467988 1287.13534454 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 831024 +BPFP 0.7887 bits/point +EBPFP 0.7887 equivalent bits/point +MSE 1287.135345 +---------------------- -------------------------------------------------------- +Time: 5.232s Load: 0.070s, Pack+Encode: 2.637s, Decode+Unpack: 2.525s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1287.1353 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892201-ILSVRC2012_val_00001145.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n02892201-ILSVRC2012_val_00001145.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892767-ILSVRC2012_val_00000808.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892767-ILSVRC2012_val_00000808.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 276,816B, BPFP=0.5254 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 571,732B, BPFP=1.0852 +⌛️ [2/4] FRONTEND: Frontend time: 2.617s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.527s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09598007 12.47064049 + layer.39.0 31.15013059 3104.16010690 + ------------------------------------------------------------------------------------- + TOTAL 15.62305533 1558.31537369 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 848548 +BPFP 0.8053 bits/point +EBPFP 0.8053 equivalent bits/point +MSE 1558.315374 +---------------------- -------------------------------------------------------- +Time: 5.206s Load: 0.061s, Pack+Encode: 2.617s, Decode+Unpack: 2.527s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1558.3154 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892767-ILSVRC2012_val_00000808.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n02892767-ILSVRC2012_val_00000808.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02895154-ILSVRC2012_val_00000080.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02895154-ILSVRC2012_val_00000080.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 293,452B, BPFP=0.5570 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 674,864B, BPFP=1.2809 +⌛️ [2/4] FRONTEND: Frontend time: 2.626s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.528s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09530723 1.61776950 + layer.39.0 971.40427600 6420.87560739 + ------------------------------------------------------------------------------------- + TOTAL 485.74979162 3211.24668844 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 968316 +BPFP 0.9190 bits/point +EBPFP 0.9190 equivalent bits/point +MSE 3211.246688 +---------------------- -------------------------------------------------------- +Time: 5.225s Load: 0.070s, Pack+Encode: 2.626s, Decode+Unpack: 2.528s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3211.2467 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02895154-ILSVRC2012_val_00000080.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n02895154-ILSVRC2012_val_00000080.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02906734-ILSVRC2012_val_00002937.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02906734-ILSVRC2012_val_00002937.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 310,844B, BPFP=0.5900 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 388,752B, BPFP=0.7379 +⌛️ [2/4] FRONTEND: Frontend time: 2.612s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.496s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09767962 0.85201685 + layer.39.0 32.09536716 2442.19266278 + ------------------------------------------------------------------------------------- + TOTAL 16.09652339 1221.52233982 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 699596 +BPFP 0.6639 bits/point +EBPFP 0.6639 equivalent bits/point +MSE 1221.522340 +---------------------- -------------------------------------------------------- +Time: 5.158s Load: 0.051s, Pack+Encode: 2.612s, Decode+Unpack: 2.496s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1221.5223 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02906734-ILSVRC2012_val_00002937.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n02906734-ILSVRC2012_val_00002937.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02910353-ILSVRC2012_val_00000558.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02910353-ILSVRC2012_val_00000558.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 276,176B, BPFP=0.5242 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 499,136B, BPFP=0.9474 +⌛️ [2/4] FRONTEND: Frontend time: 2.628s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.516s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11017090 181.68302053 + layer.39.0 483.40066205 3906.67517007 + ------------------------------------------------------------------------------------- + TOTAL 241.75541648 2044.17909530 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 775312 +BPFP 0.7358 bits/point +EBPFP 0.7358 equivalent bits/point +MSE 2044.179095 +---------------------- -------------------------------------------------------- +Time: 5.206s Load: 0.062s, Pack+Encode: 2.628s, Decode+Unpack: 2.516s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2044.1791 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02910353-ILSVRC2012_val_00000558.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n02910353-ILSVRC2012_val_00000558.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02916936-ILSVRC2012_val_00000366.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02916936-ILSVRC2012_val_00000366.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 221,872B, BPFP=0.4211 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 497,808B, BPFP=0.9449 +⌛️ [2/4] FRONTEND: Frontend time: 2.622s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.511s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10657579 12.56567929 + layer.39.0 435.18944363 3282.65986395 + ------------------------------------------------------------------------------------- + TOTAL 217.64800971 1647.61277162 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 719680 +BPFP 0.6830 bits/point +EBPFP 0.6830 equivalent bits/point +MSE 1647.612772 +---------------------- -------------------------------------------------------- +Time: 5.183s Load: 0.050s, Pack+Encode: 2.622s, Decode+Unpack: 2.511s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1647.6128 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02916936-ILSVRC2012_val_00000366.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n02916936-ILSVRC2012_val_00000366.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02917067-ILSVRC2012_val_00000562.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02917067-ILSVRC2012_val_00000562.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 350,636B, BPFP=0.6655 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 580,356B, BPFP=1.1016 +⌛️ [2/4] FRONTEND: Frontend time: 2.649s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.535s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10760244 8.95740707 + layer.39.0 37.55795979 4376.60738581 + ------------------------------------------------------------------------------------- + TOTAL 18.83278111 2192.78239644 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 930992 +BPFP 0.8835 bits/point +EBPFP 0.8835 equivalent bits/point +MSE 2192.782396 +---------------------- -------------------------------------------------------- +Time: 5.256s Load: 0.071s, Pack+Encode: 2.649s, Decode+Unpack: 2.535s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2192.7824 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02917067-ILSVRC2012_val_00000562.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n02917067-ILSVRC2012_val_00000562.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02930766-ILSVRC2012_val_00000056.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02930766-ILSVRC2012_val_00000056.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 324,316B, BPFP=0.6156 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 565,184B, BPFP=1.0728 +⌛️ [2/4] FRONTEND: Frontend time: 2.638s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.521s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10591127 12.45832954 + layer.39.0 18.32421875 4263.43488824 + ------------------------------------------------------------------------------------- + TOTAL 9.21506501 2137.94660889 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 889500 +BPFP 0.8442 bits/point +EBPFP 0.8442 equivalent bits/point +MSE 2137.946609 +---------------------- -------------------------------------------------------- +Time: 5.230s Load: 0.071s, Pack+Encode: 2.638s, Decode+Unpack: 2.521s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2137.9466 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02930766-ILSVRC2012_val_00000056.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n02930766-ILSVRC2012_val_00000056.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02939185-ILSVRC2012_val_00000302.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02939185-ILSVRC2012_val_00000302.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 304,884B, BPFP=0.5787 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 473,628B, BPFP=0.8990 +⌛️ [2/4] FRONTEND: Frontend time: 2.625s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.528s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09694758 0.87155285 + layer.39.0 25.52453269 5698.40233236 + ------------------------------------------------------------------------------------- + TOTAL 12.81074014 2849.63694260 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 778512 +BPFP 0.7388 bits/point +EBPFP 0.7388 equivalent bits/point +MSE 2849.636943 +---------------------- -------------------------------------------------------- +Time: 5.204s Load: 0.051s, Pack+Encode: 2.625s, Decode+Unpack: 2.528s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2849.6369 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02939185-ILSVRC2012_val_00000302.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n02939185-ILSVRC2012_val_00000302.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02950826-ILSVRC2012_val_00000392.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02950826-ILSVRC2012_val_00000392.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 313,836B, BPFP=0.5957 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 484,932B, BPFP=0.9204 +⌛️ [2/4] FRONTEND: Frontend time: 2.620s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.517s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10873010 50.74084366 + layer.39.0 707.96944849 3338.72740525 + ------------------------------------------------------------------------------------- + TOTAL 354.03908930 1694.73412445 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 798768 +BPFP 0.7581 bits/point +EBPFP 0.7581 equivalent bits/point +MSE 1694.734124 +---------------------- -------------------------------------------------------- +Time: 5.198s Load: 0.061s, Pack+Encode: 2.620s, Decode+Unpack: 2.517s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1694.7341 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02950826-ILSVRC2012_val_00000392.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n02950826-ILSVRC2012_val_00000392.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951358-ILSVRC2012_val_00000347.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951358-ILSVRC2012_val_00000347.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 300,100B, BPFP=0.5696 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 398,396B, BPFP=0.7562 +⌛️ [2/4] FRONTEND: Frontend time: 2.621s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.512s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12200860 20.83884346 + layer.39.0 237.66299198 2282.38654033 + ------------------------------------------------------------------------------------- + TOTAL 118.89250029 1151.61269190 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 698496 +BPFP 0.6629 bits/point +EBPFP 0.6629 equivalent bits/point +MSE 1151.612692 +---------------------- -------------------------------------------------------- +Time: 5.185s Load: 0.052s, Pack+Encode: 2.621s, Decode+Unpack: 2.512s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1151.6127 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951358-ILSVRC2012_val_00000347.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n02951358-ILSVRC2012_val_00000347.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951585-ILSVRC2012_val_00000101.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951585-ILSVRC2012_val_00000101.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 170,124B, BPFP=0.3229 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 434,700B, BPFP=0.8251 +⌛️ [2/4] FRONTEND: Frontend time: 2.615s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.506s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.07385432 1.25798570 + layer.39.0 181.90962099 2691.00315841 + ------------------------------------------------------------------------------------- + TOTAL 94.99173765 1346.13057205 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 604824 +BPFP 0.5740 bits/point +EBPFP 0.5740 equivalent bits/point +MSE 1346.130572 +---------------------- -------------------------------------------------------- +Time: 5.183s Load: 0.061s, Pack+Encode: 2.615s, Decode+Unpack: 2.506s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1346.1306 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951585-ILSVRC2012_val_00000101.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n02951585-ILSVRC2012_val_00000101.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02963159-ILSVRC2012_val_00000061.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02963159-ILSVRC2012_val_00000061.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 302,088B, BPFP=0.5734 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 427,816B, BPFP=0.8120 +⌛️ [2/4] FRONTEND: Frontend time: 2.608s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.524s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11232698 37.10571170 + layer.39.0 24.77479842 2260.71404276 + ------------------------------------------------------------------------------------- + TOTAL 12.44356270 1148.90987723 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 729904 +BPFP 0.6927 bits/point +EBPFP 0.6927 equivalent bits/point +MSE 1148.909877 +---------------------- -------------------------------------------------------- +Time: 5.183s Load: 0.052s, Pack+Encode: 2.608s, Decode+Unpack: 2.524s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1148.9099 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02963159-ILSVRC2012_val_00000061.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n02963159-ILSVRC2012_val_00000061.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02965783-ILSVRC2012_val_00000213.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02965783-ILSVRC2012_val_00000213.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 257,720B, BPFP=0.4892 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 504,452B, BPFP=0.9575 +⌛️ [2/4] FRONTEND: Frontend time: 2.621s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.510s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09516161 12.43639247 + layer.39.0 223.32294704 3183.85374150 + ------------------------------------------------------------------------------------- + TOTAL 111.70905432 1598.14506698 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 762172 +BPFP 0.7233 bits/point +EBPFP 0.7233 equivalent bits/point +MSE 1598.145067 +---------------------- -------------------------------------------------------- +Time: 5.182s Load: 0.051s, Pack+Encode: 2.621s, Decode+Unpack: 2.510s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1598.1451 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02965783-ILSVRC2012_val_00000213.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n02965783-ILSVRC2012_val_00000213.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966193-ILSVRC2012_val_00000074.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966193-ILSVRC2012_val_00000074.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 384,004B, BPFP=0.7289 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 717,024B, BPFP=1.3610 +⌛️ [2/4] FRONTEND: Frontend time: 2.624s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.537s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12190965 64.57634080 + layer.39.0 378.75431244 4050.50655977 + ------------------------------------------------------------------------------------- + TOTAL 189.43811104 2057.54145029 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1101028 +BPFP 1.0449 bits/point +EBPFP 1.0449 equivalent bits/point +MSE 2057.541450 +---------------------- -------------------------------------------------------- +Time: 5.213s Load: 0.052s, Pack+Encode: 2.624s, Decode+Unpack: 2.537s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2057.5415 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966193-ILSVRC2012_val_00000074.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n02966193-ILSVRC2012_val_00000074.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966687-ILSVRC2012_val_00001041.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966687-ILSVRC2012_val_00001041.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.053s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 299,348B, BPFP=0.5682 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 488,904B, BPFP=0.9280 +⌛️ [2/4] FRONTEND: Frontend time: 2.620s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.513s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12487827 38.32763909 + layer.39.0 254.07423773 3221.33406220 + ------------------------------------------------------------------------------------- + TOTAL 127.09955800 1629.83085064 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 788252 +BPFP 0.7481 bits/point +EBPFP 0.7481 equivalent bits/point +MSE 1629.830851 +---------------------- -------------------------------------------------------- +Time: 5.186s Load: 0.053s, Pack+Encode: 2.620s, Decode+Unpack: 2.513s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1629.8309 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966687-ILSVRC2012_val_00001041.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n02966687-ILSVRC2012_val_00001041.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02971356-ILSVRC2012_val_00000019.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02971356-ILSVRC2012_val_00000019.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 222,300B, BPFP=0.4219 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 327,720B, BPFP=0.6220 +⌛️ [2/4] FRONTEND: Frontend time: 2.604s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.515s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09754465 12.56989587 + layer.39.0 24.51746044 1685.14747328 + ------------------------------------------------------------------------------------- + TOTAL 12.30750255 848.85868457 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 550020 +BPFP 0.5220 bits/point +EBPFP 0.5220 equivalent bits/point +MSE 848.858685 +---------------------- -------------------------------------------------------- +Time: 5.181s Load: 0.061s, Pack+Encode: 2.604s, Decode+Unpack: 2.515s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 848.8587 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02971356-ILSVRC2012_val_00000019.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n02971356-ILSVRC2012_val_00000019.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02978881-ILSVRC2012_val_00000353.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02978881-ILSVRC2012_val_00000353.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 303,760B, BPFP=0.5766 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 507,424B, BPFP=0.9631 +⌛️ [2/4] FRONTEND: Frontend time: 2.629s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.525s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09975241 8.39648058 + layer.39.0 226.62124939 2715.20359572 + ------------------------------------------------------------------------------------- + TOTAL 113.36050090 1361.80003815 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 811184 +BPFP 0.7698 bits/point +EBPFP 0.7698 equivalent bits/point +MSE 1361.800038 +---------------------- -------------------------------------------------------- +Time: 5.223s Load: 0.069s, Pack+Encode: 2.629s, Decode+Unpack: 2.525s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1361.8000 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02978881-ILSVRC2012_val_00000353.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n02978881-ILSVRC2012_val_00000353.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02980441-ILSVRC2012_val_00000122.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02980441-ILSVRC2012_val_00000122.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 268,856B, BPFP=0.5103 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 324,060B, BPFP=0.6151 +⌛️ [2/4] FRONTEND: Frontend time: 2.601s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.506s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10186533 12.23081800 + layer.39.0 8.25151846 1196.07325073 + ------------------------------------------------------------------------------------- + TOTAL 4.17669190 604.15203436 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 592916 +BPFP 0.5627 bits/point +EBPFP 0.5627 equivalent bits/point +MSE 604.152034 +---------------------- -------------------------------------------------------- +Time: 5.176s Load: 0.069s, Pack+Encode: 2.601s, Decode+Unpack: 2.506s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 604.1520 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02980441-ILSVRC2012_val_00000122.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n02980441-ILSVRC2012_val_00000122.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02988304-ILSVRC2012_val_00003491.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02988304-ILSVRC2012_val_00003491.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 267,524B, BPFP=0.5078 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 501,428B, BPFP=0.9518 +⌛️ [2/4] FRONTEND: Frontend time: 2.621s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.499s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10176498 12.35324610 + layer.39.0 516.16180758 4229.26822157 + ------------------------------------------------------------------------------------- + TOTAL 258.13178628 2120.81073384 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 768952 +BPFP 0.7298 bits/point +EBPFP 0.7298 equivalent bits/point +MSE 2120.810734 +---------------------- -------------------------------------------------------- +Time: 5.170s Load: 0.050s, Pack+Encode: 2.621s, Decode+Unpack: 2.499s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2120.8107 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02988304-ILSVRC2012_val_00003491.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n02988304-ILSVRC2012_val_00003491.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992211-ILSVRC2012_val_00000108.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992211-ILSVRC2012_val_00000108.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 325,272B, BPFP=0.6174 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 727,972B, BPFP=1.3817 +⌛️ [2/4] FRONTEND: Frontend time: 2.624s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.530s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10107529 12.43681384 + layer.39.0 89.13089923 6351.75072886 + ------------------------------------------------------------------------------------- + TOTAL 44.61598726 3182.09377135 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1053244 +BPFP 0.9996 bits/point +EBPFP 0.9996 equivalent bits/point +MSE 3182.093771 +---------------------- -------------------------------------------------------- +Time: 5.204s Load: 0.050s, Pack+Encode: 2.624s, Decode+Unpack: 2.530s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 3182.0938 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992211-ILSVRC2012_val_00000108.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n02992211-ILSVRC2012_val_00000108.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992529-ILSVRC2012_val_00000089.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992529-ILSVRC2012_val_00000089.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 295,264B, BPFP=0.5604 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 600,088B, BPFP=1.1390 +⌛️ [2/4] FRONTEND: Frontend time: 2.615s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.522s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11197385 12.43633743 + layer.39.0 964.25631681 5129.55393586 + ------------------------------------------------------------------------------------- + TOTAL 482.18414533 2570.99513664 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 895352 +BPFP 0.8497 bits/point +EBPFP 0.8497 equivalent bits/point +MSE 2570.995137 +---------------------- -------------------------------------------------------- +Time: 5.190s Load: 0.052s, Pack+Encode: 2.615s, Decode+Unpack: 2.522s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2570.9951 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992529-ILSVRC2012_val_00000089.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n02992529-ILSVRC2012_val_00000089.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02999410-ILSVRC2012_val_00000376.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02999410-ILSVRC2012_val_00000376.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 313,000B, BPFP=0.5941 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 416,548B, BPFP=0.7906 +⌛️ [2/4] FRONTEND: Frontend time: 2.623s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.506s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10398186 12.32018153 + layer.39.0 145.78410471 2387.47521866 + ------------------------------------------------------------------------------------- + TOTAL 72.94404329 1199.89770010 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 729548 +BPFP 0.6924 bits/point +EBPFP 0.6924 equivalent bits/point +MSE 1199.897700 +---------------------- -------------------------------------------------------- +Time: 5.190s Load: 0.061s, Pack+Encode: 2.623s, Decode+Unpack: 2.506s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1199.8977 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02999410-ILSVRC2012_val_00000376.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n02999410-ILSVRC2012_val_00000376.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000134-ILSVRC2012_val_00001094.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000134-ILSVRC2012_val_00001094.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 271,028B, BPFP=0.5144 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 587,596B, BPFP=1.1153 +⌛️ [2/4] FRONTEND: Frontend time: 2.636s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.524s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09696872 12.57226847 + layer.39.0 22.81329530 3808.97764820 + ------------------------------------------------------------------------------------- + TOTAL 11.45513201 1910.77495834 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 858624 +BPFP 0.8149 bits/point +EBPFP 0.8149 equivalent bits/point +MSE 1910.774958 +---------------------- -------------------------------------------------------- +Time: 5.231s Load: 0.070s, Pack+Encode: 2.636s, Decode+Unpack: 2.524s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1910.7750 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000134-ILSVRC2012_val_00001094.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03000134-ILSVRC2012_val_00001094.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000247-ILSVRC2012_val_00002280.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000247-ILSVRC2012_val_00002280.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 556,512B, BPFP=1.0563 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 499,992B, BPFP=0.9490 +⌛️ [2/4] FRONTEND: Frontend time: 2.640s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.531s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.29135144 218.26987670 + layer.39.0 428.26293732 3118.27016521 + ------------------------------------------------------------------------------------- + TOTAL 214.27714438 1668.27002095 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1056504 +BPFP 1.0027 bits/point +EBPFP 1.0027 equivalent bits/point +MSE 1668.270021 +---------------------- -------------------------------------------------------- +Time: 5.221s Load: 0.050s, Pack+Encode: 2.640s, Decode+Unpack: 2.531s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1668.2700 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000247-ILSVRC2012_val_00002280.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03000247-ILSVRC2012_val_00002280.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000684-ILSVRC2012_val_00000537.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000684-ILSVRC2012_val_00000537.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 387,392B, BPFP=0.7353 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 525,200B, BPFP=0.9969 +⌛️ [2/4] FRONTEND: Frontend time: 2.636s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.530s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13150742 257.23496720 + layer.39.0 55.24585459 2628.82604470 + ------------------------------------------------------------------------------------- + TOTAL 27.68868101 1443.03050595 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 912592 +BPFP 0.8661 bits/point +EBPFP 0.8661 equivalent bits/point +MSE 1443.030506 +---------------------- -------------------------------------------------------- +Time: 5.227s Load: 0.061s, Pack+Encode: 2.636s, Decode+Unpack: 2.530s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1443.0305 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000684-ILSVRC2012_val_00000537.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03000684-ILSVRC2012_val_00000537.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03014705-ILSVRC2012_val_00001168.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03014705-ILSVRC2012_val_00001168.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 235,356B, BPFP=0.4467 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 493,256B, BPFP=0.9362 +⌛️ [2/4] FRONTEND: Frontend time: 2.609s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.513s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09787338 12.31286253 + layer.39.0 322.89622813 3246.42128280 + ------------------------------------------------------------------------------------- + TOTAL 161.49705076 1629.36707267 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 728612 +BPFP 0.6915 bits/point +EBPFP 0.6915 equivalent bits/point +MSE 1629.367073 +---------------------- -------------------------------------------------------- +Time: 5.172s Load: 0.050s, Pack+Encode: 2.609s, Decode+Unpack: 2.513s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1629.3671 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03014705-ILSVRC2012_val_00001168.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03014705-ILSVRC2012_val_00001168.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03017168-ILSVRC2012_val_00001601.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03017168-ILSVRC2012_val_00001601.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 280,172B, BPFP=0.5318 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 595,504B, BPFP=1.1303 +⌛️ [2/4] FRONTEND: Frontend time: 2.608s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.518s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10213913 0.88044352 + layer.39.0 475.40952988 4293.83916424 + ------------------------------------------------------------------------------------- + TOTAL 237.75583451 2147.35980388 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 875676 +BPFP 0.8311 bits/point +EBPFP 0.8311 equivalent bits/point +MSE 2147.359804 +---------------------- -------------------------------------------------------- +Time: 5.177s Load: 0.051s, Pack+Encode: 2.608s, Decode+Unpack: 2.518s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2147.3598 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03017168-ILSVRC2012_val_00001601.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03017168-ILSVRC2012_val_00001601.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03018349-ILSVRC2012_val_00000346.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03018349-ILSVRC2012_val_00000346.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 303,184B, BPFP=0.5755 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 594,916B, BPFP=1.1292 +⌛️ [2/4] FRONTEND: Frontend time: 2.616s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.514s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09959339 12.78530430 + layer.39.0 56.59841169 3322.55199223 + ------------------------------------------------------------------------------------- + TOTAL 28.34900254 1667.66864826 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 898100 +BPFP 0.8523 bits/point +EBPFP 0.8523 equivalent bits/point +MSE 1667.668648 +---------------------- -------------------------------------------------------- +Time: 5.192s Load: 0.062s, Pack+Encode: 2.616s, Decode+Unpack: 2.514s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1667.6686 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03018349-ILSVRC2012_val_00000346.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03018349-ILSVRC2012_val_00000346.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03026506-ILSVRC2012_val_00001908.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03026506-ILSVRC2012_val_00001908.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 317,260B, BPFP=0.6022 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 610,412B, BPFP=1.1586 +⌛️ [2/4] FRONTEND: Frontend time: 2.627s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.503s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10977067 8.52962619 + layer.39.0 668.54063411 3533.71428571 + ------------------------------------------------------------------------------------- + TOTAL 334.32520239 1771.12195595 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 927672 +BPFP 0.8804 bits/point +EBPFP 0.8804 equivalent bits/point +MSE 1771.121956 +---------------------- -------------------------------------------------------- +Time: 5.181s Load: 0.051s, Pack+Encode: 2.627s, Decode+Unpack: 2.503s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1771.1220 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03026506-ILSVRC2012_val_00001908.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03026506-ILSVRC2012_val_00001908.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03028079-ILSVRC2012_val_00003351.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03028079-ILSVRC2012_val_00003351.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 272,320B, BPFP=0.5169 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 360,448B, BPFP=0.6842 +⌛️ [2/4] FRONTEND: Frontend time: 2.609s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.527s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10934904 13.07697476 + layer.39.0 15.31112010 1732.27137998 + ------------------------------------------------------------------------------------- + TOTAL 7.71023457 872.67417737 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 632768 +BPFP 0.6005 bits/point +EBPFP 0.6005 equivalent bits/point +MSE 872.674177 +---------------------- -------------------------------------------------------- +Time: 5.207s Load: 0.071s, Pack+Encode: 2.609s, Decode+Unpack: 2.527s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 872.6742 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03028079-ILSVRC2012_val_00003351.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03028079-ILSVRC2012_val_00003351.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03032252-ILSVRC2012_val_00000086.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03032252-ILSVRC2012_val_00000086.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 370,472B, BPFP=0.7032 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 550,088B, BPFP=1.0441 +⌛️ [2/4] FRONTEND: Frontend time: 2.628s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.526s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13507480 170.50262694 + layer.39.0 103.55165816 3053.00097182 + ------------------------------------------------------------------------------------- + TOTAL 51.84336648 1611.75179938 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 920560 +BPFP 0.8736 bits/point +EBPFP 0.8736 equivalent bits/point +MSE 1611.751799 +---------------------- -------------------------------------------------------- +Time: 5.214s Load: 0.060s, Pack+Encode: 2.628s, Decode+Unpack: 2.526s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1611.7518 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03032252-ILSVRC2012_val_00000086.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03032252-ILSVRC2012_val_00000086.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03041632-ILSVRC2012_val_00000564.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03041632-ILSVRC2012_val_00000564.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 256,400B, BPFP=0.4867 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 585,040B, BPFP=1.1105 +⌛️ [2/4] FRONTEND: Frontend time: 2.614s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.498s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10123130 37.84403092 + layer.39.0 371.34277818 5633.09718173 + ------------------------------------------------------------------------------------- + TOTAL 185.72200474 2835.47060632 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 841440 +BPFP 0.7986 bits/point +EBPFP 0.7986 equivalent bits/point +MSE 2835.470606 +---------------------- -------------------------------------------------------- +Time: 5.162s Load: 0.051s, Pack+Encode: 2.614s, Decode+Unpack: 2.498s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2835.4706 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03041632-ILSVRC2012_val_00000564.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03041632-ILSVRC2012_val_00000564.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03042490-ILSVRC2012_val_00001426.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03042490-ILSVRC2012_val_00001426.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 334,516B, BPFP=0.6349 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 546,996B, BPFP=1.0382 +⌛️ [2/4] FRONTEND: Frontend time: 2.658s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.509s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10706725 87.09604288 + layer.39.0 141.71039845 4150.47619048 + ------------------------------------------------------------------------------------- + TOTAL 70.90873285 2118.78611668 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 881512 +BPFP 0.8366 bits/point +EBPFP 0.8366 equivalent bits/point +MSE 2118.786117 +---------------------- -------------------------------------------------------- +Time: 5.218s Load: 0.051s, Pack+Encode: 2.658s, Decode+Unpack: 2.509s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2118.7861 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03042490-ILSVRC2012_val_00001426.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03042490-ILSVRC2012_val_00001426.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03047690-ILSVRC2012_val_00001500.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03047690-ILSVRC2012_val_00001500.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 261,692B, BPFP=0.4967 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 541,664B, BPFP=1.0281 +⌛️ [2/4] FRONTEND: Frontend time: 2.628s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.519s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09570478 0.86278965 + layer.39.0 226.76483540 3122.14674441 + ------------------------------------------------------------------------------------- + TOTAL 113.43027009 1561.50476703 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 803356 +BPFP 0.7624 bits/point +EBPFP 0.7624 equivalent bits/point +MSE 1561.504767 +---------------------- -------------------------------------------------------- +Time: 5.219s Load: 0.071s, Pack+Encode: 2.628s, Decode+Unpack: 2.519s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1561.5048 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03047690-ILSVRC2012_val_00001500.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03047690-ILSVRC2012_val_00001500.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03062245-ILSVRC2012_val_00000344.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03062245-ILSVRC2012_val_00000344.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 229,436B, BPFP=0.4355 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 348,596B, BPFP=0.6617 +⌛️ [2/4] FRONTEND: Frontend time: 2.603s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.505s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09619164 8.67201356 + layer.39.0 46.71096787 2192.44023324 + ------------------------------------------------------------------------------------- + TOTAL 23.40357976 1100.55612340 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 578032 +BPFP 0.5486 bits/point +EBPFP 0.5486 equivalent bits/point +MSE 1100.556123 +---------------------- -------------------------------------------------------- +Time: 5.168s Load: 0.060s, Pack+Encode: 2.603s, Decode+Unpack: 2.505s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1100.5561 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03062245-ILSVRC2012_val_00000344.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03062245-ILSVRC2012_val_00000344.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063599-ILSVRC2012_val_00000164.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063599-ILSVRC2012_val_00000164.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 295,524B, BPFP=0.5609 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 535,224B, BPFP=1.0159 +⌛️ [2/4] FRONTEND: Frontend time: 2.623s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.507s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10111790 1.65364714 + layer.39.0 9.80528160 2889.62876579 + ------------------------------------------------------------------------------------- + TOTAL 4.95319975 1445.64120647 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 830748 +BPFP 0.7884 bits/point +EBPFP 0.7884 equivalent bits/point +MSE 1445.641206 +---------------------- -------------------------------------------------------- +Time: 5.198s Load: 0.069s, Pack+Encode: 2.623s, Decode+Unpack: 2.507s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1445.6412 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063599-ILSVRC2012_val_00000164.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03063599-ILSVRC2012_val_00000164.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063689-ILSVRC2012_val_00001940.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063689-ILSVRC2012_val_00001940.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 264,004B, BPFP=0.5011 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 580,544B, BPFP=1.1019 +⌛️ [2/4] FRONTEND: Frontend time: 2.637s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.515s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09645106 0.87401353 + layer.39.0 18.48014797 4426.07920311 + ------------------------------------------------------------------------------------- + TOTAL 9.28829952 2213.47660832 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 844548 +BPFP 0.8015 bits/point +EBPFP 0.8015 equivalent bits/point +MSE 2213.476608 +---------------------- -------------------------------------------------------- +Time: 5.203s Load: 0.051s, Pack+Encode: 2.637s, Decode+Unpack: 2.515s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2213.4766 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063689-ILSVRC2012_val_00001940.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03063689-ILSVRC2012_val_00001940.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03065424-ILSVRC2012_val_00000915.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03065424-ILSVRC2012_val_00000915.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 372,696B, BPFP=0.7074 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 699,604B, BPFP=1.3279 +⌛️ [2/4] FRONTEND: Frontend time: 2.640s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.536s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12384982 75.34396259 + layer.39.0 2154.15986395 5599.93100097 + ------------------------------------------------------------------------------------- + TOTAL 1077.14185688 2837.63748178 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1072300 +BPFP 1.0177 bits/point +EBPFP 1.0177 equivalent bits/point +MSE 2837.637482 +---------------------- -------------------------------------------------------- +Time: 5.237s Load: 0.062s, Pack+Encode: 2.640s, Decode+Unpack: 2.536s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2837.6375 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03065424-ILSVRC2012_val_00000915.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03065424-ILSVRC2012_val_00000915.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03075370-ILSVRC2012_val_00004971.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03075370-ILSVRC2012_val_00004971.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 265,840B, BPFP=0.5046 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 380,540B, BPFP=0.7223 +⌛️ [2/4] FRONTEND: Frontend time: 2.620s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.519s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10672879 12.53463807 + layer.39.0 301.29020894 1830.84620991 + ------------------------------------------------------------------------------------- + TOTAL 150.69846886 921.69042399 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 646380 +BPFP 0.6134 bits/point +EBPFP 0.6134 equivalent bits/point +MSE 921.690424 +---------------------- -------------------------------------------------------- +Time: 5.191s Load: 0.052s, Pack+Encode: 2.620s, Decode+Unpack: 2.519s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 921.6904 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03075370-ILSVRC2012_val_00004971.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03075370-ILSVRC2012_val_00004971.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03089624-ILSVRC2012_val_00001190.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03089624-ILSVRC2012_val_00001190.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 315,200B, BPFP=0.5983 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 568,932B, BPFP=1.0799 +⌛️ [2/4] FRONTEND: Frontend time: 2.616s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.518s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10385029 8.46767094 + layer.39.0 606.38896987 4322.72351798 + ------------------------------------------------------------------------------------- + TOTAL 303.24641008 2165.59559446 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 884132 +BPFP 0.8391 bits/point +EBPFP 0.8391 equivalent bits/point +MSE 2165.595594 +---------------------- -------------------------------------------------------- +Time: 5.186s Load: 0.052s, Pack+Encode: 2.616s, Decode+Unpack: 2.518s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2165.5956 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03089624-ILSVRC2012_val_00001190.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03089624-ILSVRC2012_val_00001190.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03095699-ILSVRC2012_val_00000403.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03095699-ILSVRC2012_val_00000403.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 365,880B, BPFP=0.6945 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 656,760B, BPFP=1.2466 +⌛️ [2/4] FRONTEND: Frontend time: 2.624s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.514s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12139760 112.39015428 + layer.39.0 62.59250486 4459.97035957 + ------------------------------------------------------------------------------------- + TOTAL 31.35695123 2286.18025692 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1022640 +BPFP 0.9705 bits/point +EBPFP 0.9705 equivalent bits/point +MSE 2286.180257 +---------------------- -------------------------------------------------------- +Time: 5.189s Load: 0.051s, Pack+Encode: 2.624s, Decode+Unpack: 2.514s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2286.1803 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03095699-ILSVRC2012_val_00000403.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03095699-ILSVRC2012_val_00000403.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03100240-ILSVRC2012_val_00001201.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03100240-ILSVRC2012_val_00001201.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 305,780B, BPFP=0.5804 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 350,568B, BPFP=0.6654 +⌛️ [2/4] FRONTEND: Frontend time: 2.617s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.505s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10258218 12.72884020 + layer.39.0 42.98202138 2923.99441205 + ------------------------------------------------------------------------------------- + TOTAL 21.54230178 1468.36162612 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 656348 +BPFP 0.6229 bits/point +EBPFP 0.6229 equivalent bits/point +MSE 1468.361626 +---------------------- -------------------------------------------------------- +Time: 5.193s Load: 0.071s, Pack+Encode: 2.617s, Decode+Unpack: 2.505s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1468.3616 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03100240-ILSVRC2012_val_00001201.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03100240-ILSVRC2012_val_00001201.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03109150-ILSVRC2012_val_00000678.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03109150-ILSVRC2012_val_00000678.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 313,956B, BPFP=0.5959 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 540,288B, BPFP=1.0255 +⌛️ [2/4] FRONTEND: Frontend time: 2.629s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.515s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09720685 12.24158486 + layer.39.0 496.21158285 3779.36030126 + ------------------------------------------------------------------------------------- + TOTAL 248.15439485 1895.80094306 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 854244 +BPFP 0.8107 bits/point +EBPFP 0.8107 equivalent bits/point +MSE 1895.800943 +---------------------- -------------------------------------------------------- +Time: 5.194s Load: 0.050s, Pack+Encode: 2.629s, Decode+Unpack: 2.515s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1895.8009 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03109150-ILSVRC2012_val_00000678.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03109150-ILSVRC2012_val_00000678.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03110669-ILSVRC2012_val_00002171.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03110669-ILSVRC2012_val_00002171.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 396,396B, BPFP=0.7524 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 584,312B, BPFP=1.1091 +⌛️ [2/4] FRONTEND: Frontend time: 2.639s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.523s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.15128201 175.61165270 + layer.39.0 15.00769387 3534.10325559 + ------------------------------------------------------------------------------------- + TOTAL 7.57948794 1854.85745414 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 980708 +BPFP 0.9307 bits/point +EBPFP 0.9307 equivalent bits/point +MSE 1854.857454 +---------------------- -------------------------------------------------------- +Time: 5.214s Load: 0.052s, Pack+Encode: 2.639s, Decode+Unpack: 2.523s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1854.8575 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03110669-ILSVRC2012_val_00002171.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03110669-ILSVRC2012_val_00002171.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124043-ILSVRC2012_val_00000766.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124043-ILSVRC2012_val_00000766.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 269,680B, BPFP=0.5119 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 544,072B, BPFP=1.0327 +⌛️ [2/4] FRONTEND: Frontend time: 2.626s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.509s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11473456 37.14900692 + layer.39.0 54.83309418 3648.23590865 + ------------------------------------------------------------------------------------- + TOTAL 27.47391437 1842.69245779 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 813752 +BPFP 0.7723 bits/point +EBPFP 0.7723 equivalent bits/point +MSE 1842.692458 +---------------------- -------------------------------------------------------- +Time: 5.185s Load: 0.050s, Pack+Encode: 2.626s, Decode+Unpack: 2.509s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1842.6925 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124043-ILSVRC2012_val_00000766.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03124043-ILSVRC2012_val_00000766.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124170-ILSVRC2012_val_00001875.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124170-ILSVRC2012_val_00001875.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 279,684B, BPFP=0.5309 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 333,560B, BPFP=0.6331 +⌛️ [2/4] FRONTEND: Frontend time: 2.593s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.500s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11393612 12.33099775 + layer.39.0 9.06747107 1779.84924684 + ------------------------------------------------------------------------------------- + TOTAL 4.59070360 896.09012229 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 613244 +BPFP 0.5820 bits/point +EBPFP 0.5820 equivalent bits/point +MSE 896.090122 +---------------------- -------------------------------------------------------- +Time: 5.143s Load: 0.050s, Pack+Encode: 2.593s, Decode+Unpack: 2.500s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 896.0901 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124170-ILSVRC2012_val_00001875.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03124170-ILSVRC2012_val_00001875.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03126707-ILSVRC2012_val_00000020.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03126707-ILSVRC2012_val_00000020.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 312,784B, BPFP=0.5937 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 414,520B, BPFP=0.7868 +⌛️ [2/4] FRONTEND: Frontend time: 2.616s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.513s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.15273996 50.68301673 + layer.39.0 1033.15269679 3069.42298348 + ------------------------------------------------------------------------------------- + TOTAL 516.65271838 1560.05300011 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 727304 +BPFP 0.6902 bits/point +EBPFP 0.6902 equivalent bits/point +MSE 1560.053000 +---------------------- -------------------------------------------------------- +Time: 5.189s Load: 0.059s, Pack+Encode: 2.616s, Decode+Unpack: 2.513s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1560.0530 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03126707-ILSVRC2012_val_00000020.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03126707-ILSVRC2012_val_00000020.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03127747-ILSVRC2012_val_00001689.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03127747-ILSVRC2012_val_00001689.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 239,868B, BPFP=0.4553 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 444,352B, BPFP=0.8434 +⌛️ [2/4] FRONTEND: Frontend time: 2.623s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.510s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10152024 12.32177687 + layer.39.0 322.92343902 3016.05393586 + ------------------------------------------------------------------------------------- + TOTAL 161.51247963 1514.18785636 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 684220 +BPFP 0.6494 bits/point +EBPFP 0.6494 equivalent bits/point +MSE 1514.187856 +---------------------- -------------------------------------------------------- +Time: 5.185s Load: 0.051s, Pack+Encode: 2.623s, Decode+Unpack: 2.510s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1514.1879 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03127747-ILSVRC2012_val_00001689.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03127747-ILSVRC2012_val_00001689.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03131574-ILSVRC2012_val_00003036.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03131574-ILSVRC2012_val_00003036.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 261,256B, BPFP=0.4959 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 632,088B, BPFP=1.1998 +⌛️ [2/4] FRONTEND: Frontend time: 2.621s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.518s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09568423 12.28848738 + layer.39.0 163.24681122 3881.71404276 + ------------------------------------------------------------------------------------- + TOTAL 81.67124773 1947.00126507 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 893344 +BPFP 0.8478 bits/point +EBPFP 0.8478 equivalent bits/point +MSE 1947.001265 +---------------------- -------------------------------------------------------- +Time: 5.199s Load: 0.060s, Pack+Encode: 2.621s, Decode+Unpack: 2.518s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1947.0013 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03131574-ILSVRC2012_val_00003036.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03131574-ILSVRC2012_val_00003036.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03133878-ILSVRC2012_val_00000534.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03133878-ILSVRC2012_val_00000534.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 363,296B, BPFP=0.6896 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 611,424B, BPFP=1.1605 +⌛️ [2/4] FRONTEND: Frontend time: 2.637s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.505s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11186348 62.55404215 + layer.39.0 28.46096218 3737.49708455 + ------------------------------------------------------------------------------------- + TOTAL 14.28641283 1900.02556335 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 974720 +BPFP 0.9250 bits/point +EBPFP 0.9250 equivalent bits/point +MSE 1900.025563 +---------------------- -------------------------------------------------------- +Time: 5.193s Load: 0.052s, Pack+Encode: 2.637s, Decode+Unpack: 2.505s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1900.0256 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03133878-ILSVRC2012_val_00000534.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03133878-ILSVRC2012_val_00000534.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03134739-ILSVRC2012_val_00000249.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03134739-ILSVRC2012_val_00000249.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 275,356B, BPFP=0.5226 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 619,236B, BPFP=1.1754 +⌛️ [2/4] FRONTEND: Frontend time: 2.616s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.513s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09967384 12.61896790 + layer.39.0 372.24465500 4583.50340136 + ------------------------------------------------------------------------------------- + TOTAL 186.17216442 2298.06118463 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 894592 +BPFP 0.8490 bits/point +EBPFP 0.8490 equivalent bits/point +MSE 2298.061185 +---------------------- -------------------------------------------------------- +Time: 5.180s Load: 0.051s, Pack+Encode: 2.616s, Decode+Unpack: 2.513s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2298.0612 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03134739-ILSVRC2012_val_00000249.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03134739-ILSVRC2012_val_00000249.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03141823-ILSVRC2012_val_00001337.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03141823-ILSVRC2012_val_00001337.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 328,000B, BPFP=0.6226 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 578,648B, BPFP=1.0983 +⌛️ [2/4] FRONTEND: Frontend time: 2.640s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.524s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10422104 1.30135376 + layer.39.0 29.45558301 3235.43513120 + ------------------------------------------------------------------------------------- + TOTAL 14.77990203 1618.36824248 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 906648 +BPFP 0.8604 bits/point +EBPFP 0.8604 equivalent bits/point +MSE 1618.368242 +---------------------- -------------------------------------------------------- +Time: 5.225s Load: 0.061s, Pack+Encode: 2.640s, Decode+Unpack: 2.524s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1618.3682 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03141823-ILSVRC2012_val_00001337.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03141823-ILSVRC2012_val_00001337.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03160309-ILSVRC2012_val_00000330.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03160309-ILSVRC2012_val_00000330.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 267,476B, BPFP=0.5077 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 312,928B, BPFP=0.5940 +⌛️ [2/4] FRONTEND: Frontend time: 2.616s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.519s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09980877 12.56144846 + layer.39.0 30.04123011 2370.44290573 + ------------------------------------------------------------------------------------- + TOTAL 15.07051944 1191.50217710 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 580404 +BPFP 0.5508 bits/point +EBPFP 0.5508 equivalent bits/point +MSE 1191.502177 +---------------------- -------------------------------------------------------- +Time: 5.196s Load: 0.061s, Pack+Encode: 2.616s, Decode+Unpack: 2.519s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1191.5022 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03160309-ILSVRC2012_val_00000330.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03160309-ILSVRC2012_val_00000330.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03187595-ILSVRC2012_val_00000137.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03187595-ILSVRC2012_val_00000137.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 285,972B, BPFP=0.5428 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 466,264B, BPFP=0.8850 +⌛️ [2/4] FRONTEND: Frontend time: 2.627s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.514s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10716813 12.31025742 + layer.39.0 12.39187394 2716.57531584 + ------------------------------------------------------------------------------------- + TOTAL 6.24952103 1364.44278663 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 752236 +BPFP 0.7139 bits/point +EBPFP 0.7139 equivalent bits/point +MSE 1364.442787 +---------------------- -------------------------------------------------------- +Time: 5.203s Load: 0.062s, Pack+Encode: 2.627s, Decode+Unpack: 2.514s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1364.4428 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03187595-ILSVRC2012_val_00000137.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03187595-ILSVRC2012_val_00000137.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03188531-ILSVRC2012_val_00000493.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03188531-ILSVRC2012_val_00000493.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 229,688B, BPFP=0.4360 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 472,368B, BPFP=0.8966 +⌛️ [2/4] FRONTEND: Frontend time: 2.631s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.526s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09509044 12.55179103 + layer.39.0 10.77256154 2983.47886297 + ------------------------------------------------------------------------------------- + TOTAL 5.43382599 1498.01532700 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 702056 +BPFP 0.6663 bits/point +EBPFP 0.6663 equivalent bits/point +MSE 1498.015327 +---------------------- -------------------------------------------------------- +Time: 5.208s Load: 0.051s, Pack+Encode: 2.631s, Decode+Unpack: 2.526s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1498.0153 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03188531-ILSVRC2012_val_00000493.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03188531-ILSVRC2012_val_00000493.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03196217-ILSVRC2012_val_00003643.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03196217-ILSVRC2012_val_00003643.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 237,184B, BPFP=0.4502 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 505,924B, BPFP=0.9603 +⌛️ [2/4] FRONTEND: Frontend time: 2.618s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.520s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09478207 12.23950361 + layer.39.0 65.57403274 4031.66034985 + ------------------------------------------------------------------------------------- + TOTAL 32.83440740 2021.94992673 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 743108 +BPFP 0.7052 bits/point +EBPFP 0.7052 equivalent bits/point +MSE 2021.949927 +---------------------- -------------------------------------------------------- +Time: 5.208s Load: 0.070s, Pack+Encode: 2.618s, Decode+Unpack: 2.520s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2021.9499 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03196217-ILSVRC2012_val_00003643.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03196217-ILSVRC2012_val_00003643.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03201208-ILSVRC2012_val_00000241.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03201208-ILSVRC2012_val_00000241.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 318,504B, BPFP=0.6045 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 383,956B, BPFP=0.7288 +⌛️ [2/4] FRONTEND: Frontend time: 2.623s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.510s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10331685 37.46943331 + layer.39.0 136.59314261 1959.87888727 + ------------------------------------------------------------------------------------- + TOTAL 68.34822973 998.67416029 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 702460 +BPFP 0.6667 bits/point +EBPFP 0.6667 equivalent bits/point +MSE 998.674160 +---------------------- -------------------------------------------------------- +Time: 5.185s Load: 0.051s, Pack+Encode: 2.623s, Decode+Unpack: 2.510s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 998.6742 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03201208-ILSVRC2012_val_00000241.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03201208-ILSVRC2012_val_00000241.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03207743-ILSVRC2012_val_00000256.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03207743-ILSVRC2012_val_00000256.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 397,956B, BPFP=0.7554 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 465,492B, BPFP=0.8835 +⌛️ [2/4] FRONTEND: Frontend time: 2.646s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.540s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09674843 223.54140853 + layer.39.0 189.63590258 4733.97376093 + ------------------------------------------------------------------------------------- + TOTAL 94.86632550 2478.75758473 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 863448 +BPFP 0.8194 bits/point +EBPFP 0.8194 equivalent bits/point +MSE 2478.757585 +---------------------- -------------------------------------------------------- +Time: 5.236s Load: 0.050s, Pack+Encode: 2.646s, Decode+Unpack: 2.540s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2478.7576 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03207743-ILSVRC2012_val_00000256.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03207743-ILSVRC2012_val_00000256.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03216828-ILSVRC2012_val_00001729.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03216828-ILSVRC2012_val_00001729.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 312,304B, BPFP=0.5928 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 397,732B, BPFP=0.7549 +⌛️ [2/4] FRONTEND: Frontend time: 2.606s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.521s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10800209 136.34948980 + layer.39.0 31.30713223 2304.98785228 + ------------------------------------------------------------------------------------- + TOTAL 15.70756716 1220.66867104 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 710036 +BPFP 0.6739 bits/point +EBPFP 0.6739 equivalent bits/point +MSE 1220.668671 +---------------------- -------------------------------------------------------- +Time: 5.179s Load: 0.051s, Pack+Encode: 2.606s, Decode+Unpack: 2.521s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1220.6687 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03216828-ILSVRC2012_val_00001729.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03216828-ILSVRC2012_val_00001729.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03218198-ILSVRC2012_val_00002266.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03218198-ILSVRC2012_val_00002266.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 358,560B, BPFP=0.6806 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 575,412B, BPFP=1.0922 +⌛️ [2/4] FRONTEND: Frontend time: 2.626s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.523s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11617067 70.64906007 + layer.39.0 195.83184524 3634.30952381 + ------------------------------------------------------------------------------------- + TOTAL 97.97400795 1852.47929194 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 933972 +BPFP 0.8864 bits/point +EBPFP 0.8864 equivalent bits/point +MSE 1852.479292 +---------------------- -------------------------------------------------------- +Time: 5.219s Load: 0.071s, Pack+Encode: 2.626s, Decode+Unpack: 2.523s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1852.4793 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03218198-ILSVRC2012_val_00002266.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03218198-ILSVRC2012_val_00002266.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03220513-ILSVRC2012_val_00001868.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03220513-ILSVRC2012_val_00001868.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.073s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 534,096B, BPFP=1.0138 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 652,472B, BPFP=1.2384 +⌛️ [2/4] FRONTEND: Frontend time: 2.641s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.535s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.20032125 25.08749962 + layer.39.0 377.00176142 4152.42079689 + ------------------------------------------------------------------------------------- + TOTAL 188.60104134 2088.75414826 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1186568 +BPFP 1.1261 bits/point +EBPFP 1.1261 equivalent bits/point +MSE 2088.754148 +---------------------- -------------------------------------------------------- +Time: 5.249s Load: 0.073s, Pack+Encode: 2.641s, Decode+Unpack: 2.535s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2088.7541 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03220513-ILSVRC2012_val_00001868.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03220513-ILSVRC2012_val_00001868.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03223299-ILSVRC2012_val_00001893.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03223299-ILSVRC2012_val_00001893.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 239,880B, BPFP=0.4553 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 457,220B, BPFP=0.8678 +⌛️ [2/4] FRONTEND: Frontend time: 2.616s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.504s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10735053 9.01056472 + layer.39.0 354.51621720 3993.10884354 + ------------------------------------------------------------------------------------- + TOTAL 177.31178386 2001.05970413 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 697100 +BPFP 0.6616 bits/point +EBPFP 0.6616 equivalent bits/point +MSE 2001.059704 +---------------------- -------------------------------------------------------- +Time: 5.179s Load: 0.060s, Pack+Encode: 2.616s, Decode+Unpack: 2.504s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2001.0597 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03223299-ILSVRC2012_val_00001893.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03223299-ILSVRC2012_val_00001893.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03240683-ILSVRC2012_val_00000504.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03240683-ILSVRC2012_val_00000504.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 264,560B, BPFP=0.5022 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 498,896B, BPFP=0.9469 +⌛️ [2/4] FRONTEND: Frontend time: 2.624s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.505s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10065408 12.26369560 + layer.39.0 443.53838678 3249.78498542 + ------------------------------------------------------------------------------------- + TOTAL 221.81952043 1631.02434051 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 763456 +BPFP 0.7246 bits/point +EBPFP 0.7246 equivalent bits/point +MSE 1631.024341 +---------------------- -------------------------------------------------------- +Time: 5.199s Load: 0.070s, Pack+Encode: 2.624s, Decode+Unpack: 2.505s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1631.0243 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03240683-ILSVRC2012_val_00000504.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03240683-ILSVRC2012_val_00000504.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03250847-ILSVRC2012_val_00000542.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03250847-ILSVRC2012_val_00000542.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 315,092B, BPFP=0.5981 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 586,964B, BPFP=1.1141 +⌛️ [2/4] FRONTEND: Frontend time: 2.633s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.530s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10136319 12.43636685 + layer.39.0 140.24735787 4981.24344023 + ------------------------------------------------------------------------------------- + TOTAL 70.17436053 2496.83990354 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 902056 +BPFP 0.8561 bits/point +EBPFP 0.8561 equivalent bits/point +MSE 2496.839904 +---------------------- -------------------------------------------------------- +Time: 5.214s Load: 0.051s, Pack+Encode: 2.633s, Decode+Unpack: 2.530s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2496.8399 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03250847-ILSVRC2012_val_00000542.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03250847-ILSVRC2012_val_00000542.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03255030-ILSVRC2012_val_00001045.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03255030-ILSVRC2012_val_00001045.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 270,928B, BPFP=0.5142 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 426,208B, BPFP=0.8090 +⌛️ [2/4] FRONTEND: Frontend time: 2.627s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.526s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10050351 0.86368020 + layer.39.0 12.06722622 2378.16763848 + ------------------------------------------------------------------------------------- + TOTAL 6.08386487 1189.51565934 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 697136 +BPFP 0.6616 bits/point +EBPFP 0.6616 equivalent bits/point +MSE 1189.515659 +---------------------- -------------------------------------------------------- +Time: 5.212s Load: 0.059s, Pack+Encode: 2.627s, Decode+Unpack: 2.526s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1189.5157 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03255030-ILSVRC2012_val_00001045.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03255030-ILSVRC2012_val_00001045.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03271574-ILSVRC2012_val_00000942.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03271574-ILSVRC2012_val_00000942.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 271,560B, BPFP=0.5154 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 567,152B, BPFP=1.0765 +⌛️ [2/4] FRONTEND: Frontend time: 2.629s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.510s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10164264 12.14353855 + layer.39.0 660.63544704 4640.03498542 + ------------------------------------------------------------------------------------- + TOTAL 330.36854484 2326.08926199 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 838712 +BPFP 0.7960 bits/point +EBPFP 0.7960 equivalent bits/point +MSE 2326.089262 +---------------------- -------------------------------------------------------- +Time: 5.199s Load: 0.061s, Pack+Encode: 2.629s, Decode+Unpack: 2.510s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2326.0893 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03271574-ILSVRC2012_val_00000942.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03271574-ILSVRC2012_val_00000942.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272010-ILSVRC2012_val_00000374.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272010-ILSVRC2012_val_00000374.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 271,620B, BPFP=0.5156 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 454,188B, BPFP=0.8621 +⌛️ [2/4] FRONTEND: Frontend time: 2.620s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.513s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10420663 12.91161208 + layer.39.0 9.63653369 2321.34936832 + ------------------------------------------------------------------------------------- + TOTAL 4.87037016 1167.13049020 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 725808 +BPFP 0.6888 bits/point +EBPFP 0.6888 equivalent bits/point +MSE 1167.130490 +---------------------- -------------------------------------------------------- +Time: 5.185s Load: 0.052s, Pack+Encode: 2.620s, Decode+Unpack: 2.513s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1167.1305 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272010-ILSVRC2012_val_00000374.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03272010-ILSVRC2012_val_00000374.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272562-ILSVRC2012_val_00001699.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272562-ILSVRC2012_val_00001699.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 335,192B, BPFP=0.6362 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 393,648B, BPFP=0.7472 +⌛️ [2/4] FRONTEND: Frontend time: 2.613s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.513s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11399285 14.24988517 + layer.39.0 12.79457642 3302.22643343 + ------------------------------------------------------------------------------------- + TOTAL 6.45428464 1658.23815930 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 728840 +BPFP 0.6917 bits/point +EBPFP 0.6917 equivalent bits/point +MSE 1658.238159 +---------------------- -------------------------------------------------------- +Time: 5.198s Load: 0.071s, Pack+Encode: 2.613s, Decode+Unpack: 2.513s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1658.2382 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272562-ILSVRC2012_val_00001699.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03272562-ILSVRC2012_val_00001699.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03290653-ILSVRC2012_val_00000199.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03290653-ILSVRC2012_val_00000199.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 252,092B, BPFP=0.4785 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 454,620B, BPFP=0.8629 +⌛️ [2/4] FRONTEND: Frontend time: 2.606s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.517s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09581849 0.83944836 + layer.39.0 9.30266794 2586.75145773 + ------------------------------------------------------------------------------------- + TOTAL 4.69924322 1293.79545304 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 706712 +BPFP 0.6707 bits/point +EBPFP 0.6707 equivalent bits/point +MSE 1293.795453 +---------------------- -------------------------------------------------------- +Time: 5.172s Load: 0.050s, Pack+Encode: 2.606s, Decode+Unpack: 2.517s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1293.7955 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03290653-ILSVRC2012_val_00000199.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03290653-ILSVRC2012_val_00000199.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03291819-ILSVRC2012_val_00000419.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03291819-ILSVRC2012_val_00000419.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 218,196B, BPFP=0.4142 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 350,704B, BPFP=0.6657 +⌛️ [2/4] FRONTEND: Frontend time: 2.606s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.506s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10621172 9.03045850 + layer.39.0 31.36357166 1644.72934888 + ------------------------------------------------------------------------------------- + TOTAL 15.73489169 826.87990369 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 568900 +BPFP 0.5399 bits/point +EBPFP 0.5399 equivalent bits/point +MSE 826.879904 +---------------------- -------------------------------------------------------- +Time: 5.183s Load: 0.071s, Pack+Encode: 2.606s, Decode+Unpack: 2.506s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 826.8799 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03291819-ILSVRC2012_val_00000419.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03291819-ILSVRC2012_val_00000419.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03314780-ILSVRC2012_val_00000624.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03314780-ILSVRC2012_val_00000624.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 291,320B, BPFP=0.5529 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 577,944B, BPFP=1.0970 +⌛️ [2/4] FRONTEND: Frontend time: 2.608s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.518s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10172509 0.88518273 + layer.39.0 35.60390853 3915.77648202 + ------------------------------------------------------------------------------------- + TOTAL 17.85281681 1958.33083237 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 869264 +BPFP 0.8250 bits/point +EBPFP 0.8250 equivalent bits/point +MSE 1958.330832 +---------------------- -------------------------------------------------------- +Time: 5.176s Load: 0.050s, Pack+Encode: 2.608s, Decode+Unpack: 2.518s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1958.3308 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03314780-ILSVRC2012_val_00000624.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03314780-ILSVRC2012_val_00000624.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03325584-ILSVRC2012_val_00001256.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03325584-ILSVRC2012_val_00001256.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 349,944B, BPFP=0.6642 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 522,692B, BPFP=0.9921 +⌛️ [2/4] FRONTEND: Frontend time: 2.623s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.509s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11348933 1.31088711 + layer.39.0 26.85401292 2815.45238095 + ------------------------------------------------------------------------------------- + TOTAL 13.48375113 1408.38163403 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 872636 +BPFP 0.8282 bits/point +EBPFP 0.8282 equivalent bits/point +MSE 1408.381634 +---------------------- -------------------------------------------------------- +Time: 5.204s Load: 0.071s, Pack+Encode: 2.623s, Decode+Unpack: 2.509s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1408.3816 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03325584-ILSVRC2012_val_00001256.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03325584-ILSVRC2012_val_00001256.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03337140-ILSVRC2012_val_00000132.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03337140-ILSVRC2012_val_00000132.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 234,868B, BPFP=0.4458 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 405,904B, BPFP=0.7704 +⌛️ [2/4] FRONTEND: Frontend time: 2.628s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.519s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09852950 0.84316207 + layer.39.0 10.39905343 2521.37633625 + ------------------------------------------------------------------------------------- + TOTAL 5.24879146 1261.10974916 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 640772 +BPFP 0.6081 bits/point +EBPFP 0.6081 equivalent bits/point +MSE 1261.109749 +---------------------- -------------------------------------------------------- +Time: 5.198s Load: 0.050s, Pack+Encode: 2.628s, Decode+Unpack: 2.519s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1261.1097 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03337140-ILSVRC2012_val_00000132.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03337140-ILSVRC2012_val_00000132.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03344393-ILSVRC2012_val_00000288.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03344393-ILSVRC2012_val_00000288.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.056s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 253,352B, BPFP=0.4809 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 457,088B, BPFP=0.8676 +⌛️ [2/4] FRONTEND: Frontend time: 2.597s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.501s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09830858 8.99026000 + layer.39.0 109.00505649 3719.53085520 + ------------------------------------------------------------------------------------- + TOTAL 54.55168253 1864.26055760 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 710440 +BPFP 0.6742 bits/point +EBPFP 0.6742 equivalent bits/point +MSE 1864.260558 +---------------------- -------------------------------------------------------- +Time: 5.154s Load: 0.056s, Pack+Encode: 2.597s, Decode+Unpack: 2.501s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1864.2606 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03344393-ILSVRC2012_val_00000288.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03344393-ILSVRC2012_val_00000288.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03345487-ILSVRC2012_val_00000764.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03345487-ILSVRC2012_val_00000764.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 305,580B, BPFP=0.5800 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 527,880B, BPFP=1.0020 +⌛️ [2/4] FRONTEND: Frontend time: 2.618s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.493s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10639974 1.70880631 + layer.39.0 14.55993569 3812.69606414 + ------------------------------------------------------------------------------------- + TOTAL 7.33316771 1907.20243523 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 833460 +BPFP 0.7910 bits/point +EBPFP 0.7910 equivalent bits/point +MSE 1907.202435 +---------------------- -------------------------------------------------------- +Time: 5.161s Load: 0.050s, Pack+Encode: 2.618s, Decode+Unpack: 2.493s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1907.2024 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03345487-ILSVRC2012_val_00000764.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03345487-ILSVRC2012_val_00000764.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03347037-ILSVRC2012_val_00000743.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03347037-ILSVRC2012_val_00000743.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 403,592B, BPFP=0.7661 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 570,916B, BPFP=1.0836 +⌛️ [2/4] FRONTEND: Frontend time: 2.637s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.522s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14351733 88.69121265 + layer.39.0 355.98426871 3644.74611273 + ------------------------------------------------------------------------------------- + TOTAL 178.06389302 1866.71866269 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 974508 +BPFP 0.9248 bits/point +EBPFP 0.9248 equivalent bits/point +MSE 1866.718663 +---------------------- -------------------------------------------------------- +Time: 5.231s Load: 0.071s, Pack+Encode: 2.637s, Decode+Unpack: 2.522s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1866.7187 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03347037-ILSVRC2012_val_00000743.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03347037-ILSVRC2012_val_00000743.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03355925-ILSVRC2012_val_00000445.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03355925-ILSVRC2012_val_00000445.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 222,552B, BPFP=0.4224 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 298,280B, BPFP=0.5662 +⌛️ [2/4] FRONTEND: Frontend time: 2.617s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.501s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09979894 0.85711528 + layer.39.0 9.06502540 1277.38119534 + ------------------------------------------------------------------------------------- + TOTAL 4.58241217 639.11915531 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 520832 +BPFP 0.4943 bits/point +EBPFP 0.4943 equivalent bits/point +MSE 639.119155 +---------------------- -------------------------------------------------------- +Time: 5.169s Load: 0.051s, Pack+Encode: 2.617s, Decode+Unpack: 2.501s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 639.1192 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03355925-ILSVRC2012_val_00000445.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03355925-ILSVRC2012_val_00000445.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03376595-ILSVRC2012_val_00001616.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03376595-ILSVRC2012_val_00001616.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 339,220B, BPFP=0.6439 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 692,612B, BPFP=1.3146 +⌛️ [2/4] FRONTEND: Frontend time: 2.622s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.517s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09988844 0.91075308 + layer.39.0 1408.20760447 5520.33624879 + ------------------------------------------------------------------------------------- + TOTAL 704.15374646 2760.62350093 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1031832 +BPFP 0.9793 bits/point +EBPFP 0.9793 equivalent bits/point +MSE 2760.623501 +---------------------- -------------------------------------------------------- +Time: 5.210s Load: 0.071s, Pack+Encode: 2.622s, Decode+Unpack: 2.517s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2760.6235 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03376595-ILSVRC2012_val_00001616.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03376595-ILSVRC2012_val_00001616.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03379051-ILSVRC2012_val_00002562.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03379051-ILSVRC2012_val_00002562.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.081s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 327,124B, BPFP=0.6209 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 638,548B, BPFP=1.2120 +⌛️ [2/4] FRONTEND: Frontend time: 2.651s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.530s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10889592 24.93492051 + layer.39.0 102.95462828 5049.68075802 + ------------------------------------------------------------------------------------- + TOTAL 51.53176210 2537.30783926 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 965672 +BPFP 0.9165 bits/point +EBPFP 0.9165 equivalent bits/point +MSE 2537.307839 +---------------------- -------------------------------------------------------- +Time: 5.262s Load: 0.081s, Pack+Encode: 2.651s, Decode+Unpack: 2.530s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2537.3078 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03379051-ILSVRC2012_val_00002562.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03379051-ILSVRC2012_val_00002562.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388043-ILSVRC2012_val_00001018.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388043-ILSVRC2012_val_00001018.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 282,352B, BPFP=0.5359 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 428,808B, BPFP=0.8139 +⌛️ [2/4] FRONTEND: Frontend time: 2.613s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.523s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09747427 121.86459852 + layer.39.0 21.12933142 2942.52356657 + ------------------------------------------------------------------------------------- + TOTAL 10.61340285 1532.19408254 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 711160 +BPFP 0.6749 bits/point +EBPFP 0.6749 equivalent bits/point +MSE 1532.194083 +---------------------- -------------------------------------------------------- +Time: 5.187s Load: 0.051s, Pack+Encode: 2.613s, Decode+Unpack: 2.523s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1532.1941 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388043-ILSVRC2012_val_00001018.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03388043-ILSVRC2012_val_00001018.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388183-ILSVRC2012_val_00002799.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388183-ILSVRC2012_val_00002799.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 331,888B, BPFP=0.6300 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 557,648B, BPFP=1.0585 +⌛️ [2/4] FRONTEND: Frontend time: 2.616s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.541s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10066175 63.37177326 + layer.39.0 786.68810739 4118.38095238 + ------------------------------------------------------------------------------------- + TOTAL 393.39438457 2090.87636282 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 889536 +BPFP 0.8442 bits/point +EBPFP 0.8442 equivalent bits/point +MSE 2090.876363 +---------------------- -------------------------------------------------------- +Time: 5.229s Load: 0.072s, Pack+Encode: 2.616s, Decode+Unpack: 2.541s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2090.8764 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388183-ILSVRC2012_val_00002799.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03388183-ILSVRC2012_val_00002799.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388549-ILSVRC2012_val_00002945.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388549-ILSVRC2012_val_00002945.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 290,572B, BPFP=0.5515 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 585,180B, BPFP=1.1107 +⌛️ [2/4] FRONTEND: Frontend time: 2.626s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.547s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09849939 0.87498428 + layer.39.0 10.79426799 3393.32798834 + ------------------------------------------------------------------------------------- + TOTAL 5.44638369 1697.10148631 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 875752 +BPFP 0.8311 bits/point +EBPFP 0.8311 equivalent bits/point +MSE 1697.101486 +---------------------- -------------------------------------------------------- +Time: 5.224s Load: 0.050s, Pack+Encode: 2.626s, Decode+Unpack: 2.547s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1697.1015 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388549-ILSVRC2012_val_00002945.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03388549-ILSVRC2012_val_00002945.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03393912-ILSVRC2012_val_00000047.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03393912-ILSVRC2012_val_00000047.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 283,464B, BPFP=0.5380 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 694,520B, BPFP=1.3183 +⌛️ [2/4] FRONTEND: Frontend time: 2.624s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.525s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09729456 8.98605480 + layer.39.0 38.26720800 5454.57240039 + ------------------------------------------------------------------------------------- + TOTAL 19.18225128 2731.77922760 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 977984 +BPFP 0.9281 bits/point +EBPFP 0.9281 equivalent bits/point +MSE 2731.779228 +---------------------- -------------------------------------------------------- +Time: 5.202s Load: 0.052s, Pack+Encode: 2.624s, Decode+Unpack: 2.525s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2731.7792 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03393912-ILSVRC2012_val_00000047.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03393912-ILSVRC2012_val_00000047.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03394916-ILSVRC2012_val_00000957.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03394916-ILSVRC2012_val_00000957.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 267,244B, BPFP=0.5073 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 432,248B, BPFP=0.8204 +⌛️ [2/4] FRONTEND: Frontend time: 2.624s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.519s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10421823 12.46999134 + layer.39.0 9.72561820 2439.22497570 + ------------------------------------------------------------------------------------- + TOTAL 4.91491822 1225.84748352 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 699492 +BPFP 0.6638 bits/point +EBPFP 0.6638 equivalent bits/point +MSE 1225.847484 +---------------------- -------------------------------------------------------- +Time: 5.205s Load: 0.062s, Pack+Encode: 2.624s, Decode+Unpack: 2.519s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1225.8475 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03394916-ILSVRC2012_val_00000957.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03394916-ILSVRC2012_val_00000957.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03404251-ILSVRC2012_val_00000641.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03404251-ILSVRC2012_val_00000641.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 278,500B, BPFP=0.5286 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 581,384B, BPFP=1.1035 +⌛️ [2/4] FRONTEND: Frontend time: 2.623s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.527s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10764784 111.50117681 + layer.39.0 585.45553936 3311.76749271 + ------------------------------------------------------------------------------------- + TOTAL 292.78159360 1711.63433476 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 859884 +BPFP 0.8161 bits/point +EBPFP 0.8161 equivalent bits/point +MSE 1711.634335 +---------------------- -------------------------------------------------------- +Time: 5.222s Load: 0.071s, Pack+Encode: 2.623s, Decode+Unpack: 2.527s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1711.6343 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03404251-ILSVRC2012_val_00000641.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03404251-ILSVRC2012_val_00000641.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03417042-ILSVRC2012_val_00001144.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03417042-ILSVRC2012_val_00001144.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 286,840B, BPFP=0.5444 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 566,020B, BPFP=1.0744 +⌛️ [2/4] FRONTEND: Frontend time: 2.617s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.504s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10091509 12.11272511 + layer.39.0 202.93364310 3883.95432459 + ------------------------------------------------------------------------------------- + TOTAL 101.51727910 1948.03352485 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 852860 +BPFP 0.8094 bits/point +EBPFP 0.8094 equivalent bits/point +MSE 1948.033525 +---------------------- -------------------------------------------------------- +Time: 5.173s Load: 0.051s, Pack+Encode: 2.617s, Decode+Unpack: 2.504s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1948.0335 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03417042-ILSVRC2012_val_00001144.zst + to output-fixed/dinov3-total/lambda0.01/elic-featurecoding-8bit-layerwise/cls_in1kval/n03417042-ILSVRC2012_val_00001144.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 0.7618 bits/point +Avg EBPFP 0.7618 equivalent bits/point +Avg MSE 1696.214405 +Avg Time 5.201s +------------------------ ---------------------------- diff --git a/lambda0.01/hyperprior-featurecoding-8bit-individual/cls_in1ka/dtufc_hyperprior-featurecoding_dinov3-total_individual.log b/lambda0.01/hyperprior-featurecoding-8bit-individual/cls_in1ka/dtufc_hyperprior-featurecoding_dinov3-total_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..402e73aedb733bb174a0bacb04be52a3ebde7ed4 --- /dev/null +++ b/lambda0.01/hyperprior-featurecoding-8bit-individual/cls_in1ka/dtufc_hyperprior-featurecoding_dinov3-total_individual.log @@ -0,0 +1,9344 @@ +Experiment: dtufc_hyperprior-featurecoding_dinov3-total_individual +Log file: output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/dtufc_hyperprior-featurecoding_dinov3-total_individual.log +DTUFCCodecConfig: + arch: hyperprior-featurecoding + handler: dinov3-total + checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.01_epochs600_lr0.0001_bs360_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.01_epochs600_lr0.0001_bs360_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 599 +Loaded hyperprior-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.9' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.19' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.29' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.39' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json +Loaded per-key mappings: model=dinov3-total + Keys: ['layer.9', 'layer.19', 'layer.29', 'layer.39'] +---------------- ----------------------------------------------------------------------------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +Checkpoint codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.01_epochs600_lr0.0001_bs360_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-DINOv3/imagenet1k-a +Output output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka +---------------- ----------------------------------------------------------------------------------------------------------------------------- +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01498041-0.006639_koala _ American bullfrog_0.6658246.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01498041-0.006639_koala _ American bullfrog_0.6658246.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.088s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 445,384B, BPFP=0.8454 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 450,588B, BPFP=0.8553 +⌛️ [2/4] FRONTEND: Frontend time: 0.881s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.077s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09594801 283.04057337 + layer.39.0 58.94484178 2800.63848397 + ------------------------------------------------------------------------------------- + TOTAL 29.52039490 1541.83952867 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 895972 +BPFP 0.8503 bits/point +EBPFP 0.8503 equivalent bits/point +MSE 1541.839529 +---------------------- -------------------------------------------------------- +Time: 2.046s Load: 0.088s, Pack+Encode: 0.881s, Decode+Unpack: 1.077s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1541.8395 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01498041-0.006639_koala _ American bullfrog_0.6658246.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01498041-0.006639_koala _ American bullfrog_0.6658246.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01531178-0.000765_fox squirrel _ ant_0.9486805.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01531178-0.000765_fox squirrel _ ant_0.9486805.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 431,316B, BPFP=0.8187 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 489,648B, BPFP=0.9294 +⌛️ [2/4] FRONTEND: Frontend time: 14.360s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.010s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09773727 298.57841351 + layer.39.0 17.17825445 1856.88921283 + ------------------------------------------------------------------------------------- + TOTAL 8.63799586 1077.73381317 + (elements=8,429,568) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 8429568 +Total Bytes 920964 +BPFP 0.8740 bits/point +EBPFP 0.8740 equivalent bits/point +MSE 1077.733813 +---------------------- --------------------------------------------------------- +Time: 15.439s Load: 0.069s, Pack+Encode: 14.360s, Decode+Unpack: 1.010s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1077.7338 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01531178-0.000765_fox squirrel _ ant_0.9486805.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01531178-0.000765_fox squirrel _ ant_0.9486805.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01534433-0.004573_stingray _ stingray_0.97124094.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01534433-0.004573_stingray _ stingray_0.97124094.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 325,812B, BPFP=0.6184 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 370,188B, BPFP=0.7026 +⌛️ [2/4] FRONTEND: Frontend time: 0.573s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.043s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09515371 25.19388894 + layer.39.0 6.87362484 1368.18768222 + ------------------------------------------------------------------------------------- + TOTAL 3.48438928 696.69078558 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 696000 +BPFP 0.6605 bits/point +EBPFP 0.6605 equivalent bits/point +MSE 696.690786 +---------------------- -------------------------------------------------------- +Time: 1.685s Load: 0.069s, Pack+Encode: 0.573s, Decode+Unpack: 1.043s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 696.6908 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01534433-0.004573_stingray _ stingray_0.97124094.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01534433-0.004573_stingray _ stingray_0.97124094.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01558993-0.000522_bow _ bow_0.9033333.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01558993-0.000522_bow _ bow_0.9033333.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 458,596B, BPFP=0.8705 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 386,468B, BPFP=0.7335 +⌛️ [2/4] FRONTEND: Frontend time: 0.531s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.056s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09874929 1363.10082604 + layer.39.0 7.31778236 1387.19193392 + ------------------------------------------------------------------------------------- + TOTAL 3.70826583 1375.14637998 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 845064 +BPFP 0.8020 bits/point +EBPFP 0.8020 equivalent bits/point +MSE 1375.146380 +---------------------- -------------------------------------------------------- +Time: 1.637s Load: 0.051s, Pack+Encode: 0.531s, Decode+Unpack: 1.056s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1375.1464 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01558993-0.000522_bow _ bow_0.9033333.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01558993-0.000522_bow _ bow_0.9033333.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01580077-0.004583_African bush elephant _ African bush elephant_0.59939015.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01580077-0.004583_African bush elephant _ African bush elephant_0.59939015.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 430,568B, BPFP=0.8173 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 435,800B, BPFP=0.8272 +⌛️ [2/4] FRONTEND: Frontend time: 0.522s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.021s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10720986 607.53055151 + layer.39.0 24.46209533 1821.66229349 + ------------------------------------------------------------------------------------- + TOTAL 12.28465260 1214.59642250 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 866368 +BPFP 0.8222 bits/point +EBPFP 0.8222 equivalent bits/point +MSE 1214.596422 +---------------------- -------------------------------------------------------- +Time: 1.603s Load: 0.060s, Pack+Encode: 0.522s, Decode+Unpack: 1.021s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1214.5964 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01580077-0.004583_African bush elephant _ African bush elephant_0.59939015.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01580077-0.004583_African bush elephant _ African bush elephant_0.59939015.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01614925-0.049724_white-headed capuchin _ white-headed capuchin_0.9309422.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01614925-0.049724_white-headed capuchin _ white-headed capuchin_0.9309422.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 474,592B, BPFP=0.9008 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 400,964B, BPFP=0.7611 +⌛️ [2/4] FRONTEND: Frontend time: 0.591s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.073s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09739119 605.09797133 + layer.39.0 8.81423010 1543.44727891 + ------------------------------------------------------------------------------------- + TOTAL 4.45581065 1074.27262512 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 875556 +BPFP 0.8309 bits/point +EBPFP 0.8309 equivalent bits/point +MSE 1074.272625 +---------------------- -------------------------------------------------------- +Time: 1.735s Load: 0.071s, Pack+Encode: 0.591s, Decode+Unpack: 1.073s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1074.2726 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01614925-0.049724_white-headed capuchin _ white-headed capuchin_0.9309422.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01614925-0.049724_white-headed capuchin _ white-headed capuchin_0.9309422.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01631663-0.004606_American bullfrog _ American bullfrog_0.8789855.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01631663-0.004606_American bullfrog _ American bullfrog_0.8789855.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 399,636B, BPFP=0.7585 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 449,072B, BPFP=0.8524 +⌛️ [2/4] FRONTEND: Frontend time: 0.495s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.008s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09716670 125.27929118 + layer.39.0 20.45897868 1775.36856171 + ------------------------------------------------------------------------------------- + TOTAL 10.27807269 950.32392645 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 848708 +BPFP 0.8055 bits/point +EBPFP 0.8055 equivalent bits/point +MSE 950.323926 +---------------------- -------------------------------------------------------- +Time: 1.554s Load: 0.051s, Pack+Encode: 0.495s, Decode+Unpack: 1.008s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 950.3239 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01631663-0.004606_American bullfrog _ American bullfrog_0.8789855.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01631663-0.004606_American bullfrog _ American bullfrog_0.8789855.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01669191-0.029754_sandal _ sandal_0.38198605.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01669191-0.029754_sandal _ sandal_0.38198605.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 534,320B, BPFP=1.0142 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 398,560B, BPFP=0.7565 +⌛️ [2/4] FRONTEND: Frontend time: 0.528s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.031s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10877632 1559.90111759 + layer.39.0 13.16500205 1528.73214286 + ------------------------------------------------------------------------------------- + TOTAL 6.63688918 1544.31663022 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 932880 +BPFP 0.8853 bits/point +EBPFP 0.8853 equivalent bits/point +MSE 1544.316630 +---------------------- -------------------------------------------------------- +Time: 1.628s Load: 0.069s, Pack+Encode: 0.528s, Decode+Unpack: 1.031s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1544.3166 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01669191-0.029754_sandal _ sandal_0.38198605.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01669191-0.029754_sandal _ sandal_0.38198605.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770081-0.000571_syringe _ syringe_0.7369336.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770081-0.000571_syringe _ syringe_0.7369336.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 355,244B, BPFP=0.6743 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 458,096B, BPFP=0.8695 +⌛️ [2/4] FRONTEND: Frontend time: 0.558s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.000s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09508557 75.17616466 + layer.39.0 60.03878538 1838.99271137 + ------------------------------------------------------------------------------------- + TOTAL 30.06693547 957.08443802 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 813340 +BPFP 0.7719 bits/point +EBPFP 0.7719 equivalent bits/point +MSE 957.084438 +---------------------- -------------------------------------------------------- +Time: 1.608s Load: 0.050s, Pack+Encode: 0.558s, Decode+Unpack: 1.000s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 957.0844 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770081-0.000571_syringe _ syringe_0.7369336.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01770081-0.000571_syringe _ syringe_0.7369336.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770393-0.000908_harvestman _ harvestman_0.8782497.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770393-0.000908_harvestman _ harvestman_0.8782497.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 437,040B, BPFP=0.8295 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 483,148B, BPFP=0.9171 +⌛️ [2/4] FRONTEND: Frontend time: 0.513s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.014s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11350316 393.07719874 + layer.39.0 19.73148992 1933.63945578 + ------------------------------------------------------------------------------------- + TOTAL 9.92249654 1163.35832726 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 920188 +BPFP 0.8733 bits/point +EBPFP 0.8733 equivalent bits/point +MSE 1163.358327 +---------------------- -------------------------------------------------------- +Time: 1.579s Load: 0.052s, Pack+Encode: 0.513s, Decode+Unpack: 1.014s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1163.3583 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770393-0.000908_harvestman _ harvestman_0.8782497.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01770393-0.000908_harvestman _ harvestman_0.8782497.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01784675-0.027853_syringe _ syringe_0.9584382.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01784675-0.027853_syringe _ syringe_0.9584382.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 520,248B, BPFP=0.9875 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 369,212B, BPFP=0.7008 +⌛️ [2/4] FRONTEND: Frontend time: 0.510s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.013s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11002613 1171.97837707 + layer.39.0 26.08665877 2867.75583090 + ------------------------------------------------------------------------------------- + TOTAL 13.09834245 2019.86710398 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 889460 +BPFP 0.8441 bits/point +EBPFP 0.8441 equivalent bits/point +MSE 2019.867104 +---------------------- -------------------------------------------------------- +Time: 1.595s Load: 0.072s, Pack+Encode: 0.510s, Decode+Unpack: 1.013s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2019.8671 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01784675-0.027853_syringe _ syringe_0.9584382.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01784675-0.027853_syringe _ syringe_0.9584382.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01819313-0.053742_koala _ koala_0.98647016.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01819313-0.053742_koala _ koala_0.98647016.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 509,156B, BPFP=0.9664 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 470,312B, BPFP=0.8927 +⌛️ [2/4] FRONTEND: Frontend time: 0.517s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.023s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14565475 1084.45493197 + layer.39.0 25.01023445 2537.53838678 + ------------------------------------------------------------------------------------- + TOTAL 12.57794460 1810.99665938 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 979468 +BPFP 0.9296 bits/point +EBPFP 0.9296 equivalent bits/point +MSE 1810.996659 +---------------------- -------------------------------------------------------- +Time: 1.609s Load: 0.069s, Pack+Encode: 0.517s, Decode+Unpack: 1.023s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1810.9967 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01819313-0.053742_koala _ koala_0.98647016.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01819313-0.053742_koala _ koala_0.98647016.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01820546-0.012522_toucan _ toucan_0.63882655.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01820546-0.012522_toucan _ toucan_0.63882655.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 420,252B, BPFP=0.7977 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 468,184B, BPFP=0.8887 +⌛️ [2/4] FRONTEND: Frontend time: 0.512s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.005s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09696376 271.59945943 + layer.39.0 16.65489097 2290.94363460 + ------------------------------------------------------------------------------------- + TOTAL 8.37592737 1281.27154701 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 888436 +BPFP 0.8432 bits/point +EBPFP 0.8432 equivalent bits/point +MSE 1281.271547 +---------------------- -------------------------------------------------------- +Time: 1.568s Load: 0.051s, Pack+Encode: 0.512s, Decode+Unpack: 1.005s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1281.2715 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01820546-0.012522_toucan _ toucan_0.63882655.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01820546-0.012522_toucan _ toucan_0.63882655.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01833805-0.013248_toucan _ lorikeet_0.77773976.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01833805-0.013248_toucan _ lorikeet_0.77773976.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.068s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 401,184B, BPFP=0.7615 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 483,760B, BPFP=0.9182 +⌛️ [2/4] FRONTEND: Frontend time: 0.509s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.010s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09866240 100.14470967 + layer.39.0 7.67772963 1635.19715743 + ------------------------------------------------------------------------------------- + TOTAL 3.88819601 867.67093355 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 884944 +BPFP 0.8398 bits/point +EBPFP 0.8398 equivalent bits/point +MSE 867.670934 +---------------------- -------------------------------------------------------- +Time: 1.587s Load: 0.068s, Pack+Encode: 0.509s, Decode+Unpack: 1.010s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 867.6709 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01833805-0.013248_toucan _ lorikeet_0.77773976.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01833805-0.013248_toucan _ lorikeet_0.77773976.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01843383-0.013605_white-headed capuchin _ white-headed capuchin_0.9984768.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01843383-0.013605_white-headed capuchin _ white-headed capuchin_0.9984768.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 484,556B, BPFP=0.9197 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 466,372B, BPFP=0.8852 +⌛️ [2/4] FRONTEND: Frontend time: 0.532s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.058s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11910487 691.65057094 + layer.39.0 9.20068692 2213.76554908 + ------------------------------------------------------------------------------------- + TOTAL 4.65989589 1452.70806001 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 950928 +BPFP 0.9025 bits/point +EBPFP 0.9025 equivalent bits/point +MSE 1452.708060 +---------------------- -------------------------------------------------------- +Time: 1.660s Load: 0.070s, Pack+Encode: 0.532s, Decode+Unpack: 1.058s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1452.7081 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01843383-0.013605_white-headed capuchin _ white-headed capuchin_0.9984768.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01843383-0.013605_white-headed capuchin _ white-headed capuchin_0.9984768.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01924916-0.000644_jay _ jay_0.82223135.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01924916-0.000644_jay _ jay_0.82223135.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 498,624B, BPFP=0.9464 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 408,388B, BPFP=0.7752 +⌛️ [2/4] FRONTEND: Frontend time: 0.518s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.031s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11488669 983.58801020 + layer.39.0 141.08750911 1749.13313897 + ------------------------------------------------------------------------------------- + TOTAL 70.60119790 1366.36057459 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 907012 +BPFP 0.8608 bits/point +EBPFP 0.8608 equivalent bits/point +MSE 1366.360575 +---------------------- -------------------------------------------------------- +Time: 1.621s Load: 0.072s, Pack+Encode: 0.518s, Decode+Unpack: 1.031s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1366.3606 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01924916-0.000644_jay _ jay_0.82223135.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01924916-0.000644_jay _ jay_0.82223135.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01944390-0.002567_American robin _ American robin_0.5629079.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01944390-0.002567_American robin _ American robin_0.5629079.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 443,672B, BPFP=0.8421 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 471,332B, BPFP=0.8946 +⌛️ [2/4] FRONTEND: Frontend time: 0.517s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.006s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10732387 795.09997570 + layer.39.0 16.74672581 1753.66277940 + ------------------------------------------------------------------------------------- + TOTAL 8.42702484 1274.38137755 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 915004 +BPFP 0.8684 bits/point +EBPFP 0.8684 equivalent bits/point +MSE 1274.381378 +---------------------- -------------------------------------------------------- +Time: 1.575s Load: 0.051s, Pack+Encode: 0.517s, Decode+Unpack: 1.006s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1274.3814 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01944390-0.002567_American robin _ American robin_0.5629079.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01944390-0.002567_American robin _ American robin_0.5629079.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01985128-0.001579_centipede _ centipede_0.85936093.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01985128-0.001579_centipede _ centipede_0.85936093.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 432,964B, BPFP=0.8218 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 407,536B, BPFP=0.7735 +⌛️ [2/4] FRONTEND: Frontend time: 0.525s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.034s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09645609 236.45370202 + layer.39.0 23.47999613 2026.70711856 + ------------------------------------------------------------------------------------- + TOTAL 11.78822611 1131.58041029 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 840500 +BPFP 0.7977 bits/point +EBPFP 0.7977 equivalent bits/point +MSE 1131.580410 +---------------------- -------------------------------------------------------- +Time: 1.629s Load: 0.070s, Pack+Encode: 0.525s, Decode+Unpack: 1.034s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1131.5804 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01985128-0.001579_centipede _ centipede_0.85936093.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01985128-0.001579_centipede _ centipede_0.85936093.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02037110-0.003503_snowmobile _ snowmobile_0.5200631.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02037110-0.003503_snowmobile _ snowmobile_0.5200631.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 268,364B, BPFP=0.5094 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 370,600B, BPFP=0.7034 +⌛️ [2/4] FRONTEND: Frontend time: 0.515s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.997s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09471867 13.55323926 + layer.39.0 17.04498261 1584.78437804 + ------------------------------------------------------------------------------------- + TOTAL 8.56985064 799.16880865 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 638964 +BPFP 0.6064 bits/point +EBPFP 0.6064 equivalent bits/point +MSE 799.168809 +---------------------- -------------------------------------------------------- +Time: 1.562s Load: 0.050s, Pack+Encode: 0.515s, Decode+Unpack: 0.997s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 799.1688 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02037110-0.003503_snowmobile _ snowmobile_0.5200631.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02037110-0.003503_snowmobile _ snowmobile_0.5200631.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02123394-0.015363_marmot _ marmot_0.82052565.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02123394-0.015363_marmot _ marmot_0.82052565.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 385,212B, BPFP=0.7312 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 456,440B, BPFP=0.8664 +⌛️ [2/4] FRONTEND: Frontend time: 0.530s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.012s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10209646 306.83087342 + layer.39.0 11.38238543 1873.98858115 + ------------------------------------------------------------------------------------- + TOTAL 5.74224095 1090.40972728 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 841652 +BPFP 0.7988 bits/point +EBPFP 0.7988 equivalent bits/point +MSE 1090.409727 +---------------------- -------------------------------------------------------- +Time: 1.602s Load: 0.061s, Pack+Encode: 0.530s, Decode+Unpack: 1.012s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1090.4097 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02123394-0.015363_marmot _ marmot_0.82052565.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02123394-0.015363_marmot _ marmot_0.82052565.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02165456-0.000157_corn _ corn_0.9868978.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02165456-0.000157_corn _ corn_0.9868978.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 457,972B, BPFP=0.8693 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 485,020B, BPFP=0.9206 +⌛️ [2/4] FRONTEND: Frontend time: 0.588s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.054s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10346756 650.25838192 + layer.39.0 776.17699223 2747.55515063 + ------------------------------------------------------------------------------------- + TOTAL 388.14022989 1698.90676628 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 942992 +BPFP 0.8949 bits/point +EBPFP 0.8949 equivalent bits/point +MSE 1698.906766 +---------------------- -------------------------------------------------------- +Time: 1.711s Load: 0.069s, Pack+Encode: 0.588s, Decode+Unpack: 1.054s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1698.9068 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02165456-0.000157_corn _ corn_0.9868978.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02165456-0.000157_corn _ corn_0.9868978.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02219486-0.000060_cliff _ cliff_0.99684334.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02219486-0.000060_cliff _ cliff_0.99684334.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 402,020B, BPFP=0.7631 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 362,624B, BPFP=0.6883 +⌛️ [2/4] FRONTEND: Frontend time: 0.521s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.008s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09584527 141.13339711 + layer.39.0 31.94620460 1816.72230321 + ------------------------------------------------------------------------------------- + TOTAL 16.02102494 978.92785016 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 764644 +BPFP 0.7257 bits/point +EBPFP 0.7257 equivalent bits/point +MSE 978.927850 +---------------------- -------------------------------------------------------- +Time: 1.579s Load: 0.050s, Pack+Encode: 0.521s, Decode+Unpack: 1.008s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 978.9279 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02219486-0.000060_cliff _ cliff_0.99684334.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02219486-0.000060_cliff _ cliff_0.99684334.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02226429-0.000085_stick insect _ stick insect_0.99900275.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02226429-0.000085_stick insect _ stick insect_0.99900275.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 414,300B, BPFP=0.7864 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 474,372B, BPFP=0.9004 +⌛️ [2/4] FRONTEND: Frontend time: 0.541s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.022s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09547379 171.39979653 + layer.39.0 19.16722850 2499.01652089 + ------------------------------------------------------------------------------------- + TOTAL 9.63135114 1335.20815871 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 888672 +BPFP 0.8434 bits/point +EBPFP 0.8434 equivalent bits/point +MSE 1335.208159 +---------------------- -------------------------------------------------------- +Time: 1.614s Load: 0.051s, Pack+Encode: 0.541s, Decode+Unpack: 1.022s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1335.2082 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02226429-0.000085_stick insect _ stick insect_0.99900275.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02226429-0.000085_stick insect _ stick insect_0.99900275.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02231487-0.000080_manhole cover _ manhole cover_0.7554716.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02231487-0.000080_manhole cover _ manhole cover_0.7554716.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 397,876B, BPFP=0.7552 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 481,356B, BPFP=0.9137 +⌛️ [2/4] FRONTEND: Frontend time: 0.522s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.007s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09512618 61.90508078 + layer.39.0 210.79875790 2394.80587949 + ------------------------------------------------------------------------------------- + TOTAL 105.44694204 1228.35548014 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 879232 +BPFP 0.8344 bits/point +EBPFP 0.8344 equivalent bits/point +MSE 1228.355480 +---------------------- -------------------------------------------------------- +Time: 1.590s Load: 0.061s, Pack+Encode: 0.522s, Decode+Unpack: 1.007s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1228.3555 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02231487-0.000080_manhole cover _ manhole cover_0.7554716.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02231487-0.000080_manhole cover _ manhole cover_0.7554716.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02233338-0.002901_manhole cover _ manhole cover_0.69458175.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02233338-0.002901_manhole cover _ manhole cover_0.69458175.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 361,728B, BPFP=0.6866 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 435,072B, BPFP=0.8258 +⌛️ [2/4] FRONTEND: Frontend time: 0.525s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.006s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09539769 49.83823038 + layer.39.0 58.97704841 1832.09426628 + ------------------------------------------------------------------------------------- + TOTAL 29.53622305 940.96624833 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 796800 +BPFP 0.7562 bits/point +EBPFP 0.7562 equivalent bits/point +MSE 940.966248 +---------------------- -------------------------------------------------------- +Time: 1.581s Load: 0.051s, Pack+Encode: 0.525s, Decode+Unpack: 1.006s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 940.9662 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02233338-0.002901_manhole cover _ manhole cover_0.69458175.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02233338-0.002901_manhole cover _ manhole cover_0.69458175.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02236044-0.000522_sundial _ sundial_0.96381366.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02236044-0.000522_sundial _ sundial_0.96381366.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 453,752B, BPFP=0.8613 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 503,496B, BPFP=0.9557 +⌛️ [2/4] FRONTEND: Frontend time: 0.528s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.009s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09795647 568.43142614 + layer.39.0 53.12385356 2268.64868805 + ------------------------------------------------------------------------------------- + TOTAL 26.61090502 1418.54005709 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 957248 +BPFP 0.9085 bits/point +EBPFP 0.9085 equivalent bits/point +MSE 1418.540057 +---------------------- -------------------------------------------------------- +Time: 1.607s Load: 0.070s, Pack+Encode: 0.528s, Decode+Unpack: 1.009s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1418.5401 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02236044-0.000522_sundial _ sundial_0.96381366.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02236044-0.000522_sundial _ sundial_0.96381366.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02259212-0.000032_chain _ chain_0.6590295.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02259212-0.000032_chain _ chain_0.6590295.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 400,388B, BPFP=0.7600 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 486,732B, BPFP=0.9239 +⌛️ [2/4] FRONTEND: Frontend time: 0.522s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.025s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09523673 149.94047619 + layer.39.0 80.66082058 2445.14625850 + ------------------------------------------------------------------------------------- + TOTAL 40.37802865 1297.54336735 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 887120 +BPFP 0.8419 bits/point +EBPFP 0.8419 equivalent bits/point +MSE 1297.543367 +---------------------- -------------------------------------------------------- +Time: 1.617s Load: 0.070s, Pack+Encode: 0.522s, Decode+Unpack: 1.025s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1297.5434 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02259212-0.000032_chain _ chain_0.6590295.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02259212-0.000032_chain _ chain_0.6590295.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02279972-0.000576_apron _ apron_0.7661352.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02279972-0.000576_apron _ apron_0.7661352.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 418,996B, BPFP=0.7953 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 427,676B, BPFP=0.8118 +⌛️ [2/4] FRONTEND: Frontend time: 0.567s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.002s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12772729 662.91350826 + layer.39.0 1038.59135083 3274.66861030 + ------------------------------------------------------------------------------------- + TOTAL 519.35953906 1968.79105928 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 846672 +BPFP 0.8035 bits/point +EBPFP 0.8035 equivalent bits/point +MSE 1968.791059 +---------------------- -------------------------------------------------------- +Time: 1.640s Load: 0.070s, Pack+Encode: 0.567s, Decode+Unpack: 1.002s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1968.7911 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02279972-0.000576_apron _ apron_0.7661352.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02279972-0.000576_apron _ apron_0.7661352.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02280649-0.001599_custard apple _ leafhopper_0.9128452.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02280649-0.001599_custard apple _ leafhopper_0.9128452.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 382,220B, BPFP=0.7255 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 458,244B, BPFP=0.8698 +⌛️ [2/4] FRONTEND: Frontend time: 0.522s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.001s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09488542 62.40669415 + layer.39.0 1031.59973275 3510.04203110 + ------------------------------------------------------------------------------------- + TOTAL 515.84730909 1786.22436262 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 840464 +BPFP 0.7976 bits/point +EBPFP 0.7976 equivalent bits/point +MSE 1786.224363 +---------------------- -------------------------------------------------------- +Time: 1.574s Load: 0.051s, Pack+Encode: 0.522s, Decode+Unpack: 1.001s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1786.2244 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02280649-0.001599_custard apple _ leafhopper_0.9128452.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02280649-0.001599_custard apple _ leafhopper_0.9128452.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02317335-0.033681_flatworm _ flatworm_0.6070924.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02317335-0.033681_flatworm _ flatworm_0.6070924.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 382,952B, BPFP=0.7269 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 395,968B, BPFP=0.7516 +⌛️ [2/4] FRONTEND: Frontend time: 0.519s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.007s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09575805 99.06574192 + layer.39.0 62.35741238 2285.44727891 + ------------------------------------------------------------------------------------- + TOTAL 31.22658522 1192.25651042 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 778920 +BPFP 0.7392 bits/point +EBPFP 0.7392 equivalent bits/point +MSE 1192.256510 +---------------------- -------------------------------------------------------- +Time: 1.578s Load: 0.052s, Pack+Encode: 0.519s, Decode+Unpack: 1.007s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1192.2565 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02317335-0.033681_flatworm _ flatworm_0.6070924.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02317335-0.033681_flatworm _ flatworm_0.6070924.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02325366-0.000350_flatworm _ flatworm_0.87634724.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02325366-0.000350_flatworm _ flatworm_0.87634724.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 322,040B, BPFP=0.6113 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 380,648B, BPFP=0.7225 +⌛️ [2/4] FRONTEND: Frontend time: 0.520s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.012s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09712043 12.92441178 + layer.39.0 30.59439155 1772.41788144 + ------------------------------------------------------------------------------------- + TOTAL 15.34575599 892.67114661 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 702688 +BPFP 0.6669 bits/point +EBPFP 0.6669 equivalent bits/point +MSE 892.671147 +---------------------- -------------------------------------------------------- +Time: 1.602s Load: 0.070s, Pack+Encode: 0.520s, Decode+Unpack: 1.012s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 892.6711 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02325366-0.000350_flatworm _ flatworm_0.87634724.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02325366-0.000350_flatworm _ flatworm_0.87634724.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02346627-0.011107_fountain _ skunk_0.28641737.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02346627-0.011107_fountain _ skunk_0.28641737.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 291,456B, BPFP=0.5532 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 330,184B, BPFP=0.6267 +⌛️ [2/4] FRONTEND: Frontend time: 0.517s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.990s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09705289 62.28475386 + layer.39.0 9.52721088 1507.34232264 + ------------------------------------------------------------------------------------- + TOTAL 4.81213189 784.81353825 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 621640 +BPFP 0.5900 bits/point +EBPFP 0.5900 equivalent bits/point +MSE 784.813538 +---------------------- -------------------------------------------------------- +Time: 1.577s Load: 0.070s, Pack+Encode: 0.517s, Decode+Unpack: 0.990s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 784.8135 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02346627-0.011107_fountain _ skunk_0.28641737.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02346627-0.011107_fountain _ skunk_0.28641737.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02445715-0.036432_shovel _ tarantula_0.26785344.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02445715-0.036432_shovel _ tarantula_0.26785344.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 457,892B, BPFP=0.8691 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 361,536B, BPFP=0.6862 +⌛️ [2/4] FRONTEND: Frontend time: 0.517s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.025s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09708806 445.35926871 + layer.39.0 8.00606437 1474.48445092 + ------------------------------------------------------------------------------------- + TOTAL 4.05157622 959.92185982 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 819428 +BPFP 0.7777 bits/point +EBPFP 0.7777 equivalent bits/point +MSE 959.921860 +---------------------- -------------------------------------------------------- +Time: 1.611s Load: 0.069s, Pack+Encode: 0.517s, Decode+Unpack: 1.025s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 959.9219 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02445715-0.036432_shovel _ tarantula_0.26785344.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02445715-0.036432_shovel _ tarantula_0.26785344.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02454379-0.082010_koala _ koala_0.7052893.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02454379-0.082010_koala _ koala_0.7052893.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 544,380B, BPFP=1.0333 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 418,596B, BPFP=0.7945 +⌛️ [2/4] FRONTEND: Frontend time: 0.514s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.002s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14585212 1899.46829446 + layer.39.0 44.19989826 1802.17529155 + ------------------------------------------------------------------------------------- + TOTAL 22.17287519 1850.82179300 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 962976 +BPFP 0.9139 bits/point +EBPFP 0.9139 equivalent bits/point +MSE 1850.821793 +---------------------- -------------------------------------------------------- +Time: 1.576s Load: 0.059s, Pack+Encode: 0.514s, Decode+Unpack: 1.002s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1850.8218 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02454379-0.082010_koala _ koala_0.7052893.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02454379-0.082010_koala _ koala_0.7052893.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02782093-0.007542_American alligator _ American alligator_0.49872985.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02782093-0.007542_American alligator _ American alligator_0.49872985.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 331,580B, BPFP=0.6294 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 427,836B, BPFP=0.8121 +⌛️ [2/4] FRONTEND: Frontend time: 0.518s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.016s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09848133 61.34024614 + layer.39.0 9.18780844 1657.63666181 + ------------------------------------------------------------------------------------- + TOTAL 4.64314488 859.48845398 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 759416 +BPFP 0.7207 bits/point +EBPFP 0.7207 equivalent bits/point +MSE 859.488454 +---------------------- -------------------------------------------------------- +Time: 1.604s Load: 0.070s, Pack+Encode: 0.518s, Decode+Unpack: 1.016s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 859.4885 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02782093-0.007542_American alligator _ American alligator_0.49872985.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02782093-0.007542_American alligator _ American alligator_0.49872985.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02787622-0.004599_marimba _ accordion_0.25991488.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02787622-0.004599_marimba _ accordion_0.25991488.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 460,276B, BPFP=0.8736 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 508,580B, BPFP=0.9653 +⌛️ [2/4] FRONTEND: Frontend time: 0.519s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.003s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12856446 786.01870748 + layer.39.0 1004.59450923 3818.37657920 + ------------------------------------------------------------------------------------- + TOTAL 502.36153685 2302.19764334 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 968856 +BPFP 0.9195 bits/point +EBPFP 0.9195 equivalent bits/point +MSE 2302.197643 +---------------------- -------------------------------------------------------- +Time: 1.573s Load: 0.050s, Pack+Encode: 0.519s, Decode+Unpack: 1.003s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2302.1976 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02787622-0.004599_marimba _ accordion_0.25991488.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02787622-0.004599_marimba _ accordion_0.25991488.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02793495-0.000130_flatworm _ stingray_0.5528234.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02793495-0.000130_flatworm _ stingray_0.5528234.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 345,184B, BPFP=0.6552 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 333,464B, BPFP=0.6329 +⌛️ [2/4] FRONTEND: Frontend time: 0.512s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.992s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09706621 50.41015245 + layer.39.0 8.05872662 1445.78024781 + ------------------------------------------------------------------------------------- + TOTAL 4.07789641 748.09520013 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 678648 +BPFP 0.6441 bits/point +EBPFP 0.6441 equivalent bits/point +MSE 748.095200 +---------------------- -------------------------------------------------------- +Time: 1.574s Load: 0.070s, Pack+Encode: 0.512s, Decode+Unpack: 0.992s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 748.0952 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02793495-0.000130_flatworm _ stingray_0.5528234.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02793495-0.000130_flatworm _ stingray_0.5528234.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02797295-0.002775_hair dryer _ box turtle_0.2407761.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02797295-0.002775_hair dryer _ box turtle_0.2407761.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 462,884B, BPFP=0.8786 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 530,064B, BPFP=1.0061 +⌛️ [2/4] FRONTEND: Frontend time: 0.522s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.004s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11161610 479.42881438 + layer.39.0 373.09438776 2776.60884354 + ------------------------------------------------------------------------------------- + TOTAL 186.60300193 1628.01882896 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 992948 +BPFP 0.9423 bits/point +EBPFP 0.9423 equivalent bits/point +MSE 1628.018829 +---------------------- -------------------------------------------------------- +Time: 1.596s Load: 0.069s, Pack+Encode: 0.522s, Decode+Unpack: 1.004s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1628.0188 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02797295-0.002775_hair dryer _ box turtle_0.2407761.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02797295-0.002775_hair dryer _ box turtle_0.2407761.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02814860-0.006340_fountain _ fountain_0.7891514.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02814860-0.006340_fountain _ fountain_0.7891514.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 257,860B, BPFP=0.4894 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 414,812B, BPFP=0.7873 +⌛️ [2/4] FRONTEND: Frontend time: 0.517s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.990s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.04615183 12.96366409 + layer.39.0 7.48662090 1630.35932945 + ------------------------------------------------------------------------------------- + TOTAL 7.76638637 821.66149677 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 672672 +BPFP 0.6384 bits/point +EBPFP 0.6384 equivalent bits/point +MSE 821.661497 +---------------------- -------------------------------------------------------- +Time: 1.558s Load: 0.051s, Pack+Encode: 0.517s, Decode+Unpack: 0.990s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 821.6615 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02814860-0.006340_fountain _ fountain_0.7891514.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02814860-0.006340_fountain _ fountain_0.7891514.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02879718-0.003578_maraca _ maraca_0.6809677.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02879718-0.003578_maraca _ maraca_0.6809677.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 412,156B, BPFP=0.7823 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 548,108B, BPFP=1.0404 +⌛️ [2/4] FRONTEND: Frontend time: 0.519s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.005s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10989876 466.41265792 + layer.39.0 33.03751367 2474.15621963 + ------------------------------------------------------------------------------------- + TOTAL 16.57370621 1470.28443878 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 960264 +BPFP 0.9113 bits/point +EBPFP 0.9113 equivalent bits/point +MSE 1470.284439 +---------------------- -------------------------------------------------------- +Time: 1.576s Load: 0.052s, Pack+Encode: 0.519s, Decode+Unpack: 1.005s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1470.2844 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02879718-0.003578_maraca _ maraca_0.6809677.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02879718-0.003578_maraca _ maraca_0.6809677.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02883205-0.000262_syringe _ syringe_0.7098205.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02883205-0.000262_syringe _ syringe_0.7098205.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 345,576B, BPFP=0.6559 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 442,700B, BPFP=0.8403 +⌛️ [2/4] FRONTEND: Frontend time: 0.505s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.015s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09610580 110.52753735 + layer.39.0 8.14318931 1606.89334305 + ------------------------------------------------------------------------------------- + TOTAL 4.11964755 858.71044020 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 788276 +BPFP 0.7481 bits/point +EBPFP 0.7481 equivalent bits/point +MSE 858.710440 +---------------------- -------------------------------------------------------- +Time: 1.581s Load: 0.061s, Pack+Encode: 0.505s, Decode+Unpack: 1.015s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 858.7104 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02883205-0.000262_syringe _ syringe_0.7098205.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02883205-0.000262_syringe _ syringe_0.7098205.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02906734-0.000163_stethoscope _ stethoscope_0.9290994.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02906734-0.000163_stethoscope _ stethoscope_0.9290994.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 416,880B, BPFP=0.7913 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 479,100B, BPFP=0.9094 +⌛️ [2/4] FRONTEND: Frontend time: 0.590s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.059s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12024398 518.95456754 + layer.39.0 47.23105336 2468.40306122 + ------------------------------------------------------------------------------------- + TOTAL 23.67564867 1493.67881438 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 895980 +BPFP 0.8503 bits/point +EBPFP 0.8503 equivalent bits/point +MSE 1493.678814 +---------------------- -------------------------------------------------------- +Time: 1.719s Load: 0.070s, Pack+Encode: 0.590s, Decode+Unpack: 1.059s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1493.6788 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02906734-0.000163_stethoscope _ stethoscope_0.9290994.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02906734-0.000163_stethoscope _ stethoscope_0.9290994.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02948072-0.002303_digital clock _ digital clock_0.928374.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02948072-0.002303_digital clock _ digital clock_0.928374.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 385,148B, BPFP=0.7310 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 480,224B, BPFP=0.9115 +⌛️ [2/4] FRONTEND: Frontend time: 0.531s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.006s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09670976 61.90656508 + layer.39.0 81.62974520 2716.73129252 + ------------------------------------------------------------------------------------- + TOTAL 40.86322748 1389.31892880 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 865372 +BPFP 0.8213 bits/point +EBPFP 0.8213 equivalent bits/point +MSE 1389.318929 +---------------------- -------------------------------------------------------- +Time: 1.596s Load: 0.060s, Pack+Encode: 0.531s, Decode+Unpack: 1.006s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1389.3189 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02948072-0.002303_digital clock _ digital clock_0.928374.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02948072-0.002303_digital clock _ digital clock_0.928374.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02999410-0.000148_chest _ chest_0.9948565.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02999410-0.000148_chest _ chest_0.9948565.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 372,720B, BPFP=0.7075 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 404,172B, BPFP=0.7672 +⌛️ [2/4] FRONTEND: Frontend time: 0.527s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.999s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10256943 137.15977284 + layer.39.0 13.72598738 1460.53972303 + ------------------------------------------------------------------------------------- + TOTAL 6.91427841 798.84974793 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 776892 +BPFP 0.7373 bits/point +EBPFP 0.7373 equivalent bits/point +MSE 798.849748 +---------------------- -------------------------------------------------------- +Time: 1.595s Load: 0.069s, Pack+Encode: 0.527s, Decode+Unpack: 0.999s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 798.8497 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02999410-0.000148_chest _ chest_0.9948565.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02999410-0.000148_chest _ chest_0.9948565.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03026506-0.001828_basketball _ basketball_0.6904969.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03026506-0.001828_basketball _ basketball_0.6904969.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 363,824B, BPFP=0.6906 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 462,920B, BPFP=0.8787 +⌛️ [2/4] FRONTEND: Frontend time: 0.521s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.000s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09484169 37.42078550 + layer.39.0 87.31533194 2025.41350826 + ------------------------------------------------------------------------------------- + TOTAL 43.70508681 1031.41714688 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 826744 +BPFP 0.7846 bits/point +EBPFP 0.7846 equivalent bits/point +MSE 1031.417147 +---------------------- -------------------------------------------------------- +Time: 1.572s Load: 0.051s, Pack+Encode: 0.521s, Decode+Unpack: 1.000s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1031.4171 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03026506-0.001828_basketball _ basketball_0.6904969.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03026506-0.001828_basketball _ basketball_0.6904969.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03124043-0.001154_bow tie _ bow tie_0.9622156.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03124043-0.001154_bow tie _ bow tie_0.9622156.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 364,868B, BPFP=0.6925 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 454,292B, BPFP=0.8623 +⌛️ [2/4] FRONTEND: Frontend time: 0.527s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.020s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09893820 269.68847182 + layer.39.0 13.24554141 1953.79919825 + ------------------------------------------------------------------------------------- + TOTAL 6.67223981 1111.74383503 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 819160 +BPFP 0.7774 bits/point +EBPFP 0.7774 equivalent bits/point +MSE 1111.743835 +---------------------- -------------------------------------------------------- +Time: 1.616s Load: 0.070s, Pack+Encode: 0.527s, Decode+Unpack: 1.020s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1111.7438 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03124043-0.001154_bow tie _ bow tie_0.9622156.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03124043-0.001154_bow tie _ bow tie_0.9622156.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03196217-0.015958_soap dispenser _ envelope_0.80274254.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03196217-0.015958_soap dispenser _ envelope_0.80274254.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 273,752B, BPFP=0.5196 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 423,936B, BPFP=0.8047 +⌛️ [2/4] FRONTEND: Frontend time: 0.521s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.007s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10340443 124.91277940 + layer.39.0 8.70910111 1850.87803693 + ------------------------------------------------------------------------------------- + TOTAL 4.40625277 987.89540816 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 697688 +BPFP 0.6621 bits/point +EBPFP 0.6621 equivalent bits/point +MSE 987.895408 +---------------------- -------------------------------------------------------- +Time: 1.578s Load: 0.050s, Pack+Encode: 0.521s, Decode+Unpack: 1.007s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 987.8954 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03196217-0.015958_soap dispenser _ envelope_0.80274254.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03196217-0.015958_soap dispenser _ envelope_0.80274254.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03223299-0.001528_hot dog _ hot dog_0.9147216.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03223299-0.001528_hot dog _ hot dog_0.9147216.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 412,272B, BPFP=0.7825 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 442,880B, BPFP=0.8406 +⌛️ [2/4] FRONTEND: Frontend time: 0.522s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.002s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10130972 394.17902697 + layer.39.0 352.09596696 2164.92079689 + ------------------------------------------------------------------------------------- + TOTAL 176.09863834 1279.54991193 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 855152 +BPFP 0.8116 bits/point +EBPFP 0.8116 equivalent bits/point +MSE 1279.549912 +---------------------- -------------------------------------------------------- +Time: 1.576s Load: 0.051s, Pack+Encode: 0.522s, Decode+Unpack: 1.002s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1279.5499 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03223299-0.001528_hot dog _ hot dog_0.9147216.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03223299-0.001528_hot dog _ hot dog_0.9147216.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03255030-0.005469_bubble _ bubble_0.9381716.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03255030-0.005469_bubble _ bubble_0.9381716.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 398,384B, BPFP=0.7562 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 536,096B, BPFP=1.0176 +⌛️ [2/4] FRONTEND: Frontend time: 0.522s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.001s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09675161 147.28501579 + layer.39.0 42.23478499 2280.77259475 + ------------------------------------------------------------------------------------- + TOTAL 21.16576830 1214.02880527 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 934480 +BPFP 0.8869 bits/point +EBPFP 0.8869 equivalent bits/point +MSE 1214.028805 +---------------------- -------------------------------------------------------- +Time: 1.575s Load: 0.052s, Pack+Encode: 0.522s, Decode+Unpack: 1.001s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1214.0288 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03255030-0.005469_bubble _ bubble_0.9381716.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03255030-0.005469_bubble _ bubble_0.9381716.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03325584-0.000773_candle _ candle_0.810919.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03325584-0.000773_candle _ candle_0.810919.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 445,368B, BPFP=0.8453 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 512,368B, BPFP=0.9725 +⌛️ [2/4] FRONTEND: Frontend time: 0.522s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.002s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10394677 640.16812439 + layer.39.0 140.58187561 3004.85568513 + ------------------------------------------------------------------------------------- + TOTAL 70.34291119 1822.51190476 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 957736 +BPFP 0.9089 bits/point +EBPFP 0.9089 equivalent bits/point +MSE 1822.511905 +---------------------- -------------------------------------------------------- +Time: 1.576s Load: 0.051s, Pack+Encode: 0.522s, Decode+Unpack: 1.002s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1822.5119 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03325584-0.000773_candle _ candle_0.810919.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03325584-0.000773_candle _ candle_0.810919.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03355925-0.004997_spider web _ spider web_0.9142101.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03355925-0.004997_spider web _ spider web_0.9142101.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 267,568B, BPFP=0.5079 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 330,076B, BPFP=0.6265 +⌛️ [2/4] FRONTEND: Frontend time: 0.518s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.006s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09873271 13.07339593 + layer.39.0 6.60211199 1523.70310982 + ------------------------------------------------------------------------------------- + TOTAL 3.35042235 768.38825287 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 597644 +BPFP 0.5672 bits/point +EBPFP 0.5672 equivalent bits/point +MSE 768.388253 +---------------------- -------------------------------------------------------- +Time: 1.575s Load: 0.051s, Pack+Encode: 0.518s, Decode+Unpack: 1.006s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 768.3883 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03355925-0.004997_spider web _ spider web_0.9142101.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03355925-0.004997_spider web _ spider web_0.9142101.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03384352-0.002273_viaduct _ viaduct_0.6161024.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03384352-0.002273_viaduct _ viaduct_0.6161024.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 392,232B, BPFP=0.7445 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 431,140B, BPFP=0.8183 +⌛️ [2/4] FRONTEND: Frontend time: 0.549s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.018s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09647940 346.01257289 + layer.39.0 175.50411504 2049.99562682 + ------------------------------------------------------------------------------------- + TOTAL 87.80029722 1198.00409985 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 823372 +BPFP 0.7814 bits/point +EBPFP 0.7814 equivalent bits/point +MSE 1198.004100 +---------------------- -------------------------------------------------------- +Time: 1.637s Load: 0.070s, Pack+Encode: 0.549s, Decode+Unpack: 1.018s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1198.0041 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03384352-0.002273_viaduct _ viaduct_0.6161024.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03384352-0.002273_viaduct _ viaduct_0.6161024.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03388043-0.005154_candle _ candle_0.9636924.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03388043-0.005154_candle _ candle_0.9636924.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 381,364B, BPFP=0.7239 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 449,224B, BPFP=0.8527 +⌛️ [2/4] FRONTEND: Frontend time: 0.566s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.999s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09640297 74.47451257 + layer.39.0 7.87377147 1700.38435374 + ------------------------------------------------------------------------------------- + TOTAL 3.98508722 887.42943316 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 830588 +BPFP 0.7883 bits/point +EBPFP 0.7883 equivalent bits/point +MSE 887.429433 +---------------------- -------------------------------------------------------- +Time: 1.636s Load: 0.071s, Pack+Encode: 0.566s, Decode+Unpack: 0.999s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 887.4294 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03388043-0.005154_candle _ candle_0.9636924.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03388043-0.005154_candle _ candle_0.9636924.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03417042-0.001187_tank _ tank_0.70379025.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03417042-0.001187_tank _ tank_0.70379025.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 349,784B, BPFP=0.6639 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 436,816B, BPFP=0.8291 +⌛️ [2/4] FRONTEND: Frontend time: 0.531s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.017s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09848782 172.05836978 + layer.39.0 16.63742104 2130.01360544 + ------------------------------------------------------------------------------------- + TOTAL 8.36795443 1151.03598761 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 786600 +BPFP 0.7465 bits/point +EBPFP 0.7465 equivalent bits/point +MSE 1151.035988 +---------------------- -------------------------------------------------------- +Time: 1.599s Load: 0.051s, Pack+Encode: 0.531s, Decode+Unpack: 1.017s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1151.0360 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03417042-0.001187_tank _ tank_0.70379025.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03417042-0.001187_tank _ tank_0.70379025.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03444034-0.002100_maraca _ maraca_0.502369.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03444034-0.002100_maraca _ maraca_0.502369.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 462,236B, BPFP=0.8774 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 568,844B, BPFP=1.0797 +⌛️ [2/4] FRONTEND: Frontend time: 0.526s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.012s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11197850 749.16065355 + layer.39.0 347.54634354 2901.45505345 + ------------------------------------------------------------------------------------- + TOTAL 173.82916102 1825.30785350 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1031080 +BPFP 0.9785 bits/point +EBPFP 0.9785 equivalent bits/point +MSE 1825.307853 +---------------------- -------------------------------------------------------- +Time: 1.599s Load: 0.062s, Pack+Encode: 0.526s, Decode+Unpack: 1.012s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1825.3079 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03444034-0.002100_maraca _ maraca_0.502369.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03444034-0.002100_maraca _ maraca_0.502369.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03445924-0.003505_baseball player _ baseball player_0.6723684.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03445924-0.003505_baseball player _ baseball player_0.6723684.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 364,620B, BPFP=0.6921 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 477,924B, BPFP=0.9071 +⌛️ [2/4] FRONTEND: Frontend time: 0.543s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.019s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09665277 147.96296465 + layer.39.0 26.28463618 1990.29689018 + ------------------------------------------------------------------------------------- + TOTAL 13.19064447 1069.12992742 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 842544 +BPFP 0.7996 bits/point +EBPFP 0.7996 equivalent bits/point +MSE 1069.129927 +---------------------- -------------------------------------------------------- +Time: 1.612s Load: 0.051s, Pack+Encode: 0.543s, Decode+Unpack: 1.019s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1069.1299 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03445924-0.003505_baseball player _ baseball player_0.6723684.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03445924-0.003505_baseball player _ baseball player_0.6723684.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03452741-0.002771_chain _ chain_0.9575044.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03452741-0.002771_chain _ chain_0.9575044.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 438,560B, BPFP=0.8324 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 529,928B, BPFP=1.0058 +⌛️ [2/4] FRONTEND: Frontend time: 0.528s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.013s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12351380 285.15163387 + layer.39.0 42.82565370 2438.95116618 + ------------------------------------------------------------------------------------- + TOTAL 21.47458375 1362.05140002 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 968488 +BPFP 0.9191 bits/point +EBPFP 0.9191 equivalent bits/point +MSE 1362.051400 +---------------------- -------------------------------------------------------- +Time: 1.610s Load: 0.070s, Pack+Encode: 0.528s, Decode+Unpack: 1.013s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1362.0514 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03452741-0.002771_chain _ chain_0.9575044.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03452741-0.002771_chain _ chain_0.9575044.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03483316-0.004974_lighter _ lighter_0.27796906.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03483316-0.004974_lighter _ lighter_0.27796906.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 462,484B, BPFP=0.8778 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 479,992B, BPFP=0.9111 +⌛️ [2/4] FRONTEND: Frontend time: 0.507s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.016s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12993333 870.78304179 + layer.39.0 87.07173986 2047.20213800 + ------------------------------------------------------------------------------------- + TOTAL 43.60083660 1458.99258989 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 942476 +BPFP 0.8944 bits/point +EBPFP 0.8944 equivalent bits/point +MSE 1458.992590 +---------------------- -------------------------------------------------------- +Time: 1.574s Load: 0.051s, Pack+Encode: 0.507s, Decode+Unpack: 1.016s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1458.9926 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03483316-0.004974_lighter _ lighter_0.27796906.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03483316-0.004974_lighter _ lighter_0.27796906.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03584829-0.104805_golf cart _ golf cart_0.73568964.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03584829-0.104805_golf cart _ golf cart_0.73568964.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 403,944B, BPFP=0.7667 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 450,072B, BPFP=0.8543 +⌛️ [2/4] FRONTEND: Frontend time: 0.529s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.015s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09917131 306.91284014 + layer.39.0 24.34873246 2338.58916424 + ------------------------------------------------------------------------------------- + TOTAL 12.22395189 1322.75100219 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 854016 +BPFP 0.8105 bits/point +EBPFP 0.8105 equivalent bits/point +MSE 1322.751002 +---------------------- -------------------------------------------------------- +Time: 1.614s Load: 0.070s, Pack+Encode: 0.529s, Decode+Unpack: 1.015s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1322.7510 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03584829-0.104805_golf cart _ golf cart_0.73568964.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03584829-0.104805_golf cart _ golf cart_0.73568964.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03590841-0.001115_rocking chair _ rocking chair_0.2192492.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03590841-0.001115_rocking chair _ rocking chair_0.2192492.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 407,152B, BPFP=0.7728 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 415,076B, BPFP=0.7878 +⌛️ [2/4] FRONTEND: Frontend time: 0.521s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.005s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11329899 397.06666059 + layer.39.0 19.97532495 1785.13253158 + ------------------------------------------------------------------------------------- + TOTAL 10.04431197 1091.09959609 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 822228 +BPFP 0.7803 bits/point +EBPFP 0.7803 equivalent bits/point +MSE 1091.099596 +---------------------- -------------------------------------------------------- +Time: 1.585s Load: 0.060s, Pack+Encode: 0.521s, Decode+Unpack: 1.005s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1091.0996 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03590841-0.001115_rocking chair _ rocking chair_0.2192492.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03590841-0.001115_rocking chair _ rocking chair_0.2192492.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03617480-0.003238_basketball _ basketball_0.67568874.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03617480-0.003238_basketball _ basketball_0.67568874.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 491,360B, BPFP=0.9326 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 539,284B, BPFP=1.0236 +⌛️ [2/4] FRONTEND: Frontend time: 0.532s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.008s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12967051 922.14965986 + layer.39.0 57.10576865 2786.48615160 + ------------------------------------------------------------------------------------- + TOTAL 28.61771958 1854.31790573 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1030644 +BPFP 0.9781 bits/point +EBPFP 0.9781 equivalent bits/point +MSE 1854.317906 +---------------------- -------------------------------------------------------- +Time: 1.590s Load: 0.050s, Pack+Encode: 0.532s, Decode+Unpack: 1.008s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1854.3179 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03617480-0.003238_basketball _ basketball_0.67568874.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03617480-0.003238_basketball _ basketball_0.67568874.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03666591-0.004622_torch _ torch_0.99906796.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03666591-0.004622_torch _ torch_0.99906796.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 309,980B, BPFP=0.5884 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 413,916B, BPFP=0.7856 +⌛️ [2/4] FRONTEND: Frontend time: 0.518s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.994s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.05477861 37.70282890 + layer.39.0 7.78975672 1655.10240525 + ------------------------------------------------------------------------------------- + TOTAL 7.92226767 846.40261707 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 723896 +BPFP 0.6870 bits/point +EBPFP 0.6870 equivalent bits/point +MSE 846.402617 +---------------------- -------------------------------------------------------- +Time: 1.572s Load: 0.061s, Pack+Encode: 0.518s, Decode+Unpack: 0.994s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 846.4026 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03666591-0.004622_torch _ torch_0.99906796.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03666591-0.004622_torch _ torch_0.99906796.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03670208-0.003899_hair dryer _ grand piano_0.7359066.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03670208-0.003899_hair dryer _ grand piano_0.7359066.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 400,596B, BPFP=0.7604 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 520,508B, BPFP=0.9880 +⌛️ [2/4] FRONTEND: Frontend time: 0.531s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.012s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11232473 406.93850219 + layer.39.0 36.60432231 2489.80976676 + ------------------------------------------------------------------------------------- + TOTAL 18.35832352 1448.37413448 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 921104 +BPFP 0.8742 bits/point +EBPFP 0.8742 equivalent bits/point +MSE 1448.374134 +---------------------- -------------------------------------------------------- +Time: 1.594s Load: 0.050s, Pack+Encode: 0.531s, Decode+Unpack: 1.012s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1448.3741 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03670208-0.003899_hair dryer _ grand piano_0.7359066.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03670208-0.003899_hair dryer _ grand piano_0.7359066.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03717622-0.001175_sundial _ sundial_0.9998197.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03717622-0.001175_sundial _ sundial_0.9998197.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 471,464B, BPFP=0.8949 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 514,836B, BPFP=0.9772 +⌛️ [2/4] FRONTEND: Frontend time: 0.529s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.018s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13381931 839.75151846 + layer.39.0 773.52204810 3205.03206997 + ------------------------------------------------------------------------------------- + TOTAL 386.82793371 2022.39179422 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 986300 +BPFP 0.9360 bits/point +EBPFP 0.9360 equivalent bits/point +MSE 2022.391794 +---------------------- -------------------------------------------------------- +Time: 1.617s Load: 0.070s, Pack+Encode: 0.529s, Decode+Unpack: 1.018s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2022.3918 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03717622-0.001175_sundial _ sundial_0.9998197.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03717622-0.001175_sundial _ sundial_0.9998197.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03720891-0.001854_African bush elephant _ African bush elephant_0.722115.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03720891-0.001854_African bush elephant _ African bush elephant_0.722115.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 402,212B, BPFP=0.7634 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 558,372B, BPFP=1.0598 +⌛️ [2/4] FRONTEND: Frontend time: 0.502s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.025s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09642763 148.94735483 + layer.39.0 155.23232507 2670.34766764 + ------------------------------------------------------------------------------------- + TOTAL 77.66437635 1409.64751124 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 960584 +BPFP 0.9116 bits/point +EBPFP 0.9116 equivalent bits/point +MSE 1409.647511 +---------------------- -------------------------------------------------------- +Time: 1.597s Load: 0.069s, Pack+Encode: 0.502s, Decode+Unpack: 1.025s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1409.6475 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03720891-0.001854_African bush elephant _ African bush elephant_0.722115.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03720891-0.001854_African bush elephant _ African bush elephant_0.722115.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03721384-0.003327_chain _ chain_0.5599652.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03721384-0.003327_chain _ chain_0.5599652.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 319,500B, BPFP=0.6064 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 404,564B, BPFP=0.7679 +⌛️ [2/4] FRONTEND: Frontend time: 0.548s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.992s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09561452 136.50025814 + layer.39.0 742.66502672 2801.62876579 + ------------------------------------------------------------------------------------- + TOTAL 371.38032062 1469.06451197 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 724064 +BPFP 0.6872 bits/point +EBPFP 0.6872 equivalent bits/point +MSE 1469.064512 +---------------------- -------------------------------------------------------- +Time: 1.591s Load: 0.051s, Pack+Encode: 0.548s, Decode+Unpack: 0.992s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1469.0645 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03721384-0.003327_chain _ chain_0.5599652.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03721384-0.003327_chain _ chain_0.5599652.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03724870-0.001366_Christmas stocking _ Christmas stocking_0.5709616.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03724870-0.001366_Christmas stocking _ Christmas stocking_0.5709616.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 419,036B, BPFP=0.7954 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 447,828B, BPFP=0.8500 +⌛️ [2/4] FRONTEND: Frontend time: 0.496s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.995s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10329660 394.45596453 + layer.39.0 513.92243683 2932.24489796 + ------------------------------------------------------------------------------------- + TOTAL 257.01286671 1663.35043124 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 866864 +BPFP 0.8227 bits/point +EBPFP 0.8227 equivalent bits/point +MSE 1663.350431 +---------------------- -------------------------------------------------------- +Time: 1.542s Load: 0.051s, Pack+Encode: 0.496s, Decode+Unpack: 0.995s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1663.3504 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03724870-0.001366_Christmas stocking _ Christmas stocking_0.5709616.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03724870-0.001366_Christmas stocking _ Christmas stocking_0.5709616.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03775071-0.000145_Christmas stocking _ Christmas stocking_0.9986047.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03775071-0.000145_Christmas stocking _ Christmas stocking_0.9986047.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 409,112B, BPFP=0.7765 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 506,248B, BPFP=0.9609 +⌛️ [2/4] FRONTEND: Frontend time: 0.497s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.001s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09700392 209.41613520 + layer.39.0 284.92189018 2653.77162293 + ------------------------------------------------------------------------------------- + TOTAL 142.50944705 1431.59387907 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 915360 +BPFP 0.8687 bits/point +EBPFP 0.8687 equivalent bits/point +MSE 1431.593879 +---------------------- -------------------------------------------------------- +Time: 1.551s Load: 0.052s, Pack+Encode: 0.497s, Decode+Unpack: 1.001s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1431.5939 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03775071-0.000145_Christmas stocking _ Christmas stocking_0.9986047.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03775071-0.000145_Christmas stocking _ Christmas stocking_0.9986047.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03788195-0.005975_steam locomotive _ steam locomotive_0.42419377.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03788195-0.005975_steam locomotive _ steam locomotive_0.42419377.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 471,232B, BPFP=0.8944 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 471,084B, BPFP=0.8942 +⌛️ [2/4] FRONTEND: Frontend time: 0.566s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.025s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10790903 700.41186832 + layer.39.0 10.34781284 2101.55369291 + ------------------------------------------------------------------------------------- + TOTAL 5.22786094 1400.98278061 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 942316 +BPFP 0.8943 bits/point +EBPFP 0.8943 equivalent bits/point +MSE 1400.982781 +---------------------- -------------------------------------------------------- +Time: 1.661s Load: 0.070s, Pack+Encode: 0.566s, Decode+Unpack: 1.025s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1400.9828 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03788195-0.005975_steam locomotive _ steam locomotive_0.42419377.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03788195-0.005975_steam locomotive _ steam locomotive_0.42419377.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03837869-0.002023_lighthouse _ lighthouse_0.5898798.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03837869-0.002023_lighthouse _ lighthouse_0.5898798.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 379,748B, BPFP=0.7208 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 423,696B, BPFP=0.8042 +⌛️ [2/4] FRONTEND: Frontend time: 0.545s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.000s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12703056 333.06240889 + layer.39.0 141.21340500 2003.92893586 + ------------------------------------------------------------------------------------- + TOTAL 70.67021778 1168.49567238 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 803444 +BPFP 0.7625 bits/point +EBPFP 0.7625 equivalent bits/point +MSE 1168.495672 +---------------------- -------------------------------------------------------- +Time: 1.595s Load: 0.050s, Pack+Encode: 0.545s, Decode+Unpack: 1.000s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1168.4957 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03837869-0.002023_lighthouse _ lighthouse_0.5898798.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03837869-0.002023_lighthouse _ lighthouse_0.5898798.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03840681-0.002188_ladybug _ ladybug_0.9972365.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03840681-0.002188_ladybug _ ladybug_0.9972365.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 309,712B, BPFP=0.5879 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 358,048B, BPFP=0.6796 +⌛️ [2/4] FRONTEND: Frontend time: 0.556s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.007s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09487485 37.09114963 + layer.39.0 29.40353574 1579.24003887 + ------------------------------------------------------------------------------------- + TOTAL 14.74920530 808.16559425 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 667760 +BPFP 0.6337 bits/point +EBPFP 0.6337 equivalent bits/point +MSE 808.165594 +---------------------- -------------------------------------------------------- +Time: 1.614s Load: 0.051s, Pack+Encode: 0.556s, Decode+Unpack: 1.007s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 808.1656 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03840681-0.002188_ladybug _ ladybug_0.9972365.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03840681-0.002188_ladybug _ ladybug_0.9972365.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03888257-0.004083_rugby ball _ snail_0.41588444.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03888257-0.004083_rugby ball _ snail_0.41588444.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 353,388B, BPFP=0.6708 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 373,208B, BPFP=0.7084 +⌛️ [2/4] FRONTEND: Frontend time: 0.506s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.001s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10005040 75.69288296 + layer.39.0 7.47115060 1417.26324101 + ------------------------------------------------------------------------------------- + TOTAL 3.78560050 746.47806198 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 726596 +BPFP 0.6896 bits/point +EBPFP 0.6896 equivalent bits/point +MSE 746.478062 +---------------------- -------------------------------------------------------- +Time: 1.568s Load: 0.062s, Pack+Encode: 0.506s, Decode+Unpack: 1.001s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 746.4781 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03888257-0.004083_rugby ball _ snail_0.41588444.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03888257-0.004083_rugby ball _ snail_0.41588444.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03891332-0.003727_syringe _ syringe_0.93799996.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03891332-0.003727_syringe _ syringe_0.93799996.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 367,224B, BPFP=0.6970 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 461,308B, BPFP=0.8756 +⌛️ [2/4] FRONTEND: Frontend time: 0.538s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.997s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09617506 99.60256469 + layer.39.0 18.45312310 2311.63022352 + ------------------------------------------------------------------------------------- + TOTAL 9.27464908 1205.61639410 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 828532 +BPFP 0.7863 bits/point +EBPFP 0.7863 equivalent bits/point +MSE 1205.616394 +---------------------- -------------------------------------------------------- +Time: 1.586s Load: 0.051s, Pack+Encode: 0.538s, Decode+Unpack: 0.997s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1205.6164 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03891332-0.003727_syringe _ syringe_0.93799996.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03891332-0.003727_syringe _ syringe_0.93799996.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03982430-0.005102_couch _ couch_0.9976859.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03982430-0.005102_couch _ couch_0.9976859.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 277,740B, BPFP=0.5272 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 413,148B, BPFP=0.7842 +⌛️ [2/4] FRONTEND: Frontend time: 0.515s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.009s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09691652 86.06811073 + layer.39.0 169.89398081 2253.98177843 + ------------------------------------------------------------------------------------- + TOTAL 84.99544866 1170.02494458 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 690888 +BPFP 0.6557 bits/point +EBPFP 0.6557 equivalent bits/point +MSE 1170.024945 +---------------------- -------------------------------------------------------- +Time: 1.593s Load: 0.069s, Pack+Encode: 0.515s, Decode+Unpack: 1.009s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1170.0249 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03982430-0.005102_couch _ couch_0.9976859.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03982430-0.005102_couch _ couch_0.9976859.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04033901-0.007476_envelope _ envelope_0.9990971.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04033901-0.007476_envelope _ envelope_0.9990971.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 325,704B, BPFP=0.6182 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 405,940B, BPFP=0.7705 +⌛️ [2/4] FRONTEND: Frontend time: 0.555s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.014s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10364226 98.67406918 + layer.39.0 7.34252906 1630.92067541 + ------------------------------------------------------------------------------------- + TOTAL 3.72308566 864.79737230 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 731644 +BPFP 0.6944 bits/point +EBPFP 0.6944 equivalent bits/point +MSE 864.797372 +---------------------- -------------------------------------------------------- +Time: 1.619s Load: 0.051s, Pack+Encode: 0.555s, Decode+Unpack: 1.014s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 864.7974 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04033901-0.007476_envelope _ envelope_0.9990971.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04033901-0.007476_envelope _ envelope_0.9990971.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04039381-0.000054_hair dryer _ hair dryer_0.5667548.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04039381-0.000054_hair dryer _ hair dryer_0.5667548.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 348,068B, BPFP=0.6607 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 492,268B, BPFP=0.9344 +⌛️ [2/4] FRONTEND: Frontend time: 0.575s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.045s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09588603 25.04409811 + layer.39.0 26.21653304 2025.02308066 + ------------------------------------------------------------------------------------- + TOTAL 13.15620954 1025.03358938 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 840336 +BPFP 0.7975 bits/point +EBPFP 0.7975 equivalent bits/point +MSE 1025.033589 +---------------------- -------------------------------------------------------- +Time: 1.690s Load: 0.070s, Pack+Encode: 0.575s, Decode+Unpack: 1.045s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1025.0336 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04039381-0.000054_hair dryer _ hair dryer_0.5667548.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04039381-0.000054_hair dryer _ hair dryer_0.5667548.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04118538-0.005804_cowboy boot _ cowboy boot_0.21208441.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04118538-0.005804_cowboy boot _ cowboy boot_0.21208441.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 406,524B, BPFP=0.7716 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 460,960B, BPFP=0.8749 +⌛️ [2/4] FRONTEND: Frontend time: 0.539s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.997s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09664223 306.58029640 + layer.39.0 8.64007266 1806.98250729 + ------------------------------------------------------------------------------------- + TOTAL 4.36835744 1056.78140185 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 867484 +BPFP 0.8233 bits/point +EBPFP 0.8233 equivalent bits/point +MSE 1056.781402 +---------------------- -------------------------------------------------------- +Time: 1.587s Load: 0.051s, Pack+Encode: 0.539s, Decode+Unpack: 0.997s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1056.7814 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04118538-0.005804_cowboy boot _ cowboy boot_0.21208441.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04118538-0.005804_cowboy boot _ cowboy boot_0.21208441.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04146614-0.008793_marimba _ marimba_0.54555196.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04146614-0.008793_marimba _ marimba_0.54555196.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 396,676B, BPFP=0.7529 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 444,812B, BPFP=0.8443 +⌛️ [2/4] FRONTEND: Frontend time: 0.550s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.997s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09774729 285.12976798 + layer.39.0 155.07908163 3474.64820214 + ------------------------------------------------------------------------------------- + TOTAL 77.58841446 1879.88898506 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 841488 +BPFP 0.7986 bits/point +EBPFP 0.7986 equivalent bits/point +MSE 1879.888985 +---------------------- -------------------------------------------------------- +Time: 1.617s Load: 0.070s, Pack+Encode: 0.550s, Decode+Unpack: 0.997s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1879.8890 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04146614-0.008793_marimba _ marimba_0.54555196.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04146614-0.008793_marimba _ marimba_0.54555196.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04179913-0.006024_guacamole _ custard apple_0.5550306.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04179913-0.006024_guacamole _ custard apple_0.5550306.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 435,652B, BPFP=0.8269 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 502,988B, BPFP=0.9547 +⌛️ [2/4] FRONTEND: Frontend time: 0.518s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.012s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11409367 491.43910957 + layer.39.0 68.43204871 2135.32871720 + ------------------------------------------------------------------------------------- + TOTAL 34.27307119 1313.38391339 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 938640 +BPFP 0.8908 bits/point +EBPFP 0.8908 equivalent bits/point +MSE 1313.383913 +---------------------- -------------------------------------------------------- +Time: 1.581s Load: 0.051s, Pack+Encode: 0.518s, Decode+Unpack: 1.012s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1313.3839 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04179913-0.006024_guacamole _ custard apple_0.5550306.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04179913-0.006024_guacamole _ custard apple_0.5550306.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04208210-0.007213_doormat _ doormat_0.81736016.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04208210-0.007213_doormat _ doormat_0.81736016.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 466,876B, BPFP=0.8862 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 455,960B, BPFP=0.8654 +⌛️ [2/4] FRONTEND: Frontend time: 0.519s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.996s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10601767 736.21167396 + layer.39.0 349.44518343 2403.59596696 + ------------------------------------------------------------------------------------- + TOTAL 174.77560055 1569.90382046 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 922836 +BPFP 0.8758 bits/point +EBPFP 0.8758 equivalent bits/point +MSE 1569.903820 +---------------------- -------------------------------------------------------- +Time: 1.584s Load: 0.070s, Pack+Encode: 0.519s, Decode+Unpack: 0.996s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1569.9038 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04208210-0.007213_doormat _ doormat_0.81736016.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04208210-0.007213_doormat _ doormat_0.81736016.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04270147-0.000435_rotary dial telephone _ rotary dial telephone_0.9268941.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04270147-0.000435_rotary dial telephone _ rotary dial telephone_0.9268941.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 343,764B, BPFP=0.6525 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 513,392B, BPFP=0.9745 +⌛️ [2/4] FRONTEND: Frontend time: 0.516s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.003s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09464848 37.11080995 + layer.39.0 229.78908528 2480.27696793 + ------------------------------------------------------------------------------------- + TOTAL 114.94186688 1258.69388894 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 857156 +BPFP 0.8135 bits/point +EBPFP 0.8135 equivalent bits/point +MSE 1258.693889 +---------------------- -------------------------------------------------------- +Time: 1.589s Load: 0.070s, Pack+Encode: 0.516s, Decode+Unpack: 1.003s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1258.6939 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04270147-0.000435_rotary dial telephone _ rotary dial telephone_0.9268941.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04270147-0.000435_rotary dial telephone _ rotary dial telephone_0.9268941.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04317175-0.006894_bow tie _ bow tie_0.95877784.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04317175-0.006894_bow tie _ bow tie_0.95877784.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 376,556B, BPFP=0.7147 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 488,828B, BPFP=0.9278 +⌛️ [2/4] FRONTEND: Frontend time: 0.523s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.998s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09706025 175.24287840 + layer.39.0 10.87108806 2044.86710398 + ------------------------------------------------------------------------------------- + TOTAL 5.48407415 1110.05499119 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 865384 +BPFP 0.8213 bits/point +EBPFP 0.8213 equivalent bits/point +MSE 1110.054991 +---------------------- -------------------------------------------------------- +Time: 1.582s Load: 0.061s, Pack+Encode: 0.523s, Decode+Unpack: 0.998s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1110.0550 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04317175-0.006894_bow tie _ bow tie_0.95877784.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04317175-0.006894_bow tie _ bow tie_0.95877784.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04347754-0.001405_viaduct _ viaduct_0.8445672.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04347754-0.001405_viaduct _ viaduct_0.8445672.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 288,284B, BPFP=0.5472 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 445,204B, BPFP=0.8450 +⌛️ [2/4] FRONTEND: Frontend time: 0.518s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.999s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09586499 25.36268715 + layer.39.0 267.55718537 2689.94047619 + ------------------------------------------------------------------------------------- + TOTAL 133.82652518 1357.65158167 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 733488 +BPFP 0.6961 bits/point +EBPFP 0.6961 equivalent bits/point +MSE 1357.651582 +---------------------- -------------------------------------------------------- +Time: 1.577s Load: 0.060s, Pack+Encode: 0.518s, Decode+Unpack: 0.999s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1357.6516 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04347754-0.001405_viaduct _ viaduct_0.8445672.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04347754-0.001405_viaduct _ viaduct_0.8445672.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04355338-0.000791_envelope _ envelope_0.9969598.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04355338-0.000791_envelope _ envelope_0.9969598.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 379,100B, BPFP=0.7196 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 454,844B, BPFP=0.8633 +⌛️ [2/4] FRONTEND: Frontend time: 0.517s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.994s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10273007 198.41952138 + layer.39.0 331.89978134 2698.10714286 + ------------------------------------------------------------------------------------- + TOTAL 166.00125571 1448.26333212 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 833944 +BPFP 0.7914 bits/point +EBPFP 0.7914 equivalent bits/point +MSE 1448.263332 +---------------------- -------------------------------------------------------- +Time: 1.582s Load: 0.071s, Pack+Encode: 0.517s, Decode+Unpack: 0.994s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1448.2633 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04355338-0.000791_envelope _ envelope_0.9969598.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04355338-0.000791_envelope _ envelope_0.9969598.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04366367-0.002021_parachute _ parachute_0.9226023.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04366367-0.002021_parachute _ parachute_0.9226023.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 304,440B, BPFP=0.5779 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 378,668B, BPFP=0.7187 +⌛️ [2/4] FRONTEND: Frontend time: 0.553s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.008s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09577132 111.60269376 + layer.39.0 47.60657343 1689.77429543 + ------------------------------------------------------------------------------------- + TOTAL 23.85117238 900.68849459 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 683108 +BPFP 0.6483 bits/point +EBPFP 0.6483 equivalent bits/point +MSE 900.688495 +---------------------- -------------------------------------------------------- +Time: 1.631s Load: 0.070s, Pack+Encode: 0.553s, Decode+Unpack: 1.008s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 900.6885 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04366367-0.002021_parachute _ parachute_0.9226023.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04366367-0.002021_parachute _ parachute_0.9226023.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04376876-0.004615_lighter _ lighter_0.69176143.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04376876-0.004615_lighter _ lighter_0.69176143.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 372,108B, BPFP=0.7063 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 495,740B, BPFP=0.9410 +⌛️ [2/4] FRONTEND: Frontend time: 0.593s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.050s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09912059 234.15331936 + layer.39.0 173.01079628 2368.59985423 + ------------------------------------------------------------------------------------- + TOTAL 86.55495844 1301.37658680 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 867848 +BPFP 0.8236 bits/point +EBPFP 0.8236 equivalent bits/point +MSE 1301.376587 +---------------------- -------------------------------------------------------- +Time: 1.712s Load: 0.070s, Pack+Encode: 0.593s, Decode+Unpack: 1.050s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1301.3766 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04376876-0.004615_lighter _ lighter_0.69176143.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04376876-0.004615_lighter _ lighter_0.69176143.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04442312-0.001690_washing machine _ washing machine_0.8494714.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04442312-0.001690_washing machine _ washing machine_0.8494714.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 287,724B, BPFP=0.5461 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 426,584B, BPFP=0.8097 +⌛️ [2/4] FRONTEND: Frontend time: 0.510s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.000s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.08302300 49.52812956 + layer.39.0 28.24609944 1779.99562682 + ------------------------------------------------------------------------------------- + TOTAL 18.16456122 914.76187819 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 714308 +BPFP 0.6779 bits/point +EBPFP 0.6779 equivalent bits/point +MSE 914.761878 +---------------------- -------------------------------------------------------- +Time: 1.581s Load: 0.071s, Pack+Encode: 0.510s, Decode+Unpack: 1.000s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 914.7619 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04442312-0.001690_washing machine _ washing machine_0.8494714.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04442312-0.001690_washing machine _ washing machine_0.8494714.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04456115-0.002573_hair dryer _ envelope_0.34823084.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04456115-0.002573_hair dryer _ envelope_0.34823084.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 315,436B, BPFP=0.5987 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 470,756B, BPFP=0.8935 +⌛️ [2/4] FRONTEND: Frontend time: 0.502s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.005s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09444211 97.91377399 + layer.39.0 8.80792942 1753.98882410 + ------------------------------------------------------------------------------------- + TOTAL 4.45118577 925.95129905 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 786192 +BPFP 0.7461 bits/point +EBPFP 0.7461 equivalent bits/point +MSE 925.951299 +---------------------- -------------------------------------------------------- +Time: 1.579s Load: 0.072s, Pack+Encode: 0.502s, Decode+Unpack: 1.005s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 925.9513 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04456115-0.002573_hair dryer _ envelope_0.34823084.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04456115-0.002573_hair dryer _ envelope_0.34823084.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04509417-0.000350_sundial _ sundial_0.99936897.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04509417-0.000350_sundial _ sundial_0.99936897.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 373,112B, BPFP=0.7082 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 422,232B, BPFP=0.8014 +⌛️ [2/4] FRONTEND: Frontend time: 0.532s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.999s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10319057 249.71253948 + layer.39.0 8.14296913 1509.82689504 + ------------------------------------------------------------------------------------- + TOTAL 4.12307985 879.76971726 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 795344 +BPFP 0.7548 bits/point +EBPFP 0.7548 equivalent bits/point +MSE 879.769717 +---------------------- -------------------------------------------------------- +Time: 1.600s Load: 0.070s, Pack+Encode: 0.532s, Decode+Unpack: 0.999s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 879.7697 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04509417-0.000350_sundial _ sundial_0.99936897.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04509417-0.000350_sundial _ sundial_0.99936897.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04532670-0.000670_spider web _ spider web_0.70711935.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04532670-0.000670_spider web _ spider web_0.70711935.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 436,956B, BPFP=0.8294 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 507,372B, BPFP=0.9630 +⌛️ [2/4] FRONTEND: Frontend time: 0.510s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.031s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09618602 151.59176081 + layer.39.0 175.41615039 2362.83017493 + ------------------------------------------------------------------------------------- + TOTAL 87.75616821 1257.21096787 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 944328 +BPFP 0.8962 bits/point +EBPFP 0.8962 equivalent bits/point +MSE 1257.210968 +---------------------- -------------------------------------------------------- +Time: 1.611s Load: 0.070s, Pack+Encode: 0.510s, Decode+Unpack: 1.031s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1257.2110 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04532670-0.000670_spider web _ spider web_0.70711935.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04532670-0.000670_spider web _ spider web_0.70711935.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04540053-0.000734_balance beam _ balance beam_0.99765223.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04540053-0.000734_balance beam _ balance beam_0.99765223.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 312,988B, BPFP=0.5941 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 427,480B, BPFP=0.8114 +⌛️ [2/4] FRONTEND: Frontend time: 0.519s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.000s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09941827 62.84848002 + layer.39.0 8.11341412 1577.82227891 + ------------------------------------------------------------------------------------- + TOTAL 4.10641619 820.33537946 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 740468 +BPFP 0.7027 bits/point +EBPFP 0.7027 equivalent bits/point +MSE 820.335379 +---------------------- -------------------------------------------------------- +Time: 1.579s Load: 0.060s, Pack+Encode: 0.519s, Decode+Unpack: 1.000s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 820.3354 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04540053-0.000734_balance beam _ balance beam_0.99765223.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04540053-0.000734_balance beam _ balance beam_0.99765223.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04606251-0.003483_cliff _ cliff_0.9972174.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04606251-0.003483_cliff _ cliff_0.9972174.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 403,480B, BPFP=0.7658 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 390,704B, BPFP=0.7416 +⌛️ [2/4] FRONTEND: Frontend time: 0.516s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.988s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09940710 674.85125121 + layer.39.0 906.86880466 2844.20626822 + ------------------------------------------------------------------------------------- + TOTAL 453.48410588 1759.52875972 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 794184 +BPFP 0.7537 bits/point +EBPFP 0.7537 equivalent bits/point +MSE 1759.528760 +---------------------- -------------------------------------------------------- +Time: 1.565s Load: 0.061s, Pack+Encode: 0.516s, Decode+Unpack: 0.988s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1759.5288 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04606251-0.003483_cliff _ cliff_0.9972174.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04606251-0.003483_cliff _ cliff_0.9972174.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07583066-0.003935_maraca _ maraca_0.5370013.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07583066-0.003935_maraca _ maraca_0.5370013.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 339,572B, BPFP=0.6445 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 402,876B, BPFP=0.7647 +⌛️ [2/4] FRONTEND: Frontend time: 0.516s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.994s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12045678 295.31638727 + layer.39.0 38.29438092 2341.83551992 + ------------------------------------------------------------------------------------- + TOTAL 19.20741885 1318.57595360 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 742448 +BPFP 0.7046 bits/point +EBPFP 0.7046 equivalent bits/point +MSE 1318.575954 +---------------------- -------------------------------------------------------- +Time: 1.561s Load: 0.051s, Pack+Encode: 0.516s, Decode+Unpack: 0.994s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1318.5760 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07583066-0.003935_maraca _ maraca_0.5370013.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n07583066-0.003935_maraca _ maraca_0.5370013.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07695742-0.003226_ant _ ant_0.64413536.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07695742-0.003226_ant _ ant_0.64413536.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 518,364B, BPFP=0.9839 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 472,324B, BPFP=0.8965 +⌛️ [2/4] FRONTEND: Frontend time: 0.522s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.005s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.16263347 1286.77198737 + layer.39.0 172.10254191 2250.01457726 + ------------------------------------------------------------------------------------- + TOTAL 86.13258769 1768.39328231 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 990688 +BPFP 0.9402 bits/point +EBPFP 0.9402 equivalent bits/point +MSE 1768.393282 +---------------------- -------------------------------------------------------- +Time: 1.597s Load: 0.070s, Pack+Encode: 0.522s, Decode+Unpack: 1.005s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1768.3933 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07695742-0.003226_ant _ ant_0.64413536.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n07695742-0.003226_ant _ ant_0.64413536.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07718472-0.001330_spatula _ spatula_0.5388231.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07718472-0.001330_spatula _ spatula_0.5388231.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 372,396B, BPFP=0.7068 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 462,120B, BPFP=0.8771 +⌛️ [2/4] FRONTEND: Frontend time: 0.521s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.026s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09672572 63.86147807 + layer.39.0 34.52145211 2253.31875607 + ------------------------------------------------------------------------------------- + TOTAL 17.30908891 1158.59011707 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 834516 +BPFP 0.7920 bits/point +EBPFP 0.7920 equivalent bits/point +MSE 1158.590117 +---------------------- -------------------------------------------------------- +Time: 1.617s Load: 0.070s, Pack+Encode: 0.521s, Decode+Unpack: 1.026s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1158.5901 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07718472-0.001330_spatula _ spatula_0.5388231.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n07718472-0.001330_spatula _ spatula_0.5388231.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07831146-0.002710_guacamole _ guacamole_0.99510396.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07831146-0.002710_guacamole _ guacamole_0.99510396.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 412,316B, BPFP=0.7826 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 501,872B, BPFP=0.9526 +⌛️ [2/4] FRONTEND: Frontend time: 0.505s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.020s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09717902 433.63951652 + layer.39.0 26.55584533 2572.70213800 + ------------------------------------------------------------------------------------- + TOTAL 13.32651218 1503.17082726 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 914188 +BPFP 0.8676 bits/point +EBPFP 0.8676 equivalent bits/point +MSE 1503.170827 +---------------------- -------------------------------------------------------- +Time: 1.575s Load: 0.050s, Pack+Encode: 0.505s, Decode+Unpack: 1.020s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1503.1708 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07831146-0.002710_guacamole _ guacamole_0.99510396.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n07831146-0.002710_guacamole _ guacamole_0.99510396.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n09229709-0.002628_stethoscope _ stethoscope_0.9726458.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n09229709-0.002628_stethoscope _ stethoscope_0.9726458.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 367,396B, BPFP=0.6973 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 448,824B, BPFP=0.8519 +⌛️ [2/4] FRONTEND: Frontend time: 0.503s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.024s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10247729 171.38547741 + layer.39.0 58.71458181 2150.30369291 + ------------------------------------------------------------------------------------- + TOTAL 29.40852955 1160.84458516 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 816220 +BPFP 0.7746 bits/point +EBPFP 0.7746 equivalent bits/point +MSE 1160.844585 +---------------------- -------------------------------------------------------- +Time: 1.597s Load: 0.071s, Pack+Encode: 0.503s, Decode+Unpack: 1.024s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1160.8446 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n09229709-0.002628_stethoscope _ stethoscope_0.9726458.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n09229709-0.002628_stethoscope _ stethoscope_0.9726458.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12057211-0.000404_nail _ newt_0.31321314.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12057211-0.000404_nail _ newt_0.31321314.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 512,264B, BPFP=0.9723 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 409,640B, BPFP=0.7775 +⌛️ [2/4] FRONTEND: Frontend time: 0.556s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.003s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11577855 1317.65415452 + layer.39.0 8.72387956 1746.62973761 + ------------------------------------------------------------------------------------- + TOTAL 4.41982905 1532.14194606 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 921904 +BPFP 0.8749 bits/point +EBPFP 0.8749 equivalent bits/point +MSE 1532.141946 +---------------------- -------------------------------------------------------- +Time: 1.609s Load: 0.051s, Pack+Encode: 0.556s, Decode+Unpack: 1.003s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1532.1419 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12057211-0.000404_nail _ newt_0.31321314.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n12057211-0.000404_nail _ newt_0.31321314.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12144580-0.002806_banana _ banana_0.999156.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12144580-0.002806_banana _ banana_0.999156.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 413,100B, BPFP=0.7841 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 490,452B, BPFP=0.9309 +⌛️ [2/4] FRONTEND: Frontend time: 0.503s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.014s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09629347 309.48229470 + layer.39.0 105.38953930 3489.92128280 + ------------------------------------------------------------------------------------- + TOTAL 52.74291638 1899.70178875 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 903552 +BPFP 0.8575 bits/point +EBPFP 0.8575 equivalent bits/point +MSE 1899.701789 +---------------------- -------------------------------------------------------- +Time: 1.568s Load: 0.052s, Pack+Encode: 0.503s, Decode+Unpack: 1.014s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1899.7018 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12144580-0.002806_banana _ banana_0.999156.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n12144580-0.002806_banana _ banana_0.999156.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12267677-0.004617_pomegranate _ pomegranate_0.9159373.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12267677-0.004617_pomegranate _ pomegranate_0.9159373.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 413,960B, BPFP=0.7857 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 467,716B, BPFP=0.8878 +⌛️ [2/4] FRONTEND: Frontend time: 0.555s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.011s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10323383 285.98906706 + layer.39.0 78.12042942 2664.50874636 + ------------------------------------------------------------------------------------- + TOTAL 39.11183162 1475.24890671 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 881676 +BPFP 0.8367 bits/point +EBPFP 0.8367 equivalent bits/point +MSE 1475.248907 +---------------------- -------------------------------------------------------- +Time: 1.636s Load: 0.070s, Pack+Encode: 0.555s, Decode+Unpack: 1.011s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1475.2489 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12267677-0.004617_pomegranate _ pomegranate_0.9159373.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n12267677-0.004617_pomegranate _ pomegranate_0.9159373.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 0.8023 bits/point +Avg EBPFP 0.8023 equivalent bits/point +Avg MSE 1270.810170 +Avg Time 1.744s +------------------------ ---------------------------- diff --git a/lambda0.01/hyperprior-featurecoding-8bit-individual/cls_in1kr/dtufc_hyperprior-featurecoding_dinov3-total_individual.log b/lambda0.01/hyperprior-featurecoding-8bit-individual/cls_in1kr/dtufc_hyperprior-featurecoding_dinov3-total_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..2ba201997314c34837a959302924443222f62122 --- /dev/null +++ b/lambda0.01/hyperprior-featurecoding-8bit-individual/cls_in1kr/dtufc_hyperprior-featurecoding_dinov3-total_individual.log @@ -0,0 +1,9344 @@ +Experiment: dtufc_hyperprior-featurecoding_dinov3-total_individual +Log file: output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/dtufc_hyperprior-featurecoding_dinov3-total_individual.log +DTUFCCodecConfig: + arch: hyperprior-featurecoding + handler: dinov3-total + checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.01_epochs600_lr0.0001_bs360_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.01_epochs600_lr0.0001_bs360_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 599 +Loaded hyperprior-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.9' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.19' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.29' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.39' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json +Loaded per-key mappings: model=dinov3-total + Keys: ['layer.9', 'layer.19', 'layer.29', 'layer.39'] +---------------- ----------------------------------------------------------------------------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +Checkpoint codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.01_epochs600_lr0.0001_bs360_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-DINOv3/imagenet1k-r +Output output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr +---------------- ----------------------------------------------------------------------------------------------------------------------------- +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01443537-graffiti_3.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01443537-graffiti_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.088s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 381,944B, BPFP=0.7250 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 431,092B, BPFP=0.8182 +⌛️ [2/4] FRONTEND: Frontend time: 0.902s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.087s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09690064 134.48505831 + layer.39.0 23.14008974 2516.66715258 + ------------------------------------------------------------------------------------- + TOTAL 11.61849519 1325.57610544 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 813036 +BPFP 0.7716 bits/point +EBPFP 0.7716 equivalent bits/point +MSE 1325.576105 +---------------------- -------------------------------------------------------- +Time: 2.076s Load: 0.088s, Pack+Encode: 0.902s, Decode+Unpack: 1.087s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1325.5761 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01443537-graffiti_3.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01443537-graffiti_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01494475-misc_31.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01494475-misc_31.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 373,028B, BPFP=0.7080 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 495,840B, BPFP=0.9411 +⌛️ [2/4] FRONTEND: Frontend time: 0.568s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.067s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09558801 12.79253789 + layer.39.0 281.54433916 2816.74295432 + ------------------------------------------------------------------------------------- + TOTAL 140.81996359 1414.76774611 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 868868 +BPFP 0.8246 bits/point +EBPFP 0.8246 equivalent bits/point +MSE 1414.767746 +---------------------- -------------------------------------------------------- +Time: 1.704s Load: 0.069s, Pack+Encode: 0.568s, Decode+Unpack: 1.067s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1414.7677 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01494475-misc_31.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01494475-misc_31.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01531178-cartoon_22.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01531178-cartoon_22.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 397,976B, BPFP=0.7554 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 461,696B, BPFP=0.8763 +⌛️ [2/4] FRONTEND: Frontend time: 0.590s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.028s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10319715 183.49453353 + layer.39.0 12.97479918 1532.36516035 + ------------------------------------------------------------------------------------- + TOTAL 6.53899817 857.92984694 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 859672 +BPFP 0.8159 bits/point +EBPFP 0.8159 equivalent bits/point +MSE 857.929847 +---------------------- -------------------------------------------------------- +Time: 1.689s Load: 0.070s, Pack+Encode: 0.590s, Decode+Unpack: 1.028s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 857.9298 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01531178-cartoon_22.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01531178-cartoon_22.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01534433-painting_9.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01534433-painting_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 448,088B, BPFP=0.8505 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 406,408B, BPFP=0.7714 +⌛️ [2/4] FRONTEND: Frontend time: 0.622s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.052s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10660143 874.94934402 + layer.39.0 8.42910859 1776.96720117 + ------------------------------------------------------------------------------------- + TOTAL 4.26785501 1325.95827259 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 854496 +BPFP 0.8110 bits/point +EBPFP 0.8110 equivalent bits/point +MSE 1325.958273 +---------------------- -------------------------------------------------------- +Time: 1.726s Load: 0.052s, Pack+Encode: 0.622s, Decode+Unpack: 1.052s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1325.9583 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01534433-painting_9.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01534433-painting_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01632777-toy_21.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01632777-toy_21.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.079s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 355,032B, BPFP=0.6739 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 471,748B, BPFP=0.8954 +⌛️ [2/4] FRONTEND: Frontend time: 0.520s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.004s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09516629 62.25746325 + layer.39.0 31.73491595 2906.20699708 + ------------------------------------------------------------------------------------- + TOTAL 15.91504112 1484.23223017 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 826780 +BPFP 0.7846 bits/point +EBPFP 0.7846 equivalent bits/point +MSE 1484.232230 +---------------------- -------------------------------------------------------- +Time: 1.602s Load: 0.079s, Pack+Encode: 0.520s, Decode+Unpack: 1.004s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1484.2322 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01632777-toy_21.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01632777-toy_21.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01748264-misc_18.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01748264-misc_18.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 416,364B, BPFP=0.7903 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 417,424B, BPFP=0.7923 +⌛️ [2/4] FRONTEND: Frontend time: 0.532s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.011s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.16139180 1030.89042760 + layer.39.0 362.83485180 2644.71647230 + ------------------------------------------------------------------------------------- + TOTAL 181.49812180 1837.80344995 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 833788 +BPFP 0.7913 bits/point +EBPFP 0.7913 equivalent bits/point +MSE 1837.803450 +---------------------- -------------------------------------------------------- +Time: 1.595s Load: 0.052s, Pack+Encode: 0.532s, Decode+Unpack: 1.011s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1837.8034 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01748264-misc_18.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01748264-misc_18.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01784675-painting_2.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01784675-painting_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 480,368B, BPFP=0.9118 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 424,648B, BPFP=0.8060 +⌛️ [2/4] FRONTEND: Frontend time: 0.541s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.007s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13866578 629.27465986 + layer.39.0 232.10166120 3015.91180758 + ------------------------------------------------------------------------------------- + TOTAL 116.12016349 1822.59323372 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 905016 +BPFP 0.8589 bits/point +EBPFP 0.8589 equivalent bits/point +MSE 1822.593234 +---------------------- -------------------------------------------------------- +Time: 1.599s Load: 0.051s, Pack+Encode: 0.541s, Decode+Unpack: 1.007s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1822.5932 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01784675-painting_2.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01784675-painting_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01820546-painting_29.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01820546-painting_29.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 463,592B, BPFP=0.8799 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 434,596B, BPFP=0.8249 +⌛️ [2/4] FRONTEND: Frontend time: 0.594s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.037s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10398871 873.44679300 + layer.39.0 202.99580904 3295.85762877 + ------------------------------------------------------------------------------------- + TOTAL 101.54989888 2084.65221088 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 898188 +BPFP 0.8524 bits/point +EBPFP 0.8524 equivalent bits/point +MSE 2084.652211 +---------------------- -------------------------------------------------------- +Time: 1.689s Load: 0.059s, Pack+Encode: 0.594s, Decode+Unpack: 1.037s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2084.6522 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01820546-painting_29.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01820546-painting_29.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01833805-painting_23.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01833805-painting_23.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 401,888B, BPFP=0.7628 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 436,960B, BPFP=0.8294 +⌛️ [2/4] FRONTEND: Frontend time: 0.505s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.997s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09675035 259.07425292 + layer.39.0 56.43029868 1900.18197279 + ------------------------------------------------------------------------------------- + TOTAL 28.26352451 1079.62811285 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 838848 +BPFP 0.7961 bits/point +EBPFP 0.7961 equivalent bits/point +MSE 1079.628113 +---------------------- -------------------------------------------------------- +Time: 1.552s Load: 0.050s, Pack+Encode: 0.505s, Decode+Unpack: 0.997s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1079.6281 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01833805-painting_23.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01833805-painting_23.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01860187-painting_2.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01860187-painting_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 406,928B, BPFP=0.7724 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 433,276B, BPFP=0.8224 +⌛️ [2/4] FRONTEND: Frontend time: 0.586s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.022s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09532418 172.61971574 + layer.39.0 11.39113179 2017.79713314 + ------------------------------------------------------------------------------------- + TOTAL 5.74322799 1095.20842444 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 840204 +BPFP 0.7974 bits/point +EBPFP 0.7974 equivalent bits/point +MSE 1095.208424 +---------------------- -------------------------------------------------------- +Time: 1.678s Load: 0.070s, Pack+Encode: 0.586s, Decode+Unpack: 1.022s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1095.2084 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01860187-painting_2.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01860187-painting_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01944390-deviantart_6.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01944390-deviantart_6.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 331,568B, BPFP=0.6293 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 380,368B, BPFP=0.7220 +⌛️ [2/4] FRONTEND: Frontend time: 0.579s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.034s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10713051 197.55525692 + layer.39.0 82.30322218 2655.29227405 + ------------------------------------------------------------------------------------- + TOTAL 41.20517635 1426.42376549 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 711936 +BPFP 0.6757 bits/point +EBPFP 0.6757 equivalent bits/point +MSE 1426.423765 +---------------------- -------------------------------------------------------- +Time: 1.685s Load: 0.072s, Pack+Encode: 0.579s, Decode+Unpack: 1.034s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1426.4238 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01944390-deviantart_6.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01944390-deviantart_6.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01983481-misc_6.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01983481-misc_6.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 413,156B, BPFP=0.7842 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 429,316B, BPFP=0.8149 +⌛️ [2/4] FRONTEND: Frontend time: 0.584s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.029s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10315659 739.16326531 + layer.39.0 236.29731535 3061.44241983 + ------------------------------------------------------------------------------------- + TOTAL 118.20023597 1900.30284257 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 842472 +BPFP 0.7995 bits/point +EBPFP 0.7995 equivalent bits/point +MSE 1900.302843 +---------------------- -------------------------------------------------------- +Time: 1.664s Load: 0.051s, Pack+Encode: 0.584s, Decode+Unpack: 1.029s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1900.3028 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01983481-misc_6.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01983481-misc_6.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02051845-cartoon_2.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02051845-cartoon_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 386,652B, BPFP=0.7339 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 463,856B, BPFP=0.8804 +⌛️ [2/4] FRONTEND: Frontend time: 0.532s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.070s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11657756 271.25473761 + layer.39.0 123.57765428 2400.55393586 + ------------------------------------------------------------------------------------- + TOTAL 61.84711592 1335.90433673 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 850508 +BPFP 0.8072 bits/point +EBPFP 0.8072 equivalent bits/point +MSE 1335.904337 +---------------------- -------------------------------------------------------- +Time: 1.652s Load: 0.050s, Pack+Encode: 0.532s, Decode+Unpack: 1.070s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1335.9043 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02051845-cartoon_2.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02051845-cartoon_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02056570-art_2.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02056570-art_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 344,264B, BPFP=0.6534 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 438,716B, BPFP=0.8327 +⌛️ [2/4] FRONTEND: Frontend time: 0.558s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.076s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09569211 134.30422437 + layer.39.0 33.39981930 2754.93027211 + ------------------------------------------------------------------------------------- + TOTAL 16.74775571 1444.61724824 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 782980 +BPFP 0.7431 bits/point +EBPFP 0.7431 equivalent bits/point +MSE 1444.617248 +---------------------- -------------------------------------------------------- +Time: 1.704s Load: 0.070s, Pack+Encode: 0.558s, Decode+Unpack: 1.076s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1444.6172 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02056570-art_2.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02056570-art_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02085620-misc_90.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02085620-misc_90.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 13.574s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 404,592B, BPFP=0.7679 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 510,460B, BPFP=0.9689 +⌛️ [2/4] FRONTEND: Frontend time: 0.590s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.078s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09843166 220.74640124 + layer.39.0 72.76188958 2580.80855199 + ------------------------------------------------------------------------------------- + TOTAL 36.43016062 1400.77747662 + (elements=8,429,568) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 8429568 +Total Bytes 915052 +BPFP 0.8684 bits/point +EBPFP 0.8684 equivalent bits/point +MSE 1400.777477 +---------------------- --------------------------------------------------------- +Time: 15.242s Load: 13.574s, Pack+Encode: 0.590s, Decode+Unpack: 1.078s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1400.7775 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02085620-misc_90.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02085620-misc_90.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088094-misc_39.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088094-misc_39.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 404,048B, BPFP=0.7669 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 432,772B, BPFP=0.8214 +⌛️ [2/4] FRONTEND: Frontend time: 0.598s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.068s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09820385 248.28718720 + layer.39.0 12.32374423 1997.40670554 + ------------------------------------------------------------------------------------- + TOTAL 6.21097404 1122.84694637 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 836820 +BPFP 0.7942 bits/point +EBPFP 0.7942 equivalent bits/point +MSE 1122.846946 +---------------------- -------------------------------------------------------- +Time: 1.716s Load: 0.050s, Pack+Encode: 0.598s, Decode+Unpack: 1.068s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1122.8469 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088094-misc_39.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02088094-misc_39.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088466-sketch_11.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088466-sketch_11.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 341,776B, BPFP=0.6487 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 459,648B, BPFP=0.8724 +⌛️ [2/4] FRONTEND: Frontend time: 0.588s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.064s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09459993 25.33324792 + layer.39.0 16.33682960 2240.71793003 + ------------------------------------------------------------------------------------- + TOTAL 8.21571477 1133.02558897 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 801424 +BPFP 0.7606 bits/point +EBPFP 0.7606 equivalent bits/point +MSE 1133.025589 +---------------------- -------------------------------------------------------- +Time: 1.722s Load: 0.070s, Pack+Encode: 0.588s, Decode+Unpack: 1.064s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1133.0256 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088466-sketch_11.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02088466-sketch_11.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02094433-misc_20.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02094433-misc_20.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 404,948B, BPFP=0.7686 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 450,180B, BPFP=0.8545 +⌛️ [2/4] FRONTEND: Frontend time: 0.575s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.049s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09538842 159.36027089 + layer.39.0 94.83275632 3577.72813411 + ------------------------------------------------------------------------------------- + TOTAL 47.46407237 1868.54420250 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 855128 +BPFP 0.8116 bits/point +EBPFP 0.8116 equivalent bits/point +MSE 1868.544203 +---------------------- -------------------------------------------------------- +Time: 1.695s Load: 0.070s, Pack+Encode: 0.575s, Decode+Unpack: 1.049s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1868.5442 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02094433-misc_20.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02094433-misc_20.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02097298-misc_9.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02097298-misc_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 491,616B, BPFP=0.9331 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 475,988B, BPFP=0.9035 +⌛️ [2/4] FRONTEND: Frontend time: 0.580s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.057s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11199322 1989.61844023 + layer.39.0 26.16675018 2532.92808552 + ------------------------------------------------------------------------------------- + TOTAL 13.13937170 2261.27326288 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 967604 +BPFP 0.9183 bits/point +EBPFP 0.9183 equivalent bits/point +MSE 2261.273263 +---------------------- -------------------------------------------------------- +Time: 1.689s Load: 0.052s, Pack+Encode: 0.580s, Decode+Unpack: 1.057s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2261.2733 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02097298-misc_9.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02097298-misc_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02106662-misc_55.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02106662-misc_55.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 414,480B, BPFP=0.7867 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 463,980B, BPFP=0.8807 +⌛️ [2/4] FRONTEND: Frontend time: 0.591s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.076s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09642073 223.75523870 + layer.39.0 14.86428154 2102.49781341 + ------------------------------------------------------------------------------------- + TOTAL 7.48035113 1163.12652606 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 878460 +BPFP 0.8337 bits/point +EBPFP 0.8337 equivalent bits/point +MSE 1163.126526 +---------------------- -------------------------------------------------------- +Time: 1.737s Load: 0.069s, Pack+Encode: 0.591s, Decode+Unpack: 1.076s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1163.1265 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02106662-misc_55.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02106662-misc_55.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02109525-sketch_23.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02109525-sketch_23.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 349,036B, BPFP=0.6625 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 477,876B, BPFP=0.9070 +⌛️ [2/4] FRONTEND: Frontend time: 0.579s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.016s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09568003 74.01525298 + layer.39.0 14.01675815 2205.36588921 + ------------------------------------------------------------------------------------- + TOTAL 7.05621909 1139.69057109 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 826912 +BPFP 0.7848 bits/point +EBPFP 0.7848 equivalent bits/point +MSE 1139.690571 +---------------------- -------------------------------------------------------- +Time: 1.646s Load: 0.051s, Pack+Encode: 0.579s, Decode+Unpack: 1.016s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1139.6906 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02109525-sketch_23.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02109525-sketch_23.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110185-painting_33.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110185-painting_33.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 386,112B, BPFP=0.7329 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 495,044B, BPFP=0.9396 +⌛️ [2/4] FRONTEND: Frontend time: 0.601s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.064s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09599521 73.92861698 + layer.39.0 22.05506522 2778.83673469 + ------------------------------------------------------------------------------------- + TOTAL 11.07553021 1426.38267584 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 881156 +BPFP 0.8363 bits/point +EBPFP 0.8363 equivalent bits/point +MSE 1426.382676 +---------------------- -------------------------------------------------------- +Time: 1.726s Load: 0.061s, Pack+Encode: 0.601s, Decode+Unpack: 1.064s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1426.3827 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110185-painting_33.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02110185-painting_33.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110341-misc_162.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110341-misc_162.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 350,096B, BPFP=0.6645 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 439,372B, BPFP=0.8340 +⌛️ [2/4] FRONTEND: Frontend time: 0.580s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.031s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11124049 234.17737184 + layer.39.0 14.33747210 1598.89297862 + ------------------------------------------------------------------------------------- + TOTAL 7.22435629 916.53517523 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 789468 +BPFP 0.7492 bits/point +EBPFP 0.7492 equivalent bits/point +MSE 916.535175 +---------------------- -------------------------------------------------------- +Time: 1.663s Load: 0.052s, Pack+Encode: 0.580s, Decode+Unpack: 1.031s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 916.5352 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110341-misc_162.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02110341-misc_162.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02165456-tattoo_37.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02165456-tattoo_37.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 445,676B, BPFP=0.8459 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 469,300B, BPFP=0.8908 +⌛️ [2/4] FRONTEND: Frontend time: 0.589s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.030s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09780899 334.49070700 + layer.39.0 88.96013271 2926.59936832 + ------------------------------------------------------------------------------------- + TOTAL 44.52897085 1630.54503766 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 914976 +BPFP 0.8683 bits/point +EBPFP 0.8683 equivalent bits/point +MSE 1630.545038 +---------------------- -------------------------------------------------------- +Time: 1.679s Load: 0.059s, Pack+Encode: 0.589s, Decode+Unpack: 1.030s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1630.5450 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02165456-tattoo_37.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02165456-tattoo_37.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02219486-misc_3.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02219486-misc_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 346,344B, BPFP=0.6574 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 407,660B, BPFP=0.7738 +⌛️ [2/4] FRONTEND: Frontend time: 0.644s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.034s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10021695 61.74298090 + layer.39.0 75.73793580 1725.38107386 + ------------------------------------------------------------------------------------- + TOTAL 37.91907638 893.56202738 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 754004 +BPFP 0.7156 bits/point +EBPFP 0.7156 equivalent bits/point +MSE 893.562027 +---------------------- -------------------------------------------------------- +Time: 1.749s Load: 0.071s, Pack+Encode: 0.644s, Decode+Unpack: 1.034s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 893.5620 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02219486-misc_3.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02219486-misc_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02226429-tattoo_0.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02226429-tattoo_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 389,092B, BPFP=0.7385 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 449,076B, BPFP=0.8524 +⌛️ [2/4] FRONTEND: Frontend time: 0.628s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.036s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09506506 74.40874028 + layer.39.0 201.13660107 2406.45432459 + ------------------------------------------------------------------------------------- + TOTAL 100.61583306 1240.43153243 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 838168 +BPFP 0.7955 bits/point +EBPFP 0.7955 equivalent bits/point +MSE 1240.431532 +---------------------- -------------------------------------------------------- +Time: 1.715s Load: 0.051s, Pack+Encode: 0.628s, Decode+Unpack: 1.036s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1240.4315 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02226429-tattoo_0.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02226429-tattoo_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02233338-tattoo_5.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02233338-tattoo_5.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 388,908B, BPFP=0.7382 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 497,252B, BPFP=0.9438 +⌛️ [2/4] FRONTEND: Frontend time: 0.594s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.022s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09502332 88.08188320 + layer.39.0 172.43500972 2875.70845481 + ------------------------------------------------------------------------------------- + TOTAL 86.26501652 1481.89516901 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 886160 +BPFP 0.8410 bits/point +EBPFP 0.8410 equivalent bits/point +MSE 1481.895169 +---------------------- -------------------------------------------------------- +Time: 1.667s Load: 0.051s, Pack+Encode: 0.594s, Decode+Unpack: 1.022s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1481.8952 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02233338-tattoo_5.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02233338-tattoo_5.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02279972-painting_2.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02279972-painting_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 499,288B, BPFP=0.9477 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 496,836B, BPFP=0.9430 +⌛️ [2/4] FRONTEND: Frontend time: 0.560s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.069s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11337867 650.67371234 + layer.39.0 361.17623299 2899.64115646 + ------------------------------------------------------------------------------------- + TOTAL 180.64480583 1775.15743440 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 996124 +BPFP 0.9454 bits/point +EBPFP 0.9454 equivalent bits/point +MSE 1775.157434 +---------------------- -------------------------------------------------------- +Time: 1.680s Load: 0.050s, Pack+Encode: 0.560s, Decode+Unpack: 1.069s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1775.1574 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02279972-painting_2.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02279972-painting_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02317335-sculpture_0.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02317335-sculpture_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 386,524B, BPFP=0.7337 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 525,864B, BPFP=0.9981 +⌛️ [2/4] FRONTEND: Frontend time: 0.589s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.059s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09546056 113.92580782 + layer.39.0 1163.18707483 2716.67395530 + ------------------------------------------------------------------------------------- + TOTAL 581.64126769 1415.29988156 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 912388 +BPFP 0.8659 bits/point +EBPFP 0.8659 equivalent bits/point +MSE 1415.299882 +---------------------- -------------------------------------------------------- +Time: 1.717s Load: 0.070s, Pack+Encode: 0.589s, Decode+Unpack: 1.059s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1415.2999 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02317335-sculpture_0.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02317335-sculpture_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02346627-painting_9.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02346627-painting_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 441,004B, BPFP=0.8371 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 493,240B, BPFP=0.9362 +⌛️ [2/4] FRONTEND: Frontend time: 0.586s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.057s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13205896 746.94229835 + layer.39.0 503.01482021 2776.57482993 + ------------------------------------------------------------------------------------- + TOTAL 251.57343959 1761.75856414 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 934244 +BPFP 0.8866 bits/point +EBPFP 0.8866 equivalent bits/point +MSE 1761.758564 +---------------------- -------------------------------------------------------- +Time: 1.694s Load: 0.051s, Pack+Encode: 0.586s, Decode+Unpack: 1.057s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1761.7586 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02346627-painting_9.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02346627-painting_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02391049-deviantart_0.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02391049-deviantart_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 359,120B, BPFP=0.6816 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 420,060B, BPFP=0.7973 +⌛️ [2/4] FRONTEND: Frontend time: 0.583s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.025s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10116939 366.67817663 + layer.39.0 17.42674737 2016.35082604 + ------------------------------------------------------------------------------------- + TOTAL 8.76395838 1191.51450134 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 779180 +BPFP 0.7395 bits/point +EBPFP 0.7395 equivalent bits/point +MSE 1191.514501 +---------------------- -------------------------------------------------------- +Time: 1.678s Load: 0.070s, Pack+Encode: 0.583s, Decode+Unpack: 1.025s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1191.5145 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02391049-deviantart_0.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02391049-deviantart_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02395406-sculpture_31.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02395406-sculpture_31.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 497,256B, BPFP=0.9438 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 397,228B, BPFP=0.7540 +⌛️ [2/4] FRONTEND: Frontend time: 0.560s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.043s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11469608 1489.38872692 + layer.39.0 30.55020044 1912.10009718 + ------------------------------------------------------------------------------------- + TOTAL 15.33244826 1700.74441205 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 894484 +BPFP 0.8489 bits/point +EBPFP 0.8489 equivalent bits/point +MSE 1700.744412 +---------------------- -------------------------------------------------------- +Time: 1.655s Load: 0.052s, Pack+Encode: 0.560s, Decode+Unpack: 1.043s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1700.7444 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02395406-sculpture_31.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02395406-sculpture_31.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02445715-art_3.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02445715-art_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 391,432B, BPFP=0.7430 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 474,400B, BPFP=0.9004 +⌛️ [2/4] FRONTEND: Frontend time: 0.544s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.038s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09587883 111.01281584 + layer.39.0 77.63827138 3006.30782313 + ------------------------------------------------------------------------------------- + TOTAL 38.86707511 1558.66031948 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 865832 +BPFP 0.8217 bits/point +EBPFP 0.8217 equivalent bits/point +MSE 1558.660319 +---------------------- -------------------------------------------------------- +Time: 1.632s Load: 0.050s, Pack+Encode: 0.544s, Decode+Unpack: 1.038s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1558.6603 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02445715-art_3.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02445715-art_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02672831-sculpture_2.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02672831-sculpture_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 460,148B, BPFP=0.8734 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 479,016B, BPFP=0.9092 +⌛️ [2/4] FRONTEND: Frontend time: 0.594s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.031s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11638676 886.10914723 + layer.39.0 42.74346681 3397.29178814 + ------------------------------------------------------------------------------------- + TOTAL 21.42992678 2141.70046769 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 939164 +BPFP 0.8913 bits/point +EBPFP 0.8913 equivalent bits/point +MSE 2141.700468 +---------------------- -------------------------------------------------------- +Time: 1.675s Load: 0.050s, Pack+Encode: 0.594s, Decode+Unpack: 1.031s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2141.7005 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02672831-sculpture_2.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02672831-sculpture_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02701002-videogame_1.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02701002-videogame_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 361,444B, BPFP=0.6860 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 479,964B, BPFP=0.9110 +⌛️ [2/4] FRONTEND: Frontend time: 0.601s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.058s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10320827 602.89583333 + layer.39.0 160.61054422 2849.00923226 + ------------------------------------------------------------------------------------- + TOTAL 80.35687624 1725.95253280 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 841408 +BPFP 0.7985 bits/point +EBPFP 0.7985 equivalent bits/point +MSE 1725.952533 +---------------------- -------------------------------------------------------- +Time: 1.729s Load: 0.069s, Pack+Encode: 0.601s, Decode+Unpack: 1.058s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1725.9525 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02701002-videogame_1.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02701002-videogame_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02749479-misc_35.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02749479-misc_35.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 305,388B, BPFP=0.5797 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 421,108B, BPFP=0.7993 +⌛️ [2/4] FRONTEND: Frontend time: 0.608s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.038s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09764870 85.08118471 + layer.39.0 172.65676628 2527.52210884 + ------------------------------------------------------------------------------------- + TOTAL 86.37720749 1306.30164677 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 726496 +BPFP 0.6895 bits/point +EBPFP 0.6895 equivalent bits/point +MSE 1306.301647 +---------------------- -------------------------------------------------------- +Time: 1.705s Load: 0.059s, Pack+Encode: 0.608s, Decode+Unpack: 1.038s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1306.3016 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02749479-misc_35.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02749479-misc_35.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02769748-cartoon_11.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02769748-cartoon_11.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 325,644B, BPFP=0.6181 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 481,712B, BPFP=0.9143 +⌛️ [2/4] FRONTEND: Frontend time: 0.580s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.073s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12263774 148.74422983 + layer.39.0 11.02823964 1949.56438290 + ------------------------------------------------------------------------------------- + TOTAL 5.57543869 1049.15430637 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 807356 +BPFP 0.7662 bits/point +EBPFP 0.7662 equivalent bits/point +MSE 1049.154306 +---------------------- -------------------------------------------------------- +Time: 1.704s Load: 0.051s, Pack+Encode: 0.580s, Decode+Unpack: 1.073s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1049.1543 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02769748-cartoon_11.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02769748-cartoon_11.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02793495-sketch_11.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02793495-sketch_11.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 332,376B, BPFP=0.6309 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 390,068B, BPFP=0.7404 +⌛️ [2/4] FRONTEND: Frontend time: 0.583s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.020s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09793751 444.49480685 + layer.39.0 182.75789602 2295.53425656 + ------------------------------------------------------------------------------------- + TOTAL 91.42791676 1370.01453171 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 722444 +BPFP 0.6856 bits/point +EBPFP 0.6856 equivalent bits/point +MSE 1370.014532 +---------------------- -------------------------------------------------------- +Time: 1.674s Load: 0.070s, Pack+Encode: 0.583s, Decode+Unpack: 1.020s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1370.0145 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02793495-sketch_11.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02793495-sketch_11.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02797295-misc_13.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02797295-misc_13.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 530,296B, BPFP=1.0065 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 498,340B, BPFP=0.9459 +⌛️ [2/4] FRONTEND: Frontend time: 0.553s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.025s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.17140635 2025.22534014 + layer.39.0 172.50999150 3164.20068027 + ------------------------------------------------------------------------------------- + TOTAL 86.34069892 2594.71301020 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1028636 +BPFP 0.9762 bits/point +EBPFP 0.9762 equivalent bits/point +MSE 2594.713010 +---------------------- -------------------------------------------------------- +Time: 1.639s Load: 0.061s, Pack+Encode: 0.553s, Decode+Unpack: 1.025s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2594.7130 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02797295-misc_13.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02797295-misc_13.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02802426-art_1.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02802426-art_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.058s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 500,604B, BPFP=0.9502 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 546,252B, BPFP=1.0368 +⌛️ [2/4] FRONTEND: Frontend time: 0.585s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.033s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.16523854 877.28808309 + layer.39.0 477.65184645 2694.34062196 + ------------------------------------------------------------------------------------- + TOTAL 238.90854250 1785.81435253 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1046856 +BPFP 0.9935 bits/point +EBPFP 0.9935 equivalent bits/point +MSE 1785.814353 +---------------------- -------------------------------------------------------- +Time: 1.676s Load: 0.058s, Pack+Encode: 0.585s, Decode+Unpack: 1.033s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1785.8144 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02802426-art_1.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02802426-art_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02814860-sticker_8.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02814860-sticker_8.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 404,940B, BPFP=0.7686 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 464,496B, BPFP=0.8817 +⌛️ [2/4] FRONTEND: Frontend time: 0.605s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.025s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12757226 319.69323980 + layer.39.0 19.27598852 1618.14698737 + ------------------------------------------------------------------------------------- + TOTAL 9.70178039 968.92011358 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 869436 +BPFP 0.8251 bits/point +EBPFP 0.8251 equivalent bits/point +MSE 968.920114 +---------------------- -------------------------------------------------------- +Time: 1.701s Load: 0.071s, Pack+Encode: 0.605s, Decode+Unpack: 1.025s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 968.9201 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02814860-sticker_8.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02814860-sticker_8.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02841315-graphic_4.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02841315-graphic_4.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 432,820B, BPFP=0.8215 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 494,520B, BPFP=0.9386 +⌛️ [2/4] FRONTEND: Frontend time: 0.582s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.067s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11826141 292.76081147 + layer.39.0 55.46440340 2179.51627794 + ------------------------------------------------------------------------------------- + TOTAL 27.79133240 1236.13854470 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 927340 +BPFP 0.8801 bits/point +EBPFP 0.8801 equivalent bits/point +MSE 1236.138545 +---------------------- -------------------------------------------------------- +Time: 1.720s Load: 0.071s, Pack+Encode: 0.582s, Decode+Unpack: 1.067s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1236.1385 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02841315-graphic_4.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02841315-graphic_4.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02843684-cartoon_9.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02843684-cartoon_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.082s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 453,540B, BPFP=0.8609 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 466,408B, BPFP=0.8853 +⌛️ [2/4] FRONTEND: Frontend time: 0.601s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.039s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12386809 649.42626336 + layer.39.0 312.00962707 1886.98760933 + ------------------------------------------------------------------------------------- + TOTAL 156.06674758 1268.20693635 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 919948 +BPFP 0.8731 bits/point +EBPFP 0.8731 equivalent bits/point +MSE 1268.206936 +---------------------- -------------------------------------------------------- +Time: 1.722s Load: 0.082s, Pack+Encode: 0.601s, Decode+Unpack: 1.039s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1268.2069 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02843684-cartoon_9.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02843684-cartoon_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02883205-sketch_15.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02883205-sketch_15.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 342,428B, BPFP=0.6500 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 428,248B, BPFP=0.8128 +⌛️ [2/4] FRONTEND: Frontend time: 0.586s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.030s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09796664 467.86033163 + layer.39.0 103.64267493 2141.38969874 + ------------------------------------------------------------------------------------- + TOTAL 51.87032078 1304.62501518 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 770676 +BPFP 0.7314 bits/point +EBPFP 0.7314 equivalent bits/point +MSE 1304.625015 +---------------------- -------------------------------------------------------- +Time: 1.667s Load: 0.050s, Pack+Encode: 0.586s, Decode+Unpack: 1.030s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1304.6250 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02883205-sketch_15.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02883205-sketch_15.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02906734-graffiti_3.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02906734-graffiti_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 542,128B, BPFP=1.0290 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 513,004B, BPFP=0.9737 +⌛️ [2/4] FRONTEND: Frontend time: 0.599s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.073s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.17339475 1512.05089893 + layer.39.0 166.12656402 2689.92905734 + ------------------------------------------------------------------------------------- + TOTAL 83.14997939 2100.98997813 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1055132 +BPFP 1.0014 bits/point +EBPFP 1.0014 equivalent bits/point +MSE 2100.989978 +---------------------- -------------------------------------------------------- +Time: 1.741s Load: 0.070s, Pack+Encode: 0.599s, Decode+Unpack: 1.073s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2100.9900 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02906734-graffiti_3.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02906734-graffiti_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02909870-sketch_0.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02909870-sketch_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 346,816B, BPFP=0.6583 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 433,000B, BPFP=0.8219 +⌛️ [2/4] FRONTEND: Frontend time: 0.587s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.070s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.15317524 547.19545675 + layer.39.0 167.75886783 2309.29834791 + ------------------------------------------------------------------------------------- + TOTAL 83.95602154 1428.24690233 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 779816 +BPFP 0.7401 bits/point +EBPFP 0.7401 equivalent bits/point +MSE 1428.246902 +---------------------- -------------------------------------------------------- +Time: 1.707s Load: 0.050s, Pack+Encode: 0.587s, Decode+Unpack: 1.070s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1428.2469 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02909870-sketch_0.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02909870-sketch_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02939185-painting_0.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02939185-painting_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 355,596B, BPFP=0.6749 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 459,576B, BPFP=0.8723 +⌛️ [2/4] FRONTEND: Frontend time: 0.651s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.050s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09512242 98.57027454 + layer.39.0 131.28711127 2583.69606414 + ------------------------------------------------------------------------------------- + TOTAL 65.69111684 1341.13316934 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 815172 +BPFP 0.7736 bits/point +EBPFP 0.7736 equivalent bits/point +MSE 1341.133169 +---------------------- -------------------------------------------------------- +Time: 1.771s Load: 0.070s, Pack+Encode: 0.651s, Decode+Unpack: 1.050s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1341.1332 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02939185-painting_0.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02939185-painting_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02948072-misc_10.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02948072-misc_10.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 360,000B, BPFP=0.6833 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 509,080B, BPFP=0.9663 +⌛️ [2/4] FRONTEND: Frontend time: 0.600s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.056s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09566823 75.86039996 + layer.39.0 102.81622783 2465.33527697 + ------------------------------------------------------------------------------------- + TOTAL 51.45594803 1270.59783847 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 869080 +BPFP 0.8248 bits/point +EBPFP 0.8248 equivalent bits/point +MSE 1270.597838 +---------------------- -------------------------------------------------------- +Time: 1.727s Load: 0.071s, Pack+Encode: 0.600s, Decode+Unpack: 1.056s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1270.5978 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02948072-misc_10.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02948072-misc_10.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02950826-art_1.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02950826-art_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 394,124B, BPFP=0.7481 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 530,472B, BPFP=1.0069 +⌛️ [2/4] FRONTEND: Frontend time: 0.589s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.069s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09506074 100.25917153 + layer.39.0 1071.96149174 3582.24222546 + ------------------------------------------------------------------------------------- + TOTAL 536.02827624 1841.25069849 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 924596 +BPFP 0.8775 bits/point +EBPFP 0.8775 equivalent bits/point +MSE 1841.250698 +---------------------- -------------------------------------------------------- +Time: 1.709s Load: 0.051s, Pack+Encode: 0.589s, Decode+Unpack: 1.069s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1841.2507 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02950826-art_1.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02950826-art_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02951358-misc_1.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02951358-misc_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 316,316B, BPFP=0.6004 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 354,248B, BPFP=0.6724 +⌛️ [2/4] FRONTEND: Frontend time: 0.634s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.013s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09568294 62.45013742 + layer.39.0 598.97078474 2617.38338192 + ------------------------------------------------------------------------------------- + TOTAL 299.53323384 1339.91675967 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 670564 +BPFP 0.6364 bits/point +EBPFP 0.6364 equivalent bits/point +MSE 1339.916760 +---------------------- -------------------------------------------------------- +Time: 1.698s Load: 0.051s, Pack+Encode: 0.634s, Decode+Unpack: 1.013s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1339.9168 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02951358-misc_1.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02951358-misc_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02966193-cartoon_22.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02966193-cartoon_22.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 503,412B, BPFP=0.9555 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 519,844B, BPFP=0.9867 +⌛️ [2/4] FRONTEND: Frontend time: 0.576s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.046s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10376222 1116.63253158 + layer.39.0 767.85532070 3689.24975705 + ------------------------------------------------------------------------------------- + TOTAL 383.97954146 2402.94114431 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1023256 +BPFP 0.9711 bits/point +EBPFP 0.9711 equivalent bits/point +MSE 2402.941144 +---------------------- -------------------------------------------------------- +Time: 1.692s Load: 0.070s, Pack+Encode: 0.576s, Decode+Unpack: 1.046s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2402.9411 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02966193-cartoon_22.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02966193-cartoon_22.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02980441-graphic_3.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02980441-graphic_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 322,744B, BPFP=0.6126 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 361,356B, BPFP=0.6859 +⌛️ [2/4] FRONTEND: Frontend time: 0.588s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.025s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09509088 123.76028760 + layer.39.0 13.13791359 1515.06328960 + ------------------------------------------------------------------------------------- + TOTAL 6.61650224 819.41178860 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 684100 +BPFP 0.6492 bits/point +EBPFP 0.6492 equivalent bits/point +MSE 819.411789 +---------------------- -------------------------------------------------------- +Time: 1.664s Load: 0.051s, Pack+Encode: 0.588s, Decode+Unpack: 1.025s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 819.4118 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02980441-graphic_3.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02980441-graphic_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03124170-painting_15.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03124170-painting_15.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 452,112B, BPFP=0.8581 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 571,120B, BPFP=1.0840 +⌛️ [2/4] FRONTEND: Frontend time: 0.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.073s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10783903 544.49143586 + layer.39.0 326.57091229 3947.66350826 + ------------------------------------------------------------------------------------- + TOTAL 163.33937566 2246.07747206 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1023232 +BPFP 0.9711 bits/point +EBPFP 0.9711 equivalent bits/point +MSE 2246.077472 +---------------------- -------------------------------------------------------- +Time: 1.724s Load: 0.070s, Pack+Encode: 0.581s, Decode+Unpack: 1.073s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2246.0775 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03124170-painting_15.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03124170-painting_15.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03345487-toy_17.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03345487-toy_17.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 361,356B, BPFP=0.6859 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 436,256B, BPFP=0.8280 +⌛️ [2/4] FRONTEND: Frontend time: 0.590s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.058s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10662318 334.15521744 + layer.39.0 198.63900024 2822.14965986 + ------------------------------------------------------------------------------------- + TOTAL 99.37281171 1578.15243865 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 797612 +BPFP 0.7570 bits/point +EBPFP 0.7570 equivalent bits/point +MSE 1578.152439 +---------------------- -------------------------------------------------------- +Time: 1.718s Load: 0.070s, Pack+Encode: 0.590s, Decode+Unpack: 1.058s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1578.1524 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03345487-toy_17.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03345487-toy_17.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03372029-sketch_14.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03372029-sketch_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 439,148B, BPFP=0.8335 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 498,368B, BPFP=0.9459 +⌛️ [2/4] FRONTEND: Frontend time: 0.561s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.065s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12162214 593.99392614 + layer.39.0 228.06095117 2550.45869776 + ------------------------------------------------------------------------------------- + TOTAL 114.09128665 1572.22631195 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 937516 +BPFP 0.8897 bits/point +EBPFP 0.8897 equivalent bits/point +MSE 1572.226312 +---------------------- -------------------------------------------------------- +Time: 1.696s Load: 0.070s, Pack+Encode: 0.561s, Decode+Unpack: 1.065s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1572.2263 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03372029-sketch_14.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03372029-sketch_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03424325-misc_4.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03424325-misc_4.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 439,980B, BPFP=0.8351 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 510,064B, BPFP=0.9681 +⌛️ [2/4] FRONTEND: Frontend time: 0.601s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.061s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10761499 430.67280126 + layer.39.0 21.03287666 2042.17249757 + ------------------------------------------------------------------------------------- + TOTAL 10.57024582 1236.42264942 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 950044 +BPFP 0.9016 bits/point +EBPFP 0.9016 equivalent bits/point +MSE 1236.422649 +---------------------- -------------------------------------------------------- +Time: 1.731s Load: 0.070s, Pack+Encode: 0.601s, Decode+Unpack: 1.061s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1236.4226 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03424325-misc_4.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03424325-misc_4.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03467068-sketch_17.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03467068-sketch_17.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 368,944B, BPFP=0.7003 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 487,968B, BPFP=0.9262 +⌛️ [2/4] FRONTEND: Frontend time: 0.569s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.022s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09564773 234.83943756 + layer.39.0 208.14688107 2354.57361516 + ------------------------------------------------------------------------------------- + TOTAL 104.12126440 1294.70652636 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 856912 +BPFP 0.8132 bits/point +EBPFP 0.8132 equivalent bits/point +MSE 1294.706526 +---------------------- -------------------------------------------------------- +Time: 1.642s Load: 0.051s, Pack+Encode: 0.569s, Decode+Unpack: 1.022s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1294.7065 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03467068-sketch_17.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03467068-sketch_17.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03481172-sketch_9.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03481172-sketch_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 337,716B, BPFP=0.6410 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 460,004B, BPFP=0.8731 +⌛️ [2/4] FRONTEND: Frontend time: 0.507s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.995s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14641065 441.54406584 + layer.39.0 516.28267736 2388.49684159 + ------------------------------------------------------------------------------------- + TOTAL 258.21454400 1415.02045372 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 797720 +BPFP 0.7571 bits/point +EBPFP 0.7571 equivalent bits/point +MSE 1415.020454 +---------------------- -------------------------------------------------------- +Time: 1.554s Load: 0.052s, Pack+Encode: 0.507s, Decode+Unpack: 0.995s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1415.0205 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03481172-sketch_9.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03481172-sketch_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03494278-deviantart_3.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03494278-deviantart_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 263,604B, BPFP=0.5003 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 422,596B, BPFP=0.8021 +⌛️ [2/4] FRONTEND: Frontend time: 0.514s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.001s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09714438 98.86520590 + layer.39.0 11.38600982 1959.67832847 + ------------------------------------------------------------------------------------- + TOTAL 5.74157710 1029.27176719 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 686200 +BPFP 0.6512 bits/point +EBPFP 0.6512 equivalent bits/point +MSE 1029.271767 +---------------------- -------------------------------------------------------- +Time: 1.565s Load: 0.050s, Pack+Encode: 0.514s, Decode+Unpack: 1.001s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1029.2718 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03494278-deviantart_3.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03494278-deviantart_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03495258-painting_3.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03495258-painting_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 453,392B, BPFP=0.8606 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 476,164B, BPFP=0.9038 +⌛️ [2/4] FRONTEND: Frontend time: 0.607s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.071s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10398556 553.23651603 + layer.39.0 359.17207240 3038.09329446 + ------------------------------------------------------------------------------------- + TOTAL 179.63802898 1795.66490525 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 929556 +BPFP 0.8822 bits/point +EBPFP 0.8822 equivalent bits/point +MSE 1795.664905 +---------------------- -------------------------------------------------------- +Time: 1.749s Load: 0.070s, Pack+Encode: 0.607s, Decode+Unpack: 1.071s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1795.6649 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03495258-painting_3.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03495258-painting_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03498962-sketch_8.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03498962-sketch_8.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 437,528B, BPFP=0.8305 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 461,468B, BPFP=0.8759 +⌛️ [2/4] FRONTEND: Frontend time: 0.596s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.026s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.16074808 709.02793975 + layer.39.0 476.99061589 2584.12925170 + ------------------------------------------------------------------------------------- + TOTAL 238.57568198 1646.57859572 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 898996 +BPFP 0.8532 bits/point +EBPFP 0.8532 equivalent bits/point +MSE 1646.578596 +---------------------- -------------------------------------------------------- +Time: 1.692s Load: 0.070s, Pack+Encode: 0.596s, Decode+Unpack: 1.026s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1646.5786 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03498962-sketch_8.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03498962-sketch_8.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03602883-misc_12.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03602883-misc_12.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 268,356B, BPFP=0.5094 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 401,180B, BPFP=0.7615 +⌛️ [2/4] FRONTEND: Frontend time: 0.583s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.072s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.09080038 147.59337038 + layer.39.0 100.93773536 1809.05636540 + ------------------------------------------------------------------------------------- + TOTAL 54.51426787 978.32486789 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 669536 +BPFP 0.6354 bits/point +EBPFP 0.6354 equivalent bits/point +MSE 978.324868 +---------------------- -------------------------------------------------------- +Time: 1.724s Load: 0.069s, Pack+Encode: 0.583s, Decode+Unpack: 1.072s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 978.3249 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03602883-misc_12.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03602883-misc_12.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03630383-toy_1.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03630383-toy_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.086s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 324,012B, BPFP=0.6150 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 474,616B, BPFP=0.9009 +⌛️ [2/4] FRONTEND: Frontend time: 0.601s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.069s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09574974 37.63963420 + layer.39.0 14.66923857 1892.22667638 + ------------------------------------------------------------------------------------- + TOTAL 7.38249415 964.93315529 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 798628 +BPFP 0.7579 bits/point +EBPFP 0.7579 equivalent bits/point +MSE 964.933155 +---------------------- -------------------------------------------------------- +Time: 1.756s Load: 0.086s, Pack+Encode: 0.601s, Decode+Unpack: 1.069s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 964.9332 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03630383-toy_1.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03630383-toy_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03649909-toy_22.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03649909-toy_22.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 328,704B, BPFP=0.6239 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 346,152B, BPFP=0.6570 +⌛️ [2/4] FRONTEND: Frontend time: 0.572s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.013s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09878858 87.05354106 + layer.39.0 29.68475348 1469.49623421 + ------------------------------------------------------------------------------------- + TOTAL 14.89177103 778.27488763 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 674856 +BPFP 0.6405 bits/point +EBPFP 0.6405 equivalent bits/point +MSE 778.274888 +---------------------- -------------------------------------------------------- +Time: 1.655s Load: 0.070s, Pack+Encode: 0.572s, Decode+Unpack: 1.013s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 778.2749 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03649909-toy_22.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03649909-toy_22.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03676483-sculpture_3.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03676483-sculpture_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.073s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 346,180B, BPFP=0.6571 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 515,040B, BPFP=0.9776 +⌛️ [2/4] FRONTEND: Frontend time: 0.571s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.035s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09491264 25.74921419 + layer.39.0 32.22669916 2639.64650146 + ------------------------------------------------------------------------------------- + TOTAL 16.16080590 1332.69785783 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 861220 +BPFP 0.8173 bits/point +EBPFP 0.8173 equivalent bits/point +MSE 1332.697858 +---------------------- -------------------------------------------------------- +Time: 1.680s Load: 0.073s, Pack+Encode: 0.571s, Decode+Unpack: 1.035s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1332.6979 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03676483-sculpture_3.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03676483-sculpture_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03710193-sketch_14.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03710193-sketch_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 339,996B, BPFP=0.6453 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 441,132B, BPFP=0.8373 +⌛️ [2/4] FRONTEND: Frontend time: 0.561s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.049s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.47394152 390.71774781 + layer.39.0 335.99814747 2201.47230321 + ------------------------------------------------------------------------------------- + TOTAL 168.23604450 1296.09502551 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 781128 +BPFP 0.7413 bits/point +EBPFP 0.7413 equivalent bits/point +MSE 1296.095026 +---------------------- -------------------------------------------------------- +Time: 1.661s Load: 0.050s, Pack+Encode: 0.561s, Decode+Unpack: 1.049s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1296.0950 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03710193-sketch_14.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03710193-sketch_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03773504-graphic_4.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03773504-graphic_4.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 283,164B, BPFP=0.5375 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 377,852B, BPFP=0.7172 +⌛️ [2/4] FRONTEND: Frontend time: 0.576s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.060s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09681199 37.57575999 + layer.39.0 18.83313593 1750.07470845 + ------------------------------------------------------------------------------------- + TOTAL 9.46497396 893.82523422 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 661016 +BPFP 0.6273 bits/point +EBPFP 0.6273 equivalent bits/point +MSE 893.825234 +---------------------- -------------------------------------------------------- +Time: 1.706s Load: 0.070s, Pack+Encode: 0.576s, Decode+Unpack: 1.060s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 893.8252 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03773504-graphic_4.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03773504-graphic_4.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03775071-painting_1.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03775071-painting_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 411,924B, BPFP=0.7819 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 432,336B, BPFP=0.8206 +⌛️ [2/4] FRONTEND: Frontend time: 0.618s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.030s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11048905 404.62181122 + layer.39.0 386.73560496 2918.01020408 + ------------------------------------------------------------------------------------- + TOTAL 193.42304701 1661.31600765 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 844260 +BPFP 0.8012 bits/point +EBPFP 0.8012 equivalent bits/point +MSE 1661.316008 +---------------------- -------------------------------------------------------- +Time: 1.718s Load: 0.070s, Pack+Encode: 0.618s, Decode+Unpack: 1.030s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1661.3160 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03775071-painting_1.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03775071-painting_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03888257-cartoon_30.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03888257-cartoon_30.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 410,728B, BPFP=0.7796 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 475,852B, BPFP=0.9032 +⌛️ [2/4] FRONTEND: Frontend time: 0.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.061s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13203045 466.17735666 + layer.39.0 375.96832483 2349.73760933 + ------------------------------------------------------------------------------------- + TOTAL 188.05017764 1407.95748299 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 886580 +BPFP 0.8414 bits/point +EBPFP 0.8414 equivalent bits/point +MSE 1407.957483 +---------------------- -------------------------------------------------------- +Time: 1.712s Load: 0.070s, Pack+Encode: 0.581s, Decode+Unpack: 1.061s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1407.9575 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03888257-cartoon_30.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03888257-cartoon_30.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03930630-toy_2.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03930630-toy_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 309,492B, BPFP=0.5874 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 421,040B, BPFP=0.7992 +⌛️ [2/4] FRONTEND: Frontend time: 0.568s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.042s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09699417 50.07434402 + layer.39.0 46.17573949 2287.98420797 + ------------------------------------------------------------------------------------- + TOTAL 23.13636683 1169.02927600 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 730532 +BPFP 0.6933 bits/point +EBPFP 0.6933 equivalent bits/point +MSE 1169.029276 +---------------------- -------------------------------------------------------- +Time: 1.681s Load: 0.070s, Pack+Encode: 0.568s, Decode+Unpack: 1.042s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1169.0293 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03930630-toy_2.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03930630-toy_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04086273-sticker_5.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04086273-sticker_5.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 340,288B, BPFP=0.6459 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 376,644B, BPFP=0.7149 +⌛️ [2/4] FRONTEND: Frontend time: 0.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.057s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10161624 148.36291302 + layer.39.0 24.98063198 1821.79664723 + ------------------------------------------------------------------------------------- + TOTAL 12.54112411 985.07978013 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 716932 +BPFP 0.6804 bits/point +EBPFP 0.6804 equivalent bits/point +MSE 985.079780 +---------------------- -------------------------------------------------------- +Time: 1.707s Load: 0.070s, Pack+Encode: 0.581s, Decode+Unpack: 1.057s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 985.0798 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04086273-sticker_5.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04086273-sticker_5.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04118538-sculpture_0.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04118538-sculpture_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 404,800B, BPFP=0.7683 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 490,936B, BPFP=0.9318 +⌛️ [2/4] FRONTEND: Frontend time: 0.583s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.070s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09846411 231.94068878 + layer.39.0 11.87055944 2262.28741497 + ------------------------------------------------------------------------------------- + TOTAL 5.98451177 1247.11405187 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 895736 +BPFP 0.8501 bits/point +EBPFP 0.8501 equivalent bits/point +MSE 1247.114052 +---------------------- -------------------------------------------------------- +Time: 1.724s Load: 0.072s, Pack+Encode: 0.583s, Decode+Unpack: 1.070s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1247.1141 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04118538-sculpture_0.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04118538-sculpture_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04133789-cartoon_7.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04133789-cartoon_7.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.073s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 490,576B, BPFP=0.9312 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 518,328B, BPFP=0.9838 +⌛️ [2/4] FRONTEND: Frontend time: 0.602s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.050s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13739287 646.99969631 + layer.39.0 370.52532799 2957.28279883 + ------------------------------------------------------------------------------------- + TOTAL 185.33136043 1802.14124757 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1008904 +BPFP 0.9575 bits/point +EBPFP 0.9575 equivalent bits/point +MSE 1802.141248 +---------------------- -------------------------------------------------------- +Time: 1.725s Load: 0.073s, Pack+Encode: 0.602s, Decode+Unpack: 1.050s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1802.1412 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04133789-cartoon_7.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04133789-cartoon_7.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04141076-cartoon_14.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04141076-cartoon_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 349,408B, BPFP=0.6632 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 487,164B, BPFP=0.9247 +⌛️ [2/4] FRONTEND: Frontend time: 0.569s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.075s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11960477 379.40533892 + layer.39.0 53.25505649 1958.02818270 + ------------------------------------------------------------------------------------- + TOTAL 26.68733063 1168.71676081 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 836572 +BPFP 0.7939 bits/point +EBPFP 0.7939 equivalent bits/point +MSE 1168.716761 +---------------------- -------------------------------------------------------- +Time: 1.696s Load: 0.052s, Pack+Encode: 0.569s, Decode+Unpack: 1.075s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1168.7168 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04141076-cartoon_14.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04141076-cartoon_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04146614-misc_4.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04146614-misc_4.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 372,192B, BPFP=0.7065 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 444,372B, BPFP=0.8435 +⌛️ [2/4] FRONTEND: Frontend time: 0.539s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.016s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10047569 283.44797741 + layer.39.0 167.29959305 2789.38751215 + ------------------------------------------------------------------------------------- + TOTAL 83.70003437 1536.41774478 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 816564 +BPFP 0.7750 bits/point +EBPFP 0.7750 equivalent bits/point +MSE 1536.417745 +---------------------- -------------------------------------------------------- +Time: 1.605s Load: 0.051s, Pack+Encode: 0.539s, Decode+Unpack: 1.016s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1536.4177 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04146614-misc_4.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04146614-misc_4.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04147183-art_9.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04147183-art_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 369,460B, BPFP=0.7013 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 411,420B, BPFP=0.7809 +⌛️ [2/4] FRONTEND: Frontend time: 0.583s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.026s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11332939 723.93768222 + layer.39.0 22.95352360 1881.42152575 + ------------------------------------------------------------------------------------- + TOTAL 11.53342649 1302.67960398 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 780880 +BPFP 0.7411 bits/point +EBPFP 0.7411 equivalent bits/point +MSE 1302.679604 +---------------------- -------------------------------------------------------- +Time: 1.679s Load: 0.070s, Pack+Encode: 0.583s, Decode+Unpack: 1.026s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1302.6796 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04147183-art_9.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04147183-art_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04192698-videogame_16.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04192698-videogame_16.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 392,712B, BPFP=0.7454 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 486,524B, BPFP=0.9235 +⌛️ [2/4] FRONTEND: Frontend time: 0.590s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.030s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09706018 148.35292153 + layer.39.0 404.66927843 2343.39431487 + ------------------------------------------------------------------------------------- + TOTAL 202.38316930 1245.87361820 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 879236 +BPFP 0.8344 bits/point +EBPFP 0.8344 equivalent bits/point +MSE 1245.873618 +---------------------- -------------------------------------------------------- +Time: 1.669s Load: 0.050s, Pack+Encode: 0.590s, Decode+Unpack: 1.030s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1245.8736 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04192698-videogame_16.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04192698-videogame_16.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04254680-deviantart_17.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04254680-deviantart_17.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 340,572B, BPFP=0.6464 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 468,104B, BPFP=0.8885 +⌛️ [2/4] FRONTEND: Frontend time: 0.530s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.041s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10685510 283.06347182 + layer.39.0 151.81593173 2185.99465500 + ------------------------------------------------------------------------------------- + TOTAL 75.96139341 1234.52906341 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 808676 +BPFP 0.7675 bits/point +EBPFP 0.7675 equivalent bits/point +MSE 1234.529063 +---------------------- -------------------------------------------------------- +Time: 1.622s Load: 0.051s, Pack+Encode: 0.530s, Decode+Unpack: 1.041s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1234.5291 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04254680-deviantart_17.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04254680-deviantart_17.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04266014-painting_13.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04266014-painting_13.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 356,640B, BPFP=0.6769 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 386,580B, BPFP=0.7338 +⌛️ [2/4] FRONTEND: Frontend time: 0.586s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.025s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09568562 271.10052235 + layer.39.0 29.62437363 1835.38326045 + ------------------------------------------------------------------------------------- + TOTAL 14.86002963 1053.24189140 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 743220 +BPFP 0.7053 bits/point +EBPFP 0.7053 equivalent bits/point +MSE 1053.241891 +---------------------- -------------------------------------------------------- +Time: 1.661s Load: 0.050s, Pack+Encode: 0.586s, Decode+Unpack: 1.025s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1053.2419 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04266014-painting_13.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04266014-painting_13.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04310018-art_3.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04310018-art_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 406,072B, BPFP=0.7708 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 427,796B, BPFP=0.8120 +⌛️ [2/4] FRONTEND: Frontend time: 0.602s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.056s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13375617 1088.91715258 + layer.39.0 75.24515610 2371.26676385 + ------------------------------------------------------------------------------------- + TOTAL 37.68945614 1730.09195821 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 833868 +BPFP 0.7914 bits/point +EBPFP 0.7914 equivalent bits/point +MSE 1730.091958 +---------------------- -------------------------------------------------------- +Time: 1.729s Load: 0.072s, Pack+Encode: 0.602s, Decode+Unpack: 1.056s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1730.0920 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04310018-art_3.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04310018-art_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04347754-sculpture_0.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04347754-sculpture_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 491,068B, BPFP=0.9321 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 437,776B, BPFP=0.8309 +⌛️ [2/4] FRONTEND: Frontend time: 0.589s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.024s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14257451 1228.45383868 + layer.39.0 394.23636419 2020.39115646 + ------------------------------------------------------------------------------------- + TOTAL 197.18946935 1624.42249757 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 928844 +BPFP 0.8815 bits/point +EBPFP 0.8815 equivalent bits/point +MSE 1624.422498 +---------------------- -------------------------------------------------------- +Time: 1.684s Load: 0.071s, Pack+Encode: 0.589s, Decode+Unpack: 1.024s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1624.4225 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04347754-sculpture_0.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04347754-sculpture_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04409515-deviantart_1.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04409515-deviantart_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 330,440B, BPFP=0.6272 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 408,432B, BPFP=0.7752 +⌛️ [2/4] FRONTEND: Frontend time: 0.596s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.053s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09627266 101.42936862 + layer.39.0 9.33068077 2086.02137998 + ------------------------------------------------------------------------------------- + TOTAL 4.71347671 1093.72537430 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 738872 +BPFP 0.7012 bits/point +EBPFP 0.7012 equivalent bits/point +MSE 1093.725374 +---------------------- -------------------------------------------------------- +Time: 1.718s Load: 0.070s, Pack+Encode: 0.596s, Decode+Unpack: 1.053s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1093.7254 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04409515-deviantart_1.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04409515-deviantart_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04487394-deviantart_8.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04487394-deviantart_8.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 406,084B, BPFP=0.7708 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 449,256B, BPFP=0.8527 +⌛️ [2/4] FRONTEND: Frontend time: 0.574s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.043s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09911632 453.52465986 + layer.39.0 99.63155977 2435.34645287 + ------------------------------------------------------------------------------------- + TOTAL 49.86533804 1444.43555637 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 855340 +BPFP 0.8118 bits/point +EBPFP 0.8118 equivalent bits/point +MSE 1444.435556 +---------------------- -------------------------------------------------------- +Time: 1.688s Load: 0.070s, Pack+Encode: 0.574s, Decode+Unpack: 1.043s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1444.4356 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04487394-deviantart_8.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04487394-deviantart_8.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04522168-painting_32.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04522168-painting_32.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 358,512B, BPFP=0.6805 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 409,272B, BPFP=0.7768 +⌛️ [2/4] FRONTEND: Frontend time: 0.507s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.005s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11740584 402.50573980 + layer.39.0 10.95138066 1773.46392128 + ------------------------------------------------------------------------------------- + TOTAL 5.53439325 1087.98483054 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 767784 +BPFP 0.7287 bits/point +EBPFP 0.7287 equivalent bits/point +MSE 1087.984831 +---------------------- -------------------------------------------------------- +Time: 1.563s Load: 0.051s, Pack+Encode: 0.507s, Decode+Unpack: 1.005s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1087.9848 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04522168-painting_32.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04522168-painting_32.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04591713-painting_14.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04591713-painting_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 471,340B, BPFP=0.8946 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 494,640B, BPFP=0.9389 +⌛️ [2/4] FRONTEND: Frontend time: 0.604s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.067s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11212821 531.16095724 + layer.39.0 165.22564383 2293.74975705 + ------------------------------------------------------------------------------------- + TOTAL 82.66888602 1412.45535714 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 965980 +BPFP 0.9168 bits/point +EBPFP 0.9168 equivalent bits/point +MSE 1412.455357 +---------------------- -------------------------------------------------------- +Time: 1.723s Load: 0.051s, Pack+Encode: 0.604s, Decode+Unpack: 1.067s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1412.4554 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04591713-painting_14.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04591713-painting_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07693725-sketch_15.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07693725-sketch_15.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 408,844B, BPFP=0.7760 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 440,644B, BPFP=0.8364 +⌛️ [2/4] FRONTEND: Frontend time: 0.595s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.020s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10569874 958.59554179 + layer.39.0 214.96065658 2736.14795918 + ------------------------------------------------------------------------------------- + TOTAL 107.53317766 1847.37175049 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 849488 +BPFP 0.8062 bits/point +EBPFP 0.8062 equivalent bits/point +MSE 1847.371750 +---------------------- -------------------------------------------------------- +Time: 1.686s Load: 0.070s, Pack+Encode: 0.595s, Decode+Unpack: 1.020s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1847.3718 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07693725-sketch_15.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07693725-sketch_15.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07695742-videogame_1.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07695742-videogame_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 466,572B, BPFP=0.8856 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 473,244B, BPFP=0.8983 +⌛️ [2/4] FRONTEND: Frontend time: 0.523s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.010s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12460778 765.53401361 + layer.39.0 438.29433916 2399.97959184 + ------------------------------------------------------------------------------------- + TOTAL 219.20947347 1582.75680272 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 939816 +BPFP 0.8919 bits/point +EBPFP 0.8919 equivalent bits/point +MSE 1582.756803 +---------------------- -------------------------------------------------------- +Time: 1.585s Load: 0.052s, Pack+Encode: 0.523s, Decode+Unpack: 1.010s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1582.7568 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07695742-videogame_1.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07695742-videogame_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697313-deviantart_7.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697313-deviantart_7.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 348,752B, BPFP=0.6620 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 395,508B, BPFP=0.7507 +⌛️ [2/4] FRONTEND: Frontend time: 0.570s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.001s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09520741 159.90732811 + layer.39.0 14.69109212 2933.72594752 + ------------------------------------------------------------------------------------- + TOTAL 7.39314977 1546.81663782 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 744260 +BPFP 0.7063 bits/point +EBPFP 0.7063 equivalent bits/point +MSE 1546.816638 +---------------------- -------------------------------------------------------- +Time: 1.641s Load: 0.070s, Pack+Encode: 0.570s, Decode+Unpack: 1.001s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1546.8166 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697313-deviantart_7.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07697313-deviantart_7.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697537-deviantart_16.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697537-deviantart_16.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 390,456B, BPFP=0.7411 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 478,272B, BPFP=0.9078 +⌛️ [2/4] FRONTEND: Frontend time: 0.596s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.071s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09755328 246.08608175 + layer.39.0 90.32537658 2179.15014577 + ------------------------------------------------------------------------------------- + TOTAL 45.21146493 1212.61811376 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 868728 +BPFP 0.8245 bits/point +EBPFP 0.8245 equivalent bits/point +MSE 1212.618114 +---------------------- -------------------------------------------------------- +Time: 1.738s Load: 0.071s, Pack+Encode: 0.596s, Decode+Unpack: 1.071s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1212.6181 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697537-deviantart_16.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07697537-deviantart_16.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714571-deviantart_0.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714571-deviantart_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 386,712B, BPFP=0.7340 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 485,460B, BPFP=0.9214 +⌛️ [2/4] FRONTEND: Frontend time: 0.606s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.044s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09528512 86.02213162 + layer.39.0 45.81401467 3517.77283771 + ------------------------------------------------------------------------------------- + TOTAL 22.95464989 1801.89748466 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 872172 +BPFP 0.8277 bits/point +EBPFP 0.8277 equivalent bits/point +MSE 1801.897485 +---------------------- -------------------------------------------------------- +Time: 1.702s Load: 0.051s, Pack+Encode: 0.606s, Decode+Unpack: 1.044s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1801.8975 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714571-deviantart_0.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07714571-deviantart_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714990-toy_14.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714990-toy_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 390,488B, BPFP=0.7412 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 495,148B, BPFP=0.9398 +⌛️ [2/4] FRONTEND: Frontend time: 0.577s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.064s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09793257 158.61251822 + layer.39.0 322.50334062 3081.04907677 + ------------------------------------------------------------------------------------- + TOTAL 161.30063660 1619.83079750 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 885636 +BPFP 0.8405 bits/point +EBPFP 0.8405 equivalent bits/point +MSE 1619.830797 +---------------------- -------------------------------------------------------- +Time: 1.712s Load: 0.071s, Pack+Encode: 0.577s, Decode+Unpack: 1.064s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1619.8308 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714990-toy_14.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07714990-toy_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07718472-cartoon_1.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07718472-cartoon_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 286,088B, BPFP=0.5430 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 380,184B, BPFP=0.7216 +⌛️ [2/4] FRONTEND: Frontend time: 0.563s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.008s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11235230 517.48165695 + layer.39.0 14.49942963 1822.08321186 + ------------------------------------------------------------------------------------- + TOTAL 7.30589096 1169.78243440 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 666272 +BPFP 0.6323 bits/point +EBPFP 0.6323 equivalent bits/point +MSE 1169.782434 +---------------------- -------------------------------------------------------- +Time: 1.641s Load: 0.070s, Pack+Encode: 0.563s, Decode+Unpack: 1.008s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1169.7824 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07718472-cartoon_1.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07718472-cartoon_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07742313-deviantart_8.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07742313-deviantart_8.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 278,420B, BPFP=0.5285 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 391,568B, BPFP=0.7432 +⌛️ [2/4] FRONTEND: Frontend time: 0.521s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.047s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09669835 25.15621014 + layer.39.0 8.77690150 2156.40014577 + ------------------------------------------------------------------------------------- + TOTAL 4.43679992 1090.77817796 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 669988 +BPFP 0.6358 bits/point +EBPFP 0.6358 equivalent bits/point +MSE 1090.778178 +---------------------- -------------------------------------------------------- +Time: 1.619s Load: 0.051s, Pack+Encode: 0.521s, Decode+Unpack: 1.047s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1090.7782 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07742313-deviantart_8.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07742313-deviantart_8.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07749582-sticker_0.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07749582-sticker_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 411,268B, BPFP=0.7806 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 482,280B, BPFP=0.9154 +⌛️ [2/4] FRONTEND: Frontend time: 0.593s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.070s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09550123 172.98786747 + layer.39.0 34.64631545 2924.25801749 + ------------------------------------------------------------------------------------- + TOTAL 17.37090834 1548.62294248 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 893548 +BPFP 0.8480 bits/point +EBPFP 0.8480 equivalent bits/point +MSE 1548.622942 +---------------------- -------------------------------------------------------- +Time: 1.734s Load: 0.071s, Pack+Encode: 0.593s, Decode+Unpack: 1.070s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1548.6229 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07749582-sticker_0.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07749582-sticker_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07753275-painting_2.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07753275-painting_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 517,404B, BPFP=0.9821 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 468,188B, BPFP=0.8887 +⌛️ [2/4] FRONTEND: Frontend time: 0.635s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.035s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10429548 1540.66861030 + layer.39.0 540.43106171 3558.89698737 + ------------------------------------------------------------------------------------- + TOTAL 270.26767859 2549.78279883 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 985592 +BPFP 0.9354 bits/point +EBPFP 0.9354 equivalent bits/point +MSE 2549.782799 +---------------------- -------------------------------------------------------- +Time: 1.722s Load: 0.052s, Pack+Encode: 0.635s, Decode+Unpack: 1.035s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2549.7828 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07753275-painting_2.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07753275-painting_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07768694-painting_12.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07768694-painting_12.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 441,800B, BPFP=0.8386 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 464,660B, BPFP=0.8820 +⌛️ [2/4] FRONTEND: Frontend time: 0.558s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.034s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09821300 384.70250243 + layer.39.0 635.68343052 3388.78595724 + ------------------------------------------------------------------------------------- + TOTAL 317.89082176 1886.74422983 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 906460 +BPFP 0.8603 bits/point +EBPFP 0.8603 equivalent bits/point +MSE 1886.744230 +---------------------- -------------------------------------------------------- +Time: 1.644s Load: 0.051s, Pack+Encode: 0.558s, Decode+Unpack: 1.034s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1886.7442 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07768694-painting_12.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07768694-painting_12.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07920052-painting_1.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07920052-painting_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 397,396B, BPFP=0.7543 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 460,984B, BPFP=0.8750 +⌛️ [2/4] FRONTEND: Frontend time: 0.577s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.020s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09582097 221.52330843 + layer.39.0 9.59182155 2634.11540330 + ------------------------------------------------------------------------------------- + TOTAL 4.84382126 1427.81935587 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 858380 +BPFP 0.8146 bits/point +EBPFP 0.8146 equivalent bits/point +MSE 1427.819356 +---------------------- -------------------------------------------------------- +Time: 1.656s Load: 0.059s, Pack+Encode: 0.577s, Decode+Unpack: 1.020s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1427.8194 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07920052-painting_1.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07920052-painting_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09472597-painting_15.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09472597-painting_15.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 313,160B, BPFP=0.5944 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 382,732B, BPFP=0.7265 +⌛️ [2/4] FRONTEND: Frontend time: 0.570s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.057s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09164813 13.76018795 + layer.39.0 9.11265014 2024.35677843 + ------------------------------------------------------------------------------------- + TOTAL 4.60214913 1019.05848319 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 695892 +BPFP 0.6604 bits/point +EBPFP 0.6604 equivalent bits/point +MSE 1019.058483 +---------------------- -------------------------------------------------------- +Time: 1.680s Load: 0.052s, Pack+Encode: 0.570s, Decode+Unpack: 1.057s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1019.0585 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09472597-painting_15.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n09472597-painting_15.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09835506-videogame_3.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09835506-videogame_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 369,304B, BPFP=0.7010 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 475,672B, BPFP=0.9029 +⌛️ [2/4] FRONTEND: Frontend time: 0.600s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.020s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09585661 325.02872935 + layer.39.0 12.34450164 2485.86443149 + ------------------------------------------------------------------------------------- + TOTAL 6.22017912 1405.44658042 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 844976 +BPFP 0.8019 bits/point +EBPFP 0.8019 equivalent bits/point +MSE 1405.446580 +---------------------- -------------------------------------------------------- +Time: 1.689s Load: 0.070s, Pack+Encode: 0.600s, Decode+Unpack: 1.020s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1405.4466 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09835506-videogame_3.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n09835506-videogame_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n12267677-misc_105.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n12267677-misc_105.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 316,924B, BPFP=0.6015 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 468,152B, BPFP=0.8886 +⌛️ [2/4] FRONTEND: Frontend time: 0.572s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.050s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10166193 173.11525146 + layer.39.0 219.41089650 2387.68027211 + ------------------------------------------------------------------------------------- + TOTAL 109.75627921 1280.39776178 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 785076 +BPFP 0.7451 bits/point +EBPFP 0.7451 equivalent bits/point +MSE 1280.397762 +---------------------- -------------------------------------------------------- +Time: 1.693s Load: 0.070s, Pack+Encode: 0.572s, Decode+Unpack: 1.050s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1280.3978 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n12267677-misc_105.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n12267677-misc_105.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 0.8020 bits/point +Avg EBPFP 0.8020 equivalent bits/point +Avg MSE 1440.686075 +Avg Time 1.823s +------------------------ ---------------------------- diff --git a/lambda0.01/hyperprior-featurecoding-8bit-individual/cls_in1kval/dtufc_hyperprior-featurecoding_dinov3-total_individual.log b/lambda0.01/hyperprior-featurecoding-8bit-individual/cls_in1kval/dtufc_hyperprior-featurecoding_dinov3-total_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..03472d84aac2d3cd2dca3c0e512bfe4398f5bbd0 --- /dev/null +++ b/lambda0.01/hyperprior-featurecoding-8bit-individual/cls_in1kval/dtufc_hyperprior-featurecoding_dinov3-total_individual.log @@ -0,0 +1,9344 @@ +Experiment: dtufc_hyperprior-featurecoding_dinov3-total_individual +Log file: output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/dtufc_hyperprior-featurecoding_dinov3-total_individual.log +DTUFCCodecConfig: + arch: hyperprior-featurecoding + handler: dinov3-total + checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.01_epochs600_lr0.0001_bs360_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.01_epochs600_lr0.0001_bs360_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 599 +Loaded hyperprior-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.9' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.19' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.29' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.39' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json +Loaded per-key mappings: model=dinov3-total + Keys: ['layer.9', 'layer.19', 'layer.29', 'layer.39'] +---------------- ----------------------------------------------------------------------------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +Checkpoint codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.01_epochs600_lr0.0001_bs360_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-DINOv3/imagenet1k-val +Output output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval +---------------- ----------------------------------------------------------------------------------------------------------------------------- +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02825657-ILSVRC2012_val_00001103.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02825657-ILSVRC2012_val_00001103.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.088s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 332,264B, BPFP=0.6307 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 449,776B, BPFP=0.8537 +⌛️ [2/4] FRONTEND: Frontend time: 0.782s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.080s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10264289 429.92489674 + layer.39.0 9.47367932 1926.27162293 + ------------------------------------------------------------------------------------- + TOTAL 4.78816110 1178.09825984 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 782040 +BPFP 0.7422 bits/point +EBPFP 0.7422 equivalent bits/point +MSE 1178.098260 +---------------------- -------------------------------------------------------- +Time: 1.950s Load: 0.088s, Pack+Encode: 0.782s, Decode+Unpack: 1.080s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1178.0983 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02825657-ILSVRC2012_val_00001103.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02825657-ILSVRC2012_val_00001103.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02834397-ILSVRC2012_val_00001252.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02834397-ILSVRC2012_val_00001252.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.068s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 466,712B, BPFP=0.8859 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 485,156B, BPFP=0.9209 +⌛️ [2/4] FRONTEND: Frontend time: 0.578s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.071s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14789204 1134.22072400 + layer.39.0 415.43227648 2934.47108844 + ------------------------------------------------------------------------------------- + TOTAL 207.79008426 2034.34590622 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 951868 +BPFP 0.9034 bits/point +EBPFP 0.9034 equivalent bits/point +MSE 2034.345906 +---------------------- -------------------------------------------------------- +Time: 1.717s Load: 0.068s, Pack+Encode: 0.578s, Decode+Unpack: 1.071s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2034.3459 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02834397-ILSVRC2012_val_00001252.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02834397-ILSVRC2012_val_00001252.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02840245-ILSVRC2012_val_00003446.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02840245-ILSVRC2012_val_00003446.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 330,840B, BPFP=0.6280 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 420,824B, BPFP=0.7988 +⌛️ [2/4] FRONTEND: Frontend time: 0.511s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.998s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10761288 172.66902029 + layer.39.0 28.71820525 1918.16897473 + ------------------------------------------------------------------------------------- + TOTAL 14.41290906 1045.41899751 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 751664 +BPFP 0.7134 bits/point +EBPFP 0.7134 equivalent bits/point +MSE 1045.418998 +---------------------- -------------------------------------------------------- +Time: 1.560s Load: 0.051s, Pack+Encode: 0.511s, Decode+Unpack: 0.998s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1045.4190 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02840245-ILSVRC2012_val_00003446.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02840245-ILSVRC2012_val_00003446.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02843684-ILSVRC2012_val_00000514.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02843684-ILSVRC2012_val_00000514.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.054s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 388,988B, BPFP=0.7383 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 437,876B, BPFP=0.8311 +⌛️ [2/4] FRONTEND: Frontend time: 0.573s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.056s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11482661 440.72145287 + layer.39.0 84.54469600 2100.19460641 + ------------------------------------------------------------------------------------- + TOTAL 42.32976130 1270.45802964 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 826864 +BPFP 0.7847 bits/point +EBPFP 0.7847 equivalent bits/point +MSE 1270.458030 +---------------------- -------------------------------------------------------- +Time: 1.683s Load: 0.054s, Pack+Encode: 0.573s, Decode+Unpack: 1.056s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1270.4580 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02843684-ILSVRC2012_val_00000514.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02843684-ILSVRC2012_val_00000514.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02859443-ILSVRC2012_val_00000193.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02859443-ILSVRC2012_val_00000193.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 12.470s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 263,760B, BPFP=0.5006 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 321,532B, BPFP=0.6103 +⌛️ [2/4] FRONTEND: Frontend time: 0.563s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.026s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11417333 329.71550049 + layer.39.0 9.67809406 1598.17249757 + ------------------------------------------------------------------------------------- + TOTAL 4.89613370 963.94399903 + (elements=8,429,568) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 8429568 +Total Bytes 585292 +BPFP 0.5555 bits/point +EBPFP 0.5555 equivalent bits/point +MSE 963.943999 +---------------------- --------------------------------------------------------- +Time: 14.059s Load: 12.470s, Pack+Encode: 0.563s, Decode+Unpack: 1.026s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 963.9440 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02859443-ILSVRC2012_val_00000193.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02859443-ILSVRC2012_val_00000193.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02860847-ILSVRC2012_val_00000601.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02860847-ILSVRC2012_val_00000601.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 438,376B, BPFP=0.8321 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 419,996B, BPFP=0.7972 +⌛️ [2/4] FRONTEND: Frontend time: 0.562s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.027s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12653054 686.95432459 + layer.39.0 266.35249636 2316.79470360 + ------------------------------------------------------------------------------------- + TOTAL 133.23951345 1501.87451409 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 858372 +BPFP 0.8146 bits/point +EBPFP 0.8146 equivalent bits/point +MSE 1501.874514 +---------------------- -------------------------------------------------------- +Time: 1.641s Load: 0.052s, Pack+Encode: 0.562s, Decode+Unpack: 1.027s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1501.8745 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02860847-ILSVRC2012_val_00000601.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02860847-ILSVRC2012_val_00000601.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02865351-ILSVRC2012_val_00000763.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02865351-ILSVRC2012_val_00000763.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 318,640B, BPFP=0.6048 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 482,560B, BPFP=0.9159 +⌛️ [2/4] FRONTEND: Frontend time: 0.582s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.070s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09467571 173.75689383 + layer.39.0 15.47581086 2270.48202138 + ------------------------------------------------------------------------------------- + TOTAL 7.78524328 1222.11945760 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 801200 +BPFP 0.7604 bits/point +EBPFP 0.7604 equivalent bits/point +MSE 1222.119458 +---------------------- -------------------------------------------------------- +Time: 1.723s Load: 0.071s, Pack+Encode: 0.582s, Decode+Unpack: 1.070s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1222.1195 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02865351-ILSVRC2012_val_00000763.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02865351-ILSVRC2012_val_00000763.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02869837-ILSVRC2012_val_00000906.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02869837-ILSVRC2012_val_00000906.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.081s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 422,084B, BPFP=0.8011 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 524,036B, BPFP=0.9947 +⌛️ [2/4] FRONTEND: Frontend time: 0.591s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.077s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09659988 183.33465440 + layer.39.0 16.39405483 2142.95383868 + ------------------------------------------------------------------------------------- + TOTAL 8.24532736 1163.14424654 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 946120 +BPFP 0.8979 bits/point +EBPFP 0.8979 equivalent bits/point +MSE 1163.144247 +---------------------- -------------------------------------------------------- +Time: 1.749s Load: 0.081s, Pack+Encode: 0.591s, Decode+Unpack: 1.077s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1163.1442 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02869837-ILSVRC2012_val_00000906.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02869837-ILSVRC2012_val_00000906.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02870880-ILSVRC2012_val_00003274.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02870880-ILSVRC2012_val_00003274.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 454,324B, BPFP=0.8623 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 446,616B, BPFP=0.8477 +⌛️ [2/4] FRONTEND: Frontend time: 0.563s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.994s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10254154 787.16168610 + layer.39.0 9.36513093 2062.93318756 + ------------------------------------------------------------------------------------- + TOTAL 4.73383623 1425.04743683 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 900940 +BPFP 0.8550 bits/point +EBPFP 0.8550 equivalent bits/point +MSE 1425.047437 +---------------------- -------------------------------------------------------- +Time: 1.607s Load: 0.050s, Pack+Encode: 0.563s, Decode+Unpack: 0.994s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1425.0474 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02870880-ILSVRC2012_val_00003274.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02870880-ILSVRC2012_val_00003274.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02871525-ILSVRC2012_val_00000879.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02871525-ILSVRC2012_val_00000879.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 486,604B, BPFP=0.9236 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 486,980B, BPFP=0.9243 +⌛️ [2/4] FRONTEND: Frontend time: 0.545s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.010s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.17072899 1114.22691934 + layer.39.0 20.29403547 2217.22497570 + ------------------------------------------------------------------------------------- + TOTAL 10.23238223 1665.72594752 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 973584 +BPFP 0.9240 bits/point +EBPFP 0.9240 equivalent bits/point +MSE 1665.725948 +---------------------- -------------------------------------------------------- +Time: 1.607s Load: 0.052s, Pack+Encode: 0.545s, Decode+Unpack: 1.010s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1665.7259 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02871525-ILSVRC2012_val_00000879.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02871525-ILSVRC2012_val_00000879.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02877765-ILSVRC2012_val_00000634.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02877765-ILSVRC2012_val_00000634.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 390,380B, BPFP=0.7410 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 466,332B, BPFP=0.8851 +⌛️ [2/4] FRONTEND: Frontend time: 0.540s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.030s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10908128 270.26782677 + layer.39.0 364.97770894 2960.76068999 + ------------------------------------------------------------------------------------- + TOTAL 182.54339511 1615.51425838 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 856712 +BPFP 0.8131 bits/point +EBPFP 0.8131 equivalent bits/point +MSE 1615.514258 +---------------------- -------------------------------------------------------- +Time: 1.622s Load: 0.052s, Pack+Encode: 0.540s, Decode+Unpack: 1.030s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1615.5143 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02877765-ILSVRC2012_val_00000634.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02877765-ILSVRC2012_val_00000634.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02879718-ILSVRC2012_val_00001354.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02879718-ILSVRC2012_val_00001354.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 451,316B, BPFP=0.8566 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 491,312B, BPFP=0.9325 +⌛️ [2/4] FRONTEND: Frontend time: 0.582s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.031s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10948122 663.24070700 + layer.39.0 55.92460444 2246.61856171 + ------------------------------------------------------------------------------------- + TOTAL 28.01704283 1454.92963435 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 942628 +BPFP 0.8946 bits/point +EBPFP 0.8946 equivalent bits/point +MSE 1454.929634 +---------------------- -------------------------------------------------------- +Time: 1.664s Load: 0.051s, Pack+Encode: 0.582s, Decode+Unpack: 1.031s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1454.9296 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02879718-ILSVRC2012_val_00001354.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02879718-ILSVRC2012_val_00001354.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02883205-ILSVRC2012_val_00000126.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02883205-ILSVRC2012_val_00000126.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.053s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 291,724B, BPFP=0.5537 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 445,588B, BPFP=0.8458 +⌛️ [2/4] FRONTEND: Frontend time: 0.561s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.063s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.06711708 99.24227861 + layer.39.0 7.82069686 1630.94800777 + ------------------------------------------------------------------------------------- + TOTAL 7.94390697 865.09514319 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 737312 +BPFP 0.6997 bits/point +EBPFP 0.6997 equivalent bits/point +MSE 865.095143 +---------------------- -------------------------------------------------------- +Time: 1.677s Load: 0.053s, Pack+Encode: 0.561s, Decode+Unpack: 1.063s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 865.0951 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02883205-ILSVRC2012_val_00000126.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02883205-ILSVRC2012_val_00000126.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892201-ILSVRC2012_val_00001145.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892201-ILSVRC2012_val_00001145.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 473,972B, BPFP=0.8996 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 457,516B, BPFP=0.8684 +⌛️ [2/4] FRONTEND: Frontend time: 0.580s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.062s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11297333 871.94071914 + layer.39.0 15.09638643 2075.19169096 + ------------------------------------------------------------------------------------- + TOTAL 7.60467988 1473.56620505 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 931488 +BPFP 0.8840 bits/point +EBPFP 0.8840 equivalent bits/point +MSE 1473.566205 +---------------------- -------------------------------------------------------- +Time: 1.692s Load: 0.051s, Pack+Encode: 0.580s, Decode+Unpack: 1.062s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1473.5662 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892201-ILSVRC2012_val_00001145.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02892201-ILSVRC2012_val_00001145.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892767-ILSVRC2012_val_00000808.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892767-ILSVRC2012_val_00000808.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 381,292B, BPFP=0.7237 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 533,328B, BPFP=1.0123 +⌛️ [2/4] FRONTEND: Frontend time: 0.535s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.995s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09598007 86.17845754 + layer.39.0 31.15013059 2228.55758017 + ------------------------------------------------------------------------------------- + TOTAL 15.62305533 1157.36801886 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 914620 +BPFP 0.8680 bits/point +EBPFP 0.8680 equivalent bits/point +MSE 1157.368019 +---------------------- -------------------------------------------------------- +Time: 1.590s Load: 0.061s, Pack+Encode: 0.535s, Decode+Unpack: 0.995s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1157.3680 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892767-ILSVRC2012_val_00000808.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02892767-ILSVRC2012_val_00000808.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02895154-ILSVRC2012_val_00000080.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02895154-ILSVRC2012_val_00000080.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.081s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 398,908B, BPFP=0.7572 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 512,256B, BPFP=0.9723 +⌛️ [2/4] FRONTEND: Frontend time: 0.596s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.072s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09530723 234.49523202 + layer.39.0 971.40427600 3507.58746356 + ------------------------------------------------------------------------------------- + TOTAL 485.74979162 1871.04134779 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 911164 +BPFP 0.8647 bits/point +EBPFP 0.8647 equivalent bits/point +MSE 1871.041348 +---------------------- -------------------------------------------------------- +Time: 1.749s Load: 0.081s, Pack+Encode: 0.596s, Decode+Unpack: 1.072s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1871.0413 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02895154-ILSVRC2012_val_00000080.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02895154-ILSVRC2012_val_00000080.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02906734-ILSVRC2012_val_00002937.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02906734-ILSVRC2012_val_00002937.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 413,952B, BPFP=0.7857 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 414,600B, BPFP=0.7869 +⌛️ [2/4] FRONTEND: Frontend time: 0.501s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.993s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09767962 357.42526118 + layer.39.0 32.09536716 2053.09863946 + ------------------------------------------------------------------------------------- + TOTAL 16.09652339 1205.26195032 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 828552 +BPFP 0.7863 bits/point +EBPFP 0.7863 equivalent bits/point +MSE 1205.261950 +---------------------- -------------------------------------------------------- +Time: 1.545s Load: 0.051s, Pack+Encode: 0.501s, Decode+Unpack: 0.993s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1205.2620 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02906734-ILSVRC2012_val_00002937.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02906734-ILSVRC2012_val_00002937.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02910353-ILSVRC2012_val_00000558.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02910353-ILSVRC2012_val_00000558.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 350,440B, BPFP=0.6652 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 439,152B, BPFP=0.8335 +⌛️ [2/4] FRONTEND: Frontend time: 0.519s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.001s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11017090 379.89273567 + layer.39.0 483.40066205 2684.83187561 + ------------------------------------------------------------------------------------- + TOTAL 241.75541648 1532.36230564 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 789592 +BPFP 0.7494 bits/point +EBPFP 0.7494 equivalent bits/point +MSE 1532.362306 +---------------------- -------------------------------------------------------- +Time: 1.582s Load: 0.062s, Pack+Encode: 0.519s, Decode+Unpack: 1.001s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1532.3623 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02910353-ILSVRC2012_val_00000558.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02910353-ILSVRC2012_val_00000558.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02916936-ILSVRC2012_val_00000366.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02916936-ILSVRC2012_val_00000366.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 303,200B, BPFP=0.5755 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 451,992B, BPFP=0.8579 +⌛️ [2/4] FRONTEND: Frontend time: 0.593s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.072s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10657579 147.77109147 + layer.39.0 435.18944363 2503.64528669 + ------------------------------------------------------------------------------------- + TOTAL 217.64800971 1325.70818908 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 755192 +BPFP 0.7167 bits/point +EBPFP 0.7167 equivalent bits/point +MSE 1325.708189 +---------------------- -------------------------------------------------------- +Time: 1.736s Load: 0.071s, Pack+Encode: 0.593s, Decode+Unpack: 1.072s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1325.7082 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02916936-ILSVRC2012_val_00000366.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02916936-ILSVRC2012_val_00000366.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02917067-ILSVRC2012_val_00000562.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02917067-ILSVRC2012_val_00000562.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 454,156B, BPFP=0.8620 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 448,788B, BPFP=0.8518 +⌛️ [2/4] FRONTEND: Frontend time: 0.566s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.085s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10760244 590.52757532 + layer.39.0 37.55795979 2502.08600583 + ------------------------------------------------------------------------------------- + TOTAL 18.83278111 1546.30679057 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 902944 +BPFP 0.8569 bits/point +EBPFP 0.8569 equivalent bits/point +MSE 1546.306791 +---------------------- -------------------------------------------------------- +Time: 1.721s Load: 0.070s, Pack+Encode: 0.566s, Decode+Unpack: 1.085s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1546.3068 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02917067-ILSVRC2012_val_00000562.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02917067-ILSVRC2012_val_00000562.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02930766-ILSVRC2012_val_00000056.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02930766-ILSVRC2012_val_00000056.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 429,800B, BPFP=0.8158 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 495,848B, BPFP=0.9412 +⌛️ [2/4] FRONTEND: Frontend time: 0.587s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.066s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10591127 492.16174684 + layer.39.0 18.32421875 2580.98663751 + ------------------------------------------------------------------------------------- + TOTAL 9.21506501 1536.57419218 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 925648 +BPFP 0.8785 bits/point +EBPFP 0.8785 equivalent bits/point +MSE 1536.574192 +---------------------- -------------------------------------------------------- +Time: 1.722s Load: 0.069s, Pack+Encode: 0.587s, Decode+Unpack: 1.066s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1536.5742 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02930766-ILSVRC2012_val_00000056.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02930766-ILSVRC2012_val_00000056.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02939185-ILSVRC2012_val_00000302.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02939185-ILSVRC2012_val_00000302.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 410,236B, BPFP=0.7787 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 446,856B, BPFP=0.8482 +⌛️ [2/4] FRONTEND: Frontend time: 0.599s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.072s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09694758 186.32879312 + layer.39.0 25.52453269 3065.04081633 + ------------------------------------------------------------------------------------- + TOTAL 12.81074014 1625.68480473 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 857092 +BPFP 0.8134 bits/point +EBPFP 0.8134 equivalent bits/point +MSE 1625.684805 +---------------------- -------------------------------------------------------- +Time: 1.752s Load: 0.080s, Pack+Encode: 0.599s, Decode+Unpack: 1.072s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1625.6848 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02939185-ILSVRC2012_val_00000302.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02939185-ILSVRC2012_val_00000302.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02950826-ILSVRC2012_val_00000392.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02950826-ILSVRC2012_val_00000392.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 411,008B, BPFP=0.7801 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 446,392B, BPFP=0.8473 +⌛️ [2/4] FRONTEND: Frontend time: 0.575s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.069s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10873010 858.72327502 + layer.39.0 707.96944849 2895.02259475 + ------------------------------------------------------------------------------------- + TOTAL 354.03908930 1876.87293489 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 857400 +BPFP 0.8137 bits/point +EBPFP 0.8137 equivalent bits/point +MSE 1876.872935 +---------------------- -------------------------------------------------------- +Time: 1.713s Load: 0.069s, Pack+Encode: 0.575s, Decode+Unpack: 1.069s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1876.8729 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02950826-ILSVRC2012_val_00000392.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02950826-ILSVRC2012_val_00000392.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951358-ILSVRC2012_val_00000347.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951358-ILSVRC2012_val_00000347.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 394,448B, BPFP=0.7487 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 404,912B, BPFP=0.7686 +⌛️ [2/4] FRONTEND: Frontend time: 0.580s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.068s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12200860 750.25327988 + layer.39.0 237.66299198 2329.95845481 + ------------------------------------------------------------------------------------- + TOTAL 118.89250029 1540.10586735 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 799360 +BPFP 0.7586 bits/point +EBPFP 0.7586 equivalent bits/point +MSE 1540.105867 +---------------------- -------------------------------------------------------- +Time: 1.718s Load: 0.070s, Pack+Encode: 0.580s, Decode+Unpack: 1.068s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1540.1059 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951358-ILSVRC2012_val_00000347.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02951358-ILSVRC2012_val_00000347.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951585-ILSVRC2012_val_00000101.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951585-ILSVRC2012_val_00000101.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.049s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 231,012B, BPFP=0.4385 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 414,888B, BPFP=0.7875 +⌛️ [2/4] FRONTEND: Frontend time: 0.551s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.001s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.07385432 74.94699040 + layer.39.0 181.90962099 2326.57385811 + ------------------------------------------------------------------------------------- + TOTAL 94.99173765 1200.76042426 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 645900 +BPFP 0.6130 bits/point +EBPFP 0.6130 equivalent bits/point +MSE 1200.760424 +---------------------- -------------------------------------------------------- +Time: 1.601s Load: 0.049s, Pack+Encode: 0.551s, Decode+Unpack: 1.001s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1200.7604 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951585-ILSVRC2012_val_00000101.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02951585-ILSVRC2012_val_00000101.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02963159-ILSVRC2012_val_00000061.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02963159-ILSVRC2012_val_00000061.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 406,376B, BPFP=0.7713 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 481,468B, BPFP=0.9139 +⌛️ [2/4] FRONTEND: Frontend time: 0.521s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.997s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11232698 235.80733722 + layer.39.0 24.77479842 1944.26287658 + ------------------------------------------------------------------------------------- + TOTAL 12.44356270 1090.03510690 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 887844 +BPFP 0.8426 bits/point +EBPFP 0.8426 equivalent bits/point +MSE 1090.035107 +---------------------- -------------------------------------------------------- +Time: 1.580s Load: 0.062s, Pack+Encode: 0.521s, Decode+Unpack: 0.997s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1090.0351 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02963159-ILSVRC2012_val_00000061.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02963159-ILSVRC2012_val_00000061.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02965783-ILSVRC2012_val_00000213.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02965783-ILSVRC2012_val_00000213.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 360,840B, BPFP=0.6849 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 469,068B, BPFP=0.8903 +⌛️ [2/4] FRONTEND: Frontend time: 0.566s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.070s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09516161 26.46599019 + layer.39.0 223.32294704 2604.27939747 + ------------------------------------------------------------------------------------- + TOTAL 111.70905432 1315.37269383 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 829908 +BPFP 0.7876 bits/point +EBPFP 0.7876 equivalent bits/point +MSE 1315.372694 +---------------------- -------------------------------------------------------- +Time: 1.687s Load: 0.050s, Pack+Encode: 0.566s, Decode+Unpack: 1.070s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1315.3727 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02965783-ILSVRC2012_val_00000213.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02965783-ILSVRC2012_val_00000213.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966193-ILSVRC2012_val_00000074.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966193-ILSVRC2012_val_00000074.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 494,144B, BPFP=0.9379 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 561,524B, BPFP=1.0658 +⌛️ [2/4] FRONTEND: Frontend time: 0.515s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.013s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12190965 823.83102527 + layer.39.0 378.75431244 3690.74708455 + ------------------------------------------------------------------------------------- + TOTAL 189.43811104 2257.28905491 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1055668 +BPFP 1.0019 bits/point +EBPFP 1.0019 equivalent bits/point +MSE 2257.289055 +---------------------- -------------------------------------------------------- +Time: 1.579s Load: 0.051s, Pack+Encode: 0.515s, Decode+Unpack: 1.013s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2257.2891 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966193-ILSVRC2012_val_00000074.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02966193-ILSVRC2012_val_00000074.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966687-ILSVRC2012_val_00001041.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966687-ILSVRC2012_val_00001041.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 400,864B, BPFP=0.7609 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 496,596B, BPFP=0.9426 +⌛️ [2/4] FRONTEND: Frontend time: 0.495s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.999s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12487827 514.48894558 + layer.39.0 254.07423773 2580.99368319 + ------------------------------------------------------------------------------------- + TOTAL 127.09955800 1547.74131438 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 897460 +BPFP 0.8517 bits/point +EBPFP 0.8517 equivalent bits/point +MSE 1547.741314 +---------------------- -------------------------------------------------------- +Time: 1.547s Load: 0.052s, Pack+Encode: 0.495s, Decode+Unpack: 0.999s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1547.7413 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966687-ILSVRC2012_val_00001041.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02966687-ILSVRC2012_val_00001041.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02971356-ILSVRC2012_val_00000019.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02971356-ILSVRC2012_val_00000019.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 315,716B, BPFP=0.5993 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 379,316B, BPFP=0.7200 +⌛️ [2/4] FRONTEND: Frontend time: 0.502s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 0.985s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09754465 99.30032191 + layer.39.0 24.51746044 1917.10398445 + ------------------------------------------------------------------------------------- + TOTAL 12.30750255 1008.20215318 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 695032 +BPFP 0.6596 bits/point +EBPFP 0.6596 equivalent bits/point +MSE 1008.202153 +---------------------- -------------------------------------------------------- +Time: 1.538s Load: 0.051s, Pack+Encode: 0.502s, Decode+Unpack: 0.985s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1008.2022 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02971356-ILSVRC2012_val_00000019.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02971356-ILSVRC2012_val_00000019.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02978881-ILSVRC2012_val_00000353.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02978881-ILSVRC2012_val_00000353.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 407,300B, BPFP=0.7731 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 515,228B, BPFP=0.9779 +⌛️ [2/4] FRONTEND: Frontend time: 0.588s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.070s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09975241 207.86881985 + layer.39.0 226.62124939 2420.25024295 + ------------------------------------------------------------------------------------- + TOTAL 113.36050090 1314.05953140 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 922528 +BPFP 0.8755 bits/point +EBPFP 0.8755 equivalent bits/point +MSE 1314.059531 +---------------------- -------------------------------------------------------- +Time: 1.729s Load: 0.072s, Pack+Encode: 0.588s, Decode+Unpack: 1.070s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1314.0595 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02978881-ILSVRC2012_val_00000353.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02978881-ILSVRC2012_val_00000353.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02980441-ILSVRC2012_val_00000122.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02980441-ILSVRC2012_val_00000122.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 368,880B, BPFP=0.7002 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 425,464B, BPFP=0.8076 +⌛️ [2/4] FRONTEND: Frontend time: 0.587s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.061s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10186533 88.66656797 + layer.39.0 8.25151846 1837.97983479 + ------------------------------------------------------------------------------------- + TOTAL 4.17669190 963.32320138 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 794344 +BPFP 0.7539 bits/point +EBPFP 0.7539 equivalent bits/point +MSE 963.323201 +---------------------- -------------------------------------------------------- +Time: 1.729s Load: 0.080s, Pack+Encode: 0.587s, Decode+Unpack: 1.061s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 963.3232 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02980441-ILSVRC2012_val_00000122.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02980441-ILSVRC2012_val_00000122.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02988304-ILSVRC2012_val_00003491.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02988304-ILSVRC2012_val_00003491.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 361,232B, BPFP=0.6856 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 413,992B, BPFP=0.7858 +⌛️ [2/4] FRONTEND: Frontend time: 0.556s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.067s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10176498 234.73767007 + layer.39.0 516.16180758 3018.03911565 + ------------------------------------------------------------------------------------- + TOTAL 258.13178628 1626.38839286 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 775224 +BPFP 0.7357 bits/point +EBPFP 0.7357 equivalent bits/point +MSE 1626.388393 +---------------------- -------------------------------------------------------- +Time: 1.675s Load: 0.052s, Pack+Encode: 0.556s, Decode+Unpack: 1.067s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1626.3884 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02988304-ILSVRC2012_val_00003491.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02988304-ILSVRC2012_val_00003491.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992211-ILSVRC2012_val_00000108.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992211-ILSVRC2012_val_00000108.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 427,264B, BPFP=0.8110 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 453,480B, BPFP=0.8607 +⌛️ [2/4] FRONTEND: Frontend time: 0.545s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.006s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10107529 233.56438290 + layer.39.0 89.13089923 3163.38775510 + ------------------------------------------------------------------------------------- + TOTAL 44.61598726 1698.47606900 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 880744 +BPFP 0.8359 bits/point +EBPFP 0.8359 equivalent bits/point +MSE 1698.476069 +---------------------- -------------------------------------------------------- +Time: 1.602s Load: 0.052s, Pack+Encode: 0.545s, Decode+Unpack: 1.006s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1698.4761 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992211-ILSVRC2012_val_00000108.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02992211-ILSVRC2012_val_00000108.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992529-ILSVRC2012_val_00000089.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992529-ILSVRC2012_val_00000089.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 393,072B, BPFP=0.7461 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 505,388B, BPFP=0.9593 +⌛️ [2/4] FRONTEND: Frontend time: 0.570s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.077s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11197385 221.36886540 + layer.39.0 964.25631681 3459.36953353 + ------------------------------------------------------------------------------------- + TOTAL 482.18414533 1840.36919947 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 898460 +BPFP 0.8527 bits/point +EBPFP 0.8527 equivalent bits/point +MSE 1840.369199 +---------------------- -------------------------------------------------------- +Time: 1.717s Load: 0.071s, Pack+Encode: 0.570s, Decode+Unpack: 1.077s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1840.3692 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992529-ILSVRC2012_val_00000089.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02992529-ILSVRC2012_val_00000089.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02999410-ILSVRC2012_val_00000376.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02999410-ILSVRC2012_val_00000376.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 417,396B, BPFP=0.7923 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 477,488B, BPFP=0.9063 +⌛️ [2/4] FRONTEND: Frontend time: 0.565s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.056s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10398186 565.48621234 + layer.39.0 145.78410471 1959.42006803 + ------------------------------------------------------------------------------------- + TOTAL 72.94404329 1262.45314018 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 894884 +BPFP 0.8493 bits/point +EBPFP 0.8493 equivalent bits/point +MSE 1262.453140 +---------------------- -------------------------------------------------------- +Time: 1.672s Load: 0.050s, Pack+Encode: 0.565s, Decode+Unpack: 1.056s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1262.4531 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02999410-ILSVRC2012_val_00000376.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02999410-ILSVRC2012_val_00000376.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000134-ILSVRC2012_val_00001094.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000134-ILSVRC2012_val_00001094.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 371,512B, BPFP=0.7052 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 522,788B, BPFP=0.9923 +⌛️ [2/4] FRONTEND: Frontend time: 0.577s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.068s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09696872 86.11436316 + layer.39.0 22.81329530 2172.90962099 + ------------------------------------------------------------------------------------- + TOTAL 11.45513201 1129.51199207 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 894300 +BPFP 0.8487 bits/point +EBPFP 0.8487 equivalent bits/point +MSE 1129.511992 +---------------------- -------------------------------------------------------- +Time: 1.697s Load: 0.052s, Pack+Encode: 0.577s, Decode+Unpack: 1.068s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1129.5120 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000134-ILSVRC2012_val_00001094.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03000134-ILSVRC2012_val_00001094.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000247-ILSVRC2012_val_00002280.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000247-ILSVRC2012_val_00002280.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 580,584B, BPFP=1.1020 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 400,520B, BPFP=0.7602 +⌛️ [2/4] FRONTEND: Frontend time: 0.591s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.075s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.29135144 2392.64747328 + layer.39.0 428.26293732 2666.68367347 + ------------------------------------------------------------------------------------- + TOTAL 214.27714438 2529.66557337 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 981104 +BPFP 0.9311 bits/point +EBPFP 0.9311 equivalent bits/point +MSE 2529.665573 +---------------------- -------------------------------------------------------- +Time: 1.745s Load: 0.080s, Pack+Encode: 0.591s, Decode+Unpack: 1.075s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2529.6656 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000247-ILSVRC2012_val_00002280.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03000247-ILSVRC2012_val_00002280.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000684-ILSVRC2012_val_00000537.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000684-ILSVRC2012_val_00000537.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 495,524B, BPFP=0.9405 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 503,888B, BPFP=0.9564 +⌛️ [2/4] FRONTEND: Frontend time: 0.520s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.009s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13150742 1007.29877308 + layer.39.0 55.24585459 2261.13556851 + ------------------------------------------------------------------------------------- + TOTAL 27.68868101 1634.21717080 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 999412 +BPFP 0.9485 bits/point +EBPFP 0.9485 equivalent bits/point +MSE 1634.217171 +---------------------- -------------------------------------------------------- +Time: 1.582s Load: 0.052s, Pack+Encode: 0.520s, Decode+Unpack: 1.009s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1634.2172 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000684-ILSVRC2012_val_00000537.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03000684-ILSVRC2012_val_00000537.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03014705-ILSVRC2012_val_00001168.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03014705-ILSVRC2012_val_00001168.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 323,752B, BPFP=0.6145 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 445,676B, BPFP=0.8459 +⌛️ [2/4] FRONTEND: Frontend time: 0.570s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.042s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09787338 111.04198554 + layer.39.0 322.89622813 2442.93440233 + ------------------------------------------------------------------------------------- + TOTAL 161.49705076 1276.98819394 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 769428 +BPFP 0.7302 bits/point +EBPFP 0.7302 equivalent bits/point +MSE 1276.988194 +---------------------- -------------------------------------------------------- +Time: 1.665s Load: 0.052s, Pack+Encode: 0.570s, Decode+Unpack: 1.042s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1276.9882 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03014705-ILSVRC2012_val_00001168.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03014705-ILSVRC2012_val_00001168.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03017168-ILSVRC2012_val_00001601.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03017168-ILSVRC2012_val_00001601.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 372,412B, BPFP=0.7069 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 486,080B, BPFP=0.9226 +⌛️ [2/4] FRONTEND: Frontend time: 0.560s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.032s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10213913 222.30873421 + layer.39.0 475.40952988 3381.50583090 + ------------------------------------------------------------------------------------- + TOTAL 237.75583451 1801.90728256 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 858492 +BPFP 0.8147 bits/point +EBPFP 0.8147 equivalent bits/point +MSE 1801.907283 +---------------------- -------------------------------------------------------- +Time: 1.644s Load: 0.051s, Pack+Encode: 0.560s, Decode+Unpack: 1.032s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1801.9073 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03017168-ILSVRC2012_val_00001601.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03017168-ILSVRC2012_val_00001601.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03018349-ILSVRC2012_val_00000346.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03018349-ILSVRC2012_val_00000346.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 406,328B, BPFP=0.7712 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 549,260B, BPFP=1.0425 +⌛️ [2/4] FRONTEND: Frontend time: 0.584s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.031s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09959339 160.79752794 + layer.39.0 56.59841169 2544.33236152 + ------------------------------------------------------------------------------------- + TOTAL 28.34900254 1352.56494473 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 955588 +BPFP 0.9069 bits/point +EBPFP 0.9069 equivalent bits/point +MSE 1352.564945 +---------------------- -------------------------------------------------------- +Time: 1.666s Load: 0.051s, Pack+Encode: 0.584s, Decode+Unpack: 1.031s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1352.5649 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03018349-ILSVRC2012_val_00000346.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03018349-ILSVRC2012_val_00000346.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03026506-ILSVRC2012_val_00001908.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03026506-ILSVRC2012_val_00001908.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 428,244B, BPFP=0.8128 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 540,504B, BPFP=1.0259 +⌛️ [2/4] FRONTEND: Frontend time: 0.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.035s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10977067 162.70487123 + layer.39.0 668.54063411 3296.97570457 + ------------------------------------------------------------------------------------- + TOTAL 334.32520239 1729.84028790 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 968748 +BPFP 0.9194 bits/point +EBPFP 0.9194 equivalent bits/point +MSE 1729.840288 +---------------------- -------------------------------------------------------- +Time: 1.666s Load: 0.050s, Pack+Encode: 0.581s, Decode+Unpack: 1.035s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1729.8403 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03026506-ILSVRC2012_val_00001908.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03026506-ILSVRC2012_val_00001908.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03028079-ILSVRC2012_val_00003351.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03028079-ILSVRC2012_val_00003351.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 372,240B, BPFP=0.7065 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 459,208B, BPFP=0.8716 +⌛️ [2/4] FRONTEND: Frontend time: 0.596s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.064s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10934904 318.05912901 + layer.39.0 15.31112010 1884.25315841 + ------------------------------------------------------------------------------------- + TOTAL 7.71023457 1101.15614371 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 831448 +BPFP 0.7891 bits/point +EBPFP 0.7891 equivalent bits/point +MSE 1101.156144 +---------------------- -------------------------------------------------------- +Time: 1.741s Load: 0.080s, Pack+Encode: 0.596s, Decode+Unpack: 1.064s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1101.1561 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03028079-ILSVRC2012_val_00003351.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03028079-ILSVRC2012_val_00003351.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03032252-ILSVRC2012_val_00000086.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03032252-ILSVRC2012_val_00000086.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 470,164B, BPFP=0.8924 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 496,892B, BPFP=0.9431 +⌛️ [2/4] FRONTEND: Frontend time: 0.571s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.071s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13507480 760.44521380 + layer.39.0 103.55165816 2109.49781341 + ------------------------------------------------------------------------------------- + TOTAL 51.84336648 1434.97151361 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 967056 +BPFP 0.9178 bits/point +EBPFP 0.9178 equivalent bits/point +MSE 1434.971514 +---------------------- -------------------------------------------------------- +Time: 1.692s Load: 0.050s, Pack+Encode: 0.571s, Decode+Unpack: 1.071s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1434.9715 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03032252-ILSVRC2012_val_00000086.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03032252-ILSVRC2012_val_00000086.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03041632-ILSVRC2012_val_00000564.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03041632-ILSVRC2012_val_00000564.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 316,584B, BPFP=0.6009 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 425,668B, BPFP=0.8080 +⌛️ [2/4] FRONTEND: Frontend time: 0.589s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.072s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10123130 467.11443149 + layer.39.0 371.34277818 3333.55344995 + ------------------------------------------------------------------------------------- + TOTAL 185.72200474 1900.33394072 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 742252 +BPFP 0.7044 bits/point +EBPFP 0.7044 equivalent bits/point +MSE 1900.333941 +---------------------- -------------------------------------------------------- +Time: 1.731s Load: 0.071s, Pack+Encode: 0.589s, Decode+Unpack: 1.072s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1900.3339 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03041632-ILSVRC2012_val_00000564.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03041632-ILSVRC2012_val_00000564.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03042490-ILSVRC2012_val_00001426.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03042490-ILSVRC2012_val_00001426.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.081s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 443,224B, BPFP=0.8413 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 444,268B, BPFP=0.8433 +⌛️ [2/4] FRONTEND: Frontend time: 0.592s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.034s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10706725 237.31274295 + layer.39.0 141.71039845 3109.56632653 + ------------------------------------------------------------------------------------- + TOTAL 70.90873285 1673.43953474 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 887492 +BPFP 0.8423 bits/point +EBPFP 0.8423 equivalent bits/point +MSE 1673.439535 +---------------------- -------------------------------------------------------- +Time: 1.706s Load: 0.081s, Pack+Encode: 0.592s, Decode+Unpack: 1.034s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1673.4395 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03042490-ILSVRC2012_val_00001426.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03042490-ILSVRC2012_val_00001426.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03047690-ILSVRC2012_val_00001500.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03047690-ILSVRC2012_val_00001500.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 359,944B, BPFP=0.6832 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 516,484B, BPFP=0.9803 +⌛️ [2/4] FRONTEND: Frontend time: 0.590s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.029s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09570478 52.44822036 + layer.39.0 226.76483540 2745.99368319 + ------------------------------------------------------------------------------------- + TOTAL 113.43027009 1399.22095177 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 876428 +BPFP 0.8318 bits/point +EBPFP 0.8318 equivalent bits/point +MSE 1399.220952 +---------------------- -------------------------------------------------------- +Time: 1.678s Load: 0.059s, Pack+Encode: 0.590s, Decode+Unpack: 1.029s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1399.2210 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03047690-ILSVRC2012_val_00001500.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03047690-ILSVRC2012_val_00001500.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03062245-ILSVRC2012_val_00000344.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03062245-ILSVRC2012_val_00000344.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 330,460B, BPFP=0.6272 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 432,392B, BPFP=0.8207 +⌛️ [2/4] FRONTEND: Frontend time: 0.583s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.025s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09619164 123.64873360 + layer.39.0 46.71096787 2094.76166181 + ------------------------------------------------------------------------------------- + TOTAL 23.40357976 1109.20519770 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 762852 +BPFP 0.7240 bits/point +EBPFP 0.7240 equivalent bits/point +MSE 1109.205198 +---------------------- -------------------------------------------------------- +Time: 1.670s Load: 0.062s, Pack+Encode: 0.583s, Decode+Unpack: 1.025s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1109.2052 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03062245-ILSVRC2012_val_00000344.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03062245-ILSVRC2012_val_00000344.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063599-ILSVRC2012_val_00000164.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063599-ILSVRC2012_val_00000164.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 397,500B, BPFP=0.7545 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 530,956B, BPFP=1.0078 +⌛️ [2/4] FRONTEND: Frontend time: 0.599s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.068s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10111790 185.81884718 + layer.39.0 9.80528160 1869.14139942 + ------------------------------------------------------------------------------------- + TOTAL 4.95319975 1027.48012330 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 928456 +BPFP 0.8811 bits/point +EBPFP 0.8811 equivalent bits/point +MSE 1027.480123 +---------------------- -------------------------------------------------------- +Time: 1.747s Load: 0.080s, Pack+Encode: 0.599s, Decode+Unpack: 1.068s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1027.4801 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063599-ILSVRC2012_val_00000164.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03063599-ILSVRC2012_val_00000164.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063689-ILSVRC2012_val_00001940.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063689-ILSVRC2012_val_00001940.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 364,380B, BPFP=0.6916 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 477,972B, BPFP=0.9072 +⌛️ [2/4] FRONTEND: Frontend time: 0.584s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.075s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09645106 111.88682884 + layer.39.0 18.48014797 2849.75485909 + ------------------------------------------------------------------------------------- + TOTAL 9.28829952 1480.82084396 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 842352 +BPFP 0.7994 bits/point +EBPFP 0.7994 equivalent bits/point +MSE 1480.820844 +---------------------- -------------------------------------------------------- +Time: 1.731s Load: 0.071s, Pack+Encode: 0.584s, Decode+Unpack: 1.075s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1480.8208 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063689-ILSVRC2012_val_00001940.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03063689-ILSVRC2012_val_00001940.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03065424-ILSVRC2012_val_00000915.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03065424-ILSVRC2012_val_00000915.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 478,004B, BPFP=0.9073 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 507,580B, BPFP=0.9634 +⌛️ [2/4] FRONTEND: Frontend time: 0.600s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.063s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12384982 673.58047862 + layer.39.0 2154.15986395 4037.06608358 + ------------------------------------------------------------------------------------- + TOTAL 1077.14185688 2355.32328110 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 985584 +BPFP 0.9354 bits/point +EBPFP 0.9354 equivalent bits/point +MSE 2355.323281 +---------------------- -------------------------------------------------------- +Time: 1.714s Load: 0.051s, Pack+Encode: 0.600s, Decode+Unpack: 1.063s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2355.3233 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03065424-ILSVRC2012_val_00000915.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03065424-ILSVRC2012_val_00000915.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03075370-ILSVRC2012_val_00004971.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03075370-ILSVRC2012_val_00004971.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 370,644B, BPFP=0.7035 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 441,208B, BPFP=0.8374 +⌛️ [2/4] FRONTEND: Frontend time: 0.588s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.062s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10672879 161.90293975 + layer.39.0 301.29020894 2256.23785228 + ------------------------------------------------------------------------------------- + TOTAL 150.69846886 1209.07039602 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 811852 +BPFP 0.7705 bits/point +EBPFP 0.7705 equivalent bits/point +MSE 1209.070396 +---------------------- -------------------------------------------------------- +Time: 1.702s Load: 0.052s, Pack+Encode: 0.588s, Decode+Unpack: 1.062s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1209.0704 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03075370-ILSVRC2012_val_00004971.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03075370-ILSVRC2012_val_00004971.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03089624-ILSVRC2012_val_00001190.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03089624-ILSVRC2012_val_00001190.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.081s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 422,372B, BPFP=0.8017 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 504,892B, BPFP=0.9583 +⌛️ [2/4] FRONTEND: Frontend time: 0.578s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.075s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10385029 174.25068331 + layer.39.0 606.38896987 3127.22521866 + ------------------------------------------------------------------------------------- + TOTAL 303.24641008 1650.73795098 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 927264 +BPFP 0.8800 bits/point +EBPFP 0.8800 equivalent bits/point +MSE 1650.737951 +---------------------- -------------------------------------------------------- +Time: 1.734s Load: 0.081s, Pack+Encode: 0.578s, Decode+Unpack: 1.075s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1650.7380 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03089624-ILSVRC2012_val_00001190.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03089624-ILSVRC2012_val_00001190.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03095699-ILSVRC2012_val_00000403.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03095699-ILSVRC2012_val_00000403.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.074s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 463,860B, BPFP=0.8804 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 482,348B, BPFP=0.9155 +⌛️ [2/4] FRONTEND: Frontend time: 0.567s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.066s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12139760 920.28377065 + layer.39.0 62.59250486 2675.19582119 + ------------------------------------------------------------------------------------- + TOTAL 31.35695123 1797.73979592 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 946208 +BPFP 0.8980 bits/point +EBPFP 0.8980 equivalent bits/point +MSE 1797.739796 +---------------------- -------------------------------------------------------- +Time: 1.707s Load: 0.074s, Pack+Encode: 0.567s, Decode+Unpack: 1.066s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1797.7398 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03095699-ILSVRC2012_val_00000403.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03095699-ILSVRC2012_val_00000403.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03100240-ILSVRC2012_val_00001201.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03100240-ILSVRC2012_val_00001201.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.079s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 406,872B, BPFP=0.7723 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 385,604B, BPFP=0.7319 +⌛️ [2/4] FRONTEND: Frontend time: 0.589s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.067s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10258218 678.44150875 + layer.39.0 42.98202138 1999.52089407 + ------------------------------------------------------------------------------------- + TOTAL 21.54230178 1338.98120141 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 792476 +BPFP 0.7521 bits/point +EBPFP 0.7521 equivalent bits/point +MSE 1338.981201 +---------------------- -------------------------------------------------------- +Time: 1.736s Load: 0.079s, Pack+Encode: 0.589s, Decode+Unpack: 1.067s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1338.9812 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03100240-ILSVRC2012_val_00001201.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03100240-ILSVRC2012_val_00001201.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03109150-ILSVRC2012_val_00000678.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03109150-ILSVRC2012_val_00000678.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 415,864B, BPFP=0.7893 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 497,540B, BPFP=0.9444 +⌛️ [2/4] FRONTEND: Frontend time: 0.593s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.079s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09720685 332.67182945 + layer.39.0 496.21158285 3186.96938776 + ------------------------------------------------------------------------------------- + TOTAL 248.15439485 1759.82060860 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 913404 +BPFP 0.8669 bits/point +EBPFP 0.8669 equivalent bits/point +MSE 1759.820609 +---------------------- -------------------------------------------------------- +Time: 1.743s Load: 0.071s, Pack+Encode: 0.593s, Decode+Unpack: 1.079s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1759.8206 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03109150-ILSVRC2012_val_00000678.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03109150-ILSVRC2012_val_00000678.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03110669-ILSVRC2012_val_00002171.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03110669-ILSVRC2012_val_00002171.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 493,584B, BPFP=0.9369 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 555,408B, BPFP=1.0542 +⌛️ [2/4] FRONTEND: Frontend time: 0.584s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.070s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.15128201 1005.55782313 + layer.39.0 15.00769387 2443.98688047 + ------------------------------------------------------------------------------------- + TOTAL 7.57948794 1724.77235180 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1048992 +BPFP 0.9955 bits/point +EBPFP 0.9955 equivalent bits/point +MSE 1724.772352 +---------------------- -------------------------------------------------------- +Time: 1.725s Load: 0.071s, Pack+Encode: 0.584s, Decode+Unpack: 1.070s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1724.7724 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03110669-ILSVRC2012_val_00002171.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03110669-ILSVRC2012_val_00002171.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124043-ILSVRC2012_val_00000766.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124043-ILSVRC2012_val_00000766.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 354,000B, BPFP=0.6719 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 469,624B, BPFP=0.8914 +⌛️ [2/4] FRONTEND: Frontend time: 0.575s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.076s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11473456 283.54187925 + layer.39.0 54.83309418 2927.17128280 + ------------------------------------------------------------------------------------- + TOTAL 27.47391437 1605.35658103 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 823624 +BPFP 0.7817 bits/point +EBPFP 0.7817 equivalent bits/point +MSE 1605.356581 +---------------------- -------------------------------------------------------- +Time: 1.722s Load: 0.071s, Pack+Encode: 0.575s, Decode+Unpack: 1.076s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1605.3566 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124043-ILSVRC2012_val_00000766.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03124043-ILSVRC2012_val_00000766.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124170-ILSVRC2012_val_00001875.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124170-ILSVRC2012_val_00001875.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 382,732B, BPFP=0.7265 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 392,256B, BPFP=0.7445 +⌛️ [2/4] FRONTEND: Frontend time: 0.598s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.081s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11393612 416.75425170 + layer.39.0 9.06747107 1736.06875607 + ------------------------------------------------------------------------------------- + TOTAL 4.59070360 1076.41150389 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 774988 +BPFP 0.7355 bits/point +EBPFP 0.7355 equivalent bits/point +MSE 1076.411504 +---------------------- -------------------------------------------------------- +Time: 1.729s Load: 0.050s, Pack+Encode: 0.598s, Decode+Unpack: 1.081s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1076.4115 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124170-ILSVRC2012_val_00001875.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03124170-ILSVRC2012_val_00001875.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03126707-ILSVRC2012_val_00000020.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03126707-ILSVRC2012_val_00000020.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 398,900B, BPFP=0.7571 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 390,636B, BPFP=0.7415 +⌛️ [2/4] FRONTEND: Frontend time: 0.563s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.065s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.15273996 819.97363946 + layer.39.0 1033.15269679 2335.72521866 + ------------------------------------------------------------------------------------- + TOTAL 516.65271838 1577.84942906 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 789536 +BPFP 0.7493 bits/point +EBPFP 0.7493 equivalent bits/point +MSE 1577.849429 +---------------------- -------------------------------------------------------- +Time: 1.680s Load: 0.052s, Pack+Encode: 0.563s, Decode+Unpack: 1.065s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1577.8494 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03126707-ILSVRC2012_val_00000020.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03126707-ILSVRC2012_val_00000020.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03127747-ILSVRC2012_val_00001689.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03127747-ILSVRC2012_val_00001689.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 324,956B, BPFP=0.6168 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 448,000B, BPFP=0.8503 +⌛️ [2/4] FRONTEND: Frontend time: 0.563s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.071s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10152024 111.53269254 + layer.39.0 322.92343902 2733.56025267 + ------------------------------------------------------------------------------------- + TOTAL 161.51247963 1422.54647261 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 772956 +BPFP 0.7336 bits/point +EBPFP 0.7336 equivalent bits/point +MSE 1422.546473 +---------------------- -------------------------------------------------------- +Time: 1.706s Load: 0.071s, Pack+Encode: 0.563s, Decode+Unpack: 1.071s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1422.5465 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03127747-ILSVRC2012_val_00001689.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03127747-ILSVRC2012_val_00001689.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03131574-ILSVRC2012_val_00003036.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03131574-ILSVRC2012_val_00003036.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 355,532B, BPFP=0.6748 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 467,472B, BPFP=0.8873 +⌛️ [2/4] FRONTEND: Frontend time: 0.582s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.062s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09568423 197.08627915 + layer.39.0 163.24681122 2943.23712342 + ------------------------------------------------------------------------------------- + TOTAL 81.67124773 1570.16170129 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 823004 +BPFP 0.7811 bits/point +EBPFP 0.7811 equivalent bits/point +MSE 1570.161701 +---------------------- -------------------------------------------------------- +Time: 1.715s Load: 0.070s, Pack+Encode: 0.582s, Decode+Unpack: 1.062s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1570.1617 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03131574-ILSVRC2012_val_00003036.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03131574-ILSVRC2012_val_00003036.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03133878-ILSVRC2012_val_00000534.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03133878-ILSVRC2012_val_00000534.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 470,756B, BPFP=0.8935 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 507,220B, BPFP=0.9627 +⌛️ [2/4] FRONTEND: Frontend time: 0.593s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.072s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11186348 471.83515549 + layer.39.0 28.46096218 2868.90111759 + ------------------------------------------------------------------------------------- + TOTAL 14.28641283 1670.36813654 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 977976 +BPFP 0.9281 bits/point +EBPFP 0.9281 equivalent bits/point +MSE 1670.368137 +---------------------- -------------------------------------------------------- +Time: 1.716s Load: 0.050s, Pack+Encode: 0.593s, Decode+Unpack: 1.072s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1670.3681 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03133878-ILSVRC2012_val_00000534.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03133878-ILSVRC2012_val_00000534.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03134739-ILSVRC2012_val_00000249.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03134739-ILSVRC2012_val_00000249.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 378,104B, BPFP=0.7177 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 528,452B, BPFP=1.0030 +⌛️ [2/4] FRONTEND: Frontend time: 0.583s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.066s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09967384 74.14236364 + layer.39.0 372.24465500 3295.16277940 + ------------------------------------------------------------------------------------- + TOTAL 186.17216442 1684.65257152 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 906556 +BPFP 0.8604 bits/point +EBPFP 0.8604 equivalent bits/point +MSE 1684.652572 +---------------------- -------------------------------------------------------- +Time: 1.721s Load: 0.071s, Pack+Encode: 0.583s, Decode+Unpack: 1.066s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1684.6526 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03134739-ILSVRC2012_val_00000249.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03134739-ILSVRC2012_val_00000249.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03141823-ILSVRC2012_val_00001337.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03141823-ILSVRC2012_val_00001337.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 440,228B, BPFP=0.8356 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 557,584B, BPFP=1.0583 +⌛️ [2/4] FRONTEND: Frontend time: 0.574s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.088s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10422104 284.65424563 + layer.39.0 29.45558301 2595.95991254 + ------------------------------------------------------------------------------------- + TOTAL 14.77990203 1440.30707908 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 997812 +BPFP 0.9470 bits/point +EBPFP 0.9470 equivalent bits/point +MSE 1440.307079 +---------------------- -------------------------------------------------------- +Time: 1.733s Load: 0.070s, Pack+Encode: 0.574s, Decode+Unpack: 1.088s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1440.3071 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03141823-ILSVRC2012_val_00001337.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03141823-ILSVRC2012_val_00001337.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03160309-ILSVRC2012_val_00000330.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03160309-ILSVRC2012_val_00000330.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 366,144B, BPFP=0.6950 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 338,692B, BPFP=0.6429 +⌛️ [2/4] FRONTEND: Frontend time: 0.509s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.024s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09980877 380.43446307 + layer.39.0 30.04123011 1635.26907191 + ------------------------------------------------------------------------------------- + TOTAL 15.07051944 1007.85176749 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 704836 +BPFP 0.6689 bits/point +EBPFP 0.6689 equivalent bits/point +MSE 1007.851767 +---------------------- -------------------------------------------------------- +Time: 1.583s Load: 0.051s, Pack+Encode: 0.509s, Decode+Unpack: 1.024s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1007.8518 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03160309-ILSVRC2012_val_00000330.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03160309-ILSVRC2012_val_00000330.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03187595-ILSVRC2012_val_00000137.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03187595-ILSVRC2012_val_00000137.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 391,820B, BPFP=0.7437 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 504,416B, BPFP=0.9574 +⌛️ [2/4] FRONTEND: Frontend time: 0.600s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.068s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10716813 196.69040027 + layer.39.0 12.39187394 2207.30879495 + ------------------------------------------------------------------------------------- + TOTAL 6.24952103 1201.99959761 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 896236 +BPFP 0.8506 bits/point +EBPFP 0.8506 equivalent bits/point +MSE 1201.999598 +---------------------- -------------------------------------------------------- +Time: 1.717s Load: 0.050s, Pack+Encode: 0.600s, Decode+Unpack: 1.068s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1201.9996 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03187595-ILSVRC2012_val_00000137.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03187595-ILSVRC2012_val_00000137.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03188531-ILSVRC2012_val_00000493.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03188531-ILSVRC2012_val_00000493.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 326,316B, BPFP=0.6194 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 468,716B, BPFP=0.8897 +⌛️ [2/4] FRONTEND: Frontend time: 0.573s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.066s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09509044 99.74665938 + layer.39.0 10.77256154 2277.90889213 + ------------------------------------------------------------------------------------- + TOTAL 5.43382599 1188.82777575 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 795032 +BPFP 0.7545 bits/point +EBPFP 0.7545 equivalent bits/point +MSE 1188.827776 +---------------------- -------------------------------------------------------- +Time: 1.709s Load: 0.070s, Pack+Encode: 0.573s, Decode+Unpack: 1.066s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1188.8278 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03188531-ILSVRC2012_val_00000493.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03188531-ILSVRC2012_val_00000493.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03196217-ILSVRC2012_val_00003643.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03196217-ILSVRC2012_val_00003643.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 336,884B, BPFP=0.6394 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 474,288B, BPFP=0.9002 +⌛️ [2/4] FRONTEND: Frontend time: 0.570s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.061s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09478207 24.88952032 + layer.39.0 65.57403274 3346.18561710 + ------------------------------------------------------------------------------------- + TOTAL 32.83440740 1685.53756871 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 811172 +BPFP 0.7698 bits/point +EBPFP 0.7698 equivalent bits/point +MSE 1685.537569 +---------------------- -------------------------------------------------------- +Time: 1.702s Load: 0.071s, Pack+Encode: 0.570s, Decode+Unpack: 1.061s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1685.5376 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03196217-ILSVRC2012_val_00003643.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03196217-ILSVRC2012_val_00003643.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03201208-ILSVRC2012_val_00000241.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03201208-ILSVRC2012_val_00000241.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 425,392B, BPFP=0.8074 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 445,128B, BPFP=0.8449 +⌛️ [2/4] FRONTEND: Frontend time: 0.586s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.067s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10331685 504.60659621 + layer.39.0 136.59314261 1994.07920311 + ------------------------------------------------------------------------------------- + TOTAL 68.34822973 1249.34289966 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 870520 +BPFP 0.8262 bits/point +EBPFP 0.8262 equivalent bits/point +MSE 1249.342900 +---------------------- -------------------------------------------------------- +Time: 1.704s Load: 0.051s, Pack+Encode: 0.586s, Decode+Unpack: 1.067s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1249.3429 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03201208-ILSVRC2012_val_00000241.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03201208-ILSVRC2012_val_00000241.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03207743-ILSVRC2012_val_00000256.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03207743-ILSVRC2012_val_00000256.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 481,792B, BPFP=0.9145 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 421,248B, BPFP=0.7996 +⌛️ [2/4] FRONTEND: Frontend time: 0.591s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.068s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09674843 1070.79239553 + layer.39.0 189.63590258 2806.13362488 + ------------------------------------------------------------------------------------- + TOTAL 94.86632550 1938.46301020 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 903040 +BPFP 0.8570 bits/point +EBPFP 0.8570 equivalent bits/point +MSE 1938.463010 +---------------------- -------------------------------------------------------- +Time: 1.731s Load: 0.072s, Pack+Encode: 0.591s, Decode+Unpack: 1.068s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1938.4630 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03207743-ILSVRC2012_val_00000256.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03207743-ILSVRC2012_val_00000256.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03216828-ILSVRC2012_val_00001729.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03216828-ILSVRC2012_val_00001729.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.053s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 412,512B, BPFP=0.7830 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 436,972B, BPFP=0.8294 +⌛️ [2/4] FRONTEND: Frontend time: 0.565s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.066s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10800209 638.27544947 + layer.39.0 31.30713223 1972.38678328 + ------------------------------------------------------------------------------------- + TOTAL 15.70756716 1305.33111638 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 849484 +BPFP 0.8062 bits/point +EBPFP 0.8062 equivalent bits/point +MSE 1305.331116 +---------------------- -------------------------------------------------------- +Time: 1.683s Load: 0.053s, Pack+Encode: 0.565s, Decode+Unpack: 1.066s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1305.3311 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03216828-ILSVRC2012_val_00001729.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03216828-ILSVRC2012_val_00001729.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03218198-ILSVRC2012_val_00002266.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03218198-ILSVRC2012_val_00002266.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 470,836B, BPFP=0.8937 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 532,692B, BPFP=1.0111 +⌛️ [2/4] FRONTEND: Frontend time: 0.574s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.070s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11617067 689.88818027 + layer.39.0 195.83184524 2555.55928086 + ------------------------------------------------------------------------------------- + TOTAL 97.97400795 1622.72373056 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1003528 +BPFP 0.9524 bits/point +EBPFP 0.9524 equivalent bits/point +MSE 1622.723731 +---------------------- -------------------------------------------------------- +Time: 1.715s Load: 0.070s, Pack+Encode: 0.574s, Decode+Unpack: 1.070s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1622.7237 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03218198-ILSVRC2012_val_00002266.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03218198-ILSVRC2012_val_00002266.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03220513-ILSVRC2012_val_00001868.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03220513-ILSVRC2012_val_00001868.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 578,560B, BPFP=1.0982 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 492,324B, BPFP=0.9345 +⌛️ [2/4] FRONTEND: Frontend time: 0.592s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.082s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.20032125 1834.69120505 + layer.39.0 377.00176142 3494.60325559 + ------------------------------------------------------------------------------------- + TOTAL 188.60104134 2664.64723032 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1070884 +BPFP 1.0163 bits/point +EBPFP 1.0163 equivalent bits/point +MSE 2664.647230 +---------------------- -------------------------------------------------------- +Time: 1.743s Load: 0.070s, Pack+Encode: 0.592s, Decode+Unpack: 1.082s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2664.6472 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03220513-ILSVRC2012_val_00001868.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03220513-ILSVRC2012_val_00001868.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03223299-ILSVRC2012_val_00001893.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03223299-ILSVRC2012_val_00001893.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 335,464B, BPFP=0.6367 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 415,024B, BPFP=0.7877 +⌛️ [2/4] FRONTEND: Frontend time: 0.587s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.059s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10735053 196.88269862 + layer.39.0 354.51621720 2283.18999028 + ------------------------------------------------------------------------------------- + TOTAL 177.31178386 1240.03634445 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 750488 +BPFP 0.7122 bits/point +EBPFP 0.7122 equivalent bits/point +MSE 1240.036344 +---------------------- -------------------------------------------------------- +Time: 1.698s Load: 0.052s, Pack+Encode: 0.587s, Decode+Unpack: 1.059s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1240.0363 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03223299-ILSVRC2012_val_00001893.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03223299-ILSVRC2012_val_00001893.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03240683-ILSVRC2012_val_00000504.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03240683-ILSVRC2012_val_00000504.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.053s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 350,260B, BPFP=0.6648 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 468,736B, BPFP=0.8897 +⌛️ [2/4] FRONTEND: Frontend time: 0.569s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.063s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10065408 347.11509961 + layer.39.0 443.53838678 2761.09256560 + ------------------------------------------------------------------------------------- + TOTAL 221.81952043 1554.10383260 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 818996 +BPFP 0.7773 bits/point +EBPFP 0.7773 equivalent bits/point +MSE 1554.103833 +---------------------- -------------------------------------------------------- +Time: 1.685s Load: 0.053s, Pack+Encode: 0.569s, Decode+Unpack: 1.063s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1554.1038 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03240683-ILSVRC2012_val_00000504.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03240683-ILSVRC2012_val_00000504.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03250847-ILSVRC2012_val_00000542.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03250847-ILSVRC2012_val_00000542.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 423,120B, BPFP=0.8031 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 524,728B, BPFP=0.9960 +⌛️ [2/4] FRONTEND: Frontend time: 0.594s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.083s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10136319 295.61807580 + layer.39.0 140.24735787 3159.70845481 + ------------------------------------------------------------------------------------- + TOTAL 70.17436053 1727.66326531 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 947848 +BPFP 0.8995 bits/point +EBPFP 0.8995 equivalent bits/point +MSE 1727.663265 +---------------------- -------------------------------------------------------- +Time: 1.746s Load: 0.070s, Pack+Encode: 0.594s, Decode+Unpack: 1.083s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1727.6633 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03250847-ILSVRC2012_val_00000542.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03250847-ILSVRC2012_val_00000542.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03255030-ILSVRC2012_val_00001045.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03255030-ILSVRC2012_val_00001045.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 369,508B, BPFP=0.7014 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 443,876B, BPFP=0.8425 +⌛️ [2/4] FRONTEND: Frontend time: 0.506s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.029s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10050351 51.87526573 + layer.39.0 12.06722622 2056.43780369 + ------------------------------------------------------------------------------------- + TOTAL 6.08386487 1054.15653471 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 813384 +BPFP 0.7719 bits/point +EBPFP 0.7719 equivalent bits/point +MSE 1054.156535 +---------------------- -------------------------------------------------------- +Time: 1.584s Load: 0.050s, Pack+Encode: 0.506s, Decode+Unpack: 1.029s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1054.1565 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03255030-ILSVRC2012_val_00001045.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03255030-ILSVRC2012_val_00001045.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03271574-ILSVRC2012_val_00000942.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03271574-ILSVRC2012_val_00000942.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 374,724B, BPFP=0.7113 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 441,348B, BPFP=0.8377 +⌛️ [2/4] FRONTEND: Frontend time: 0.540s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.012s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10164264 134.42088800 + layer.39.0 660.63544704 3371.70529640 + ------------------------------------------------------------------------------------- + TOTAL 330.36854484 1753.06309220 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 816072 +BPFP 0.7745 bits/point +EBPFP 0.7745 equivalent bits/point +MSE 1753.063092 +---------------------- -------------------------------------------------------- +Time: 1.602s Load: 0.050s, Pack+Encode: 0.540s, Decode+Unpack: 1.012s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1753.0631 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03271574-ILSVRC2012_val_00000942.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03271574-ILSVRC2012_val_00000942.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272010-ILSVRC2012_val_00000374.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272010-ILSVRC2012_val_00000374.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 378,184B, BPFP=0.7178 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 515,068B, BPFP=0.9776 +⌛️ [2/4] FRONTEND: Frontend time: 0.588s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.077s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10420663 135.15061650 + layer.39.0 9.63653369 1856.76360544 + ------------------------------------------------------------------------------------- + TOTAL 4.87037016 995.95711097 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 893252 +BPFP 0.8477 bits/point +EBPFP 0.8477 equivalent bits/point +MSE 995.957111 +---------------------- -------------------------------------------------------- +Time: 1.724s Load: 0.060s, Pack+Encode: 0.588s, Decode+Unpack: 1.077s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 995.9571 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272010-ILSVRC2012_val_00000374.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03272010-ILSVRC2012_val_00000374.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272562-ILSVRC2012_val_00001699.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272562-ILSVRC2012_val_00001699.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 437,296B, BPFP=0.8300 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 406,444B, BPFP=0.7715 +⌛️ [2/4] FRONTEND: Frontend time: 0.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.066s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11399285 522.94175170 + layer.39.0 12.79457642 2342.41375121 + ------------------------------------------------------------------------------------- + TOTAL 6.45428464 1432.67775146 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 843740 +BPFP 0.8007 bits/point +EBPFP 0.8007 equivalent bits/point +MSE 1432.677751 +---------------------- -------------------------------------------------------- +Time: 1.700s Load: 0.052s, Pack+Encode: 0.581s, Decode+Unpack: 1.066s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1432.6778 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272562-ILSVRC2012_val_00001699.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03272562-ILSVRC2012_val_00001699.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03290653-ILSVRC2012_val_00000199.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03290653-ILSVRC2012_val_00000199.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 344,012B, BPFP=0.6530 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 480,260B, BPFP=0.9116 +⌛️ [2/4] FRONTEND: Frontend time: 0.589s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.068s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09581849 135.47020773 + layer.39.0 9.30266794 1856.34620991 + ------------------------------------------------------------------------------------- + TOTAL 4.69924322 995.90820882 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 824272 +BPFP 0.7823 bits/point +EBPFP 0.7823 equivalent bits/point +MSE 995.908209 +---------------------- -------------------------------------------------------- +Time: 1.709s Load: 0.052s, Pack+Encode: 0.589s, Decode+Unpack: 1.068s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 995.9082 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03290653-ILSVRC2012_val_00000199.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03290653-ILSVRC2012_val_00000199.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03291819-ILSVRC2012_val_00000419.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03291819-ILSVRC2012_val_00000419.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 318,312B, BPFP=0.6042 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 399,088B, BPFP=0.7575 +⌛️ [2/4] FRONTEND: Frontend time: 0.575s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.074s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10621172 111.42466138 + layer.39.0 31.36357166 1920.80065598 + ------------------------------------------------------------------------------------- + TOTAL 15.73489169 1016.11265868 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 717400 +BPFP 0.6808 bits/point +EBPFP 0.6808 equivalent bits/point +MSE 1016.112659 +---------------------- -------------------------------------------------------- +Time: 1.720s Load: 0.071s, Pack+Encode: 0.575s, Decode+Unpack: 1.074s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1016.1127 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03291819-ILSVRC2012_val_00000419.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03291819-ILSVRC2012_val_00000419.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03314780-ILSVRC2012_val_00000624.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03314780-ILSVRC2012_val_00000624.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 393,900B, BPFP=0.7477 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 530,040B, BPFP=1.0061 +⌛️ [2/4] FRONTEND: Frontend time: 0.571s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.069s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10172509 209.89812621 + layer.39.0 35.60390853 2999.71622935 + ------------------------------------------------------------------------------------- + TOTAL 17.85281681 1604.80717778 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 923940 +BPFP 0.8769 bits/point +EBPFP 0.8769 equivalent bits/point +MSE 1604.807178 +---------------------- -------------------------------------------------------- +Time: 1.692s Load: 0.052s, Pack+Encode: 0.571s, Decode+Unpack: 1.069s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1604.8072 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03314780-ILSVRC2012_val_00000624.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03314780-ILSVRC2012_val_00000624.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03325584-ILSVRC2012_val_00001256.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03325584-ILSVRC2012_val_00001256.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.088s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 464,632B, BPFP=0.8819 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 532,160B, BPFP=1.0101 +⌛️ [2/4] FRONTEND: Frontend time: 0.584s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.079s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11348933 653.22327502 + layer.39.0 26.85401292 2386.01870748 + ------------------------------------------------------------------------------------- + TOTAL 13.48375113 1519.62099125 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 996792 +BPFP 0.9460 bits/point +EBPFP 0.9460 equivalent bits/point +MSE 1519.620991 +---------------------- -------------------------------------------------------- +Time: 1.752s Load: 0.088s, Pack+Encode: 0.584s, Decode+Unpack: 1.079s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1519.6210 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03325584-ILSVRC2012_val_00001256.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03325584-ILSVRC2012_val_00001256.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03337140-ILSVRC2012_val_00000132.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03337140-ILSVRC2012_val_00000132.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 330,008B, BPFP=0.6264 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 444,140B, BPFP=0.8430 +⌛️ [2/4] FRONTEND: Frontend time: 0.579s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.059s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09852950 184.80546951 + layer.39.0 10.39905343 1902.30915938 + ------------------------------------------------------------------------------------- + TOTAL 5.24879146 1043.55731444 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 774148 +BPFP 0.7347 bits/point +EBPFP 0.7347 equivalent bits/point +MSE 1043.557314 +---------------------- -------------------------------------------------------- +Time: 1.708s Load: 0.070s, Pack+Encode: 0.579s, Decode+Unpack: 1.059s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1043.5573 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03337140-ILSVRC2012_val_00000132.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03337140-ILSVRC2012_val_00000132.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03344393-ILSVRC2012_val_00000288.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03344393-ILSVRC2012_val_00000288.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 350,140B, BPFP=0.6646 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 435,424B, BPFP=0.8265 +⌛️ [2/4] FRONTEND: Frontend time: 0.563s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.071s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09830858 160.86561589 + layer.39.0 109.00505649 2753.37512148 + ------------------------------------------------------------------------------------- + TOTAL 54.55168253 1457.12036868 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 785564 +BPFP 0.7455 bits/point +EBPFP 0.7455 equivalent bits/point +MSE 1457.120369 +---------------------- -------------------------------------------------------- +Time: 1.686s Load: 0.052s, Pack+Encode: 0.563s, Decode+Unpack: 1.071s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1457.1204 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03344393-ILSVRC2012_val_00000288.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03344393-ILSVRC2012_val_00000288.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03345487-ILSVRC2012_val_00000764.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03345487-ILSVRC2012_val_00000764.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 411,964B, BPFP=0.7819 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 471,740B, BPFP=0.8954 +⌛️ [2/4] FRONTEND: Frontend time: 0.568s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.079s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10639974 307.47625121 + layer.39.0 14.55993569 2523.40136054 + ------------------------------------------------------------------------------------- + TOTAL 7.33316771 1415.43880588 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 883704 +BPFP 0.8387 bits/point +EBPFP 0.8387 equivalent bits/point +MSE 1415.438806 +---------------------- -------------------------------------------------------- +Time: 1.698s Load: 0.052s, Pack+Encode: 0.568s, Decode+Unpack: 1.079s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1415.4388 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03345487-ILSVRC2012_val_00000764.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03345487-ILSVRC2012_val_00000764.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03347037-ILSVRC2012_val_00000743.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03347037-ILSVRC2012_val_00000743.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 480,808B, BPFP=0.9126 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 486,788B, BPFP=0.9240 +⌛️ [2/4] FRONTEND: Frontend time: 0.594s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.074s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14351733 775.78784014 + layer.39.0 355.98426871 2648.06681244 + ------------------------------------------------------------------------------------- + TOTAL 178.06389302 1711.92732629 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 967596 +BPFP 0.9183 bits/point +EBPFP 0.9183 equivalent bits/point +MSE 1711.927326 +---------------------- -------------------------------------------------------- +Time: 1.720s Load: 0.052s, Pack+Encode: 0.594s, Decode+Unpack: 1.074s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1711.9273 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03347037-ILSVRC2012_val_00000743.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03347037-ILSVRC2012_val_00000743.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03355925-ILSVRC2012_val_00000445.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03355925-ILSVRC2012_val_00000445.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 324,212B, BPFP=0.6154 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 396,688B, BPFP=0.7529 +⌛️ [2/4] FRONTEND: Frontend time: 0.588s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.076s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09979894 87.64201440 + layer.39.0 9.06502540 1776.23530126 + ------------------------------------------------------------------------------------- + TOTAL 4.58241217 931.93865783 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 720900 +BPFP 0.6842 bits/point +EBPFP 0.6842 equivalent bits/point +MSE 931.938658 +---------------------- -------------------------------------------------------- +Time: 1.716s Load: 0.052s, Pack+Encode: 0.588s, Decode+Unpack: 1.076s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 931.9387 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03355925-ILSVRC2012_val_00000445.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03355925-ILSVRC2012_val_00000445.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03376595-ILSVRC2012_val_00001616.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03376595-ILSVRC2012_val_00001616.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 441,488B, BPFP=0.8380 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 522,564B, BPFP=0.9919 +⌛️ [2/4] FRONTEND: Frontend time: 0.573s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.073s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09988844 528.91022838 + layer.39.0 1408.20760447 3851.89917396 + ------------------------------------------------------------------------------------- + TOTAL 704.15374646 2190.40470117 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 964052 +BPFP 0.9149 bits/point +EBPFP 0.9149 equivalent bits/point +MSE 2190.404701 +---------------------- -------------------------------------------------------- +Time: 1.696s Load: 0.050s, Pack+Encode: 0.573s, Decode+Unpack: 1.073s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 2190.4047 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03376595-ILSVRC2012_val_00001616.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03376595-ILSVRC2012_val_00001616.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03379051-ILSVRC2012_val_00002562.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03379051-ILSVRC2012_val_00002562.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 437,784B, BPFP=0.8309 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 504,548B, BPFP=0.9577 +⌛️ [2/4] FRONTEND: Frontend time: 0.597s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.080s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10889592 446.74009961 + layer.39.0 102.95462828 3456.08503401 + ------------------------------------------------------------------------------------- + TOTAL 51.53176210 1951.41256681 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 942332 +BPFP 0.8943 bits/point +EBPFP 0.8943 equivalent bits/point +MSE 1951.412567 +---------------------- -------------------------------------------------------- +Time: 1.729s Load: 0.051s, Pack+Encode: 0.597s, Decode+Unpack: 1.080s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1951.4126 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03379051-ILSVRC2012_val_00002562.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03379051-ILSVRC2012_val_00002562.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388043-ILSVRC2012_val_00001018.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388043-ILSVRC2012_val_00001018.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.073s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 379,476B, BPFP=0.7203 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 429,448B, BPFP=0.8151 +⌛️ [2/4] FRONTEND: Frontend time: 0.570s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.062s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09747427 161.11517553 + layer.39.0 21.12933142 2275.13483965 + ------------------------------------------------------------------------------------- + TOTAL 10.61340285 1218.12500759 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 808924 +BPFP 0.7677 bits/point +EBPFP 0.7677 equivalent bits/point +MSE 1218.125008 +---------------------- -------------------------------------------------------- +Time: 1.704s Load: 0.073s, Pack+Encode: 0.570s, Decode+Unpack: 1.062s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1218.1250 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388043-ILSVRC2012_val_00001018.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03388043-ILSVRC2012_val_00001018.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388183-ILSVRC2012_val_00002799.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388183-ILSVRC2012_val_00002799.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 442,648B, BPFP=0.8402 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 510,752B, BPFP=0.9694 +⌛️ [2/4] FRONTEND: Frontend time: 0.580s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.080s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10066175 332.70277575 + layer.39.0 786.68810739 3183.30976676 + ------------------------------------------------------------------------------------- + TOTAL 393.39438457 1758.00627126 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 953400 +BPFP 0.9048 bits/point +EBPFP 0.9048 equivalent bits/point +MSE 1758.006271 +---------------------- -------------------------------------------------------- +Time: 1.730s Load: 0.070s, Pack+Encode: 0.580s, Decode+Unpack: 1.080s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1758.0063 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388183-ILSVRC2012_val_00002799.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03388183-ILSVRC2012_val_00002799.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388549-ILSVRC2012_val_00002945.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388549-ILSVRC2012_val_00002945.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 396,432B, BPFP=0.7525 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 453,128B, BPFP=0.8601 +⌛️ [2/4] FRONTEND: Frontend time: 0.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.066s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09849939 187.79915270 + layer.39.0 10.79426799 2217.58041788 + ------------------------------------------------------------------------------------- + TOTAL 5.44638369 1202.68978529 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 849560 +BPFP 0.8063 bits/point +EBPFP 0.8063 equivalent bits/point +MSE 1202.689785 +---------------------- -------------------------------------------------------- +Time: 1.718s Load: 0.071s, Pack+Encode: 0.581s, Decode+Unpack: 1.066s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1202.6898 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388549-ILSVRC2012_val_00002945.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03388549-ILSVRC2012_val_00002945.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03393912-ILSVRC2012_val_00000047.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03393912-ILSVRC2012_val_00000047.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 382,576B, BPFP=0.7262 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 414,320B, BPFP=0.7864 +⌛️ [2/4] FRONTEND: Frontend time: 0.579s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.061s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09729456 236.26330175 + layer.39.0 38.26720800 3162.22643343 + ------------------------------------------------------------------------------------- + TOTAL 19.18225128 1699.24486759 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 796896 +BPFP 0.7563 bits/point +EBPFP 0.7563 equivalent bits/point +MSE 1699.244868 +---------------------- -------------------------------------------------------- +Time: 1.711s Load: 0.071s, Pack+Encode: 0.579s, Decode+Unpack: 1.061s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1699.2449 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03393912-ILSVRC2012_val_00000047.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03393912-ILSVRC2012_val_00000047.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03394916-ILSVRC2012_val_00000957.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03394916-ILSVRC2012_val_00000957.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 371,640B, BPFP=0.7054 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 474,304B, BPFP=0.9003 +⌛️ [2/4] FRONTEND: Frontend time: 0.580s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.078s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10421823 270.00139699 + layer.39.0 9.72561820 1880.83199708 + ------------------------------------------------------------------------------------- + TOTAL 4.91491822 1075.41669704 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 845944 +BPFP 0.8028 bits/point +EBPFP 0.8028 equivalent bits/point +MSE 1075.416697 +---------------------- -------------------------------------------------------- +Time: 1.728s Load: 0.070s, Pack+Encode: 0.580s, Decode+Unpack: 1.078s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1075.4167 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03394916-ILSVRC2012_val_00000957.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03394916-ILSVRC2012_val_00000957.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03404251-ILSVRC2012_val_00000641.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03404251-ILSVRC2012_val_00000641.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.053s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 379,344B, BPFP=0.7200 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 515,328B, BPFP=0.9781 +⌛️ [2/4] FRONTEND: Frontend time: 0.591s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.063s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10764784 197.11090865 + layer.39.0 585.45553936 2913.27065112 + ------------------------------------------------------------------------------------- + TOTAL 292.78159360 1555.19077988 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 894672 +BPFP 0.8491 bits/point +EBPFP 0.8491 equivalent bits/point +MSE 1555.190780 +---------------------- -------------------------------------------------------- +Time: 1.706s Load: 0.053s, Pack+Encode: 0.591s, Decode+Unpack: 1.063s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1555.1908 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03404251-ILSVRC2012_val_00000641.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03404251-ILSVRC2012_val_00000641.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03417042-ILSVRC2012_val_00001144.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03417042-ILSVRC2012_val_00001144.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 391,312B, BPFP=0.7427 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 447,892B, BPFP=0.8501 +⌛️ [2/4] FRONTEND: Frontend time: 0.583s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.065s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10091509 357.23754859 + layer.39.0 202.93364310 2721.89115646 + ------------------------------------------------------------------------------------- + TOTAL 101.51727910 1539.56435253 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 839204 +BPFP 0.7964 bits/point +EBPFP 0.7964 equivalent bits/point +MSE 1539.564353 +---------------------- -------------------------------------------------------- +Time: 1.719s Load: 0.071s, Pack+Encode: 0.583s, Decode+Unpack: 1.065s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1539.5644 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03417042-ILSVRC2012_val_00001144.zst + to output-fixed/dinov3-total/lambda0.01/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03417042-ILSVRC2012_val_00001144.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 0.8200 bits/point +Avg EBPFP 0.8200 equivalent bits/point +Avg MSE 1471.953306 +Avg Time 1.814s +------------------------ ---------------------------- diff --git a/lambda0.02/elic-featurecoding-8bit-individual/cls_in1ka/dtufc_elic-featurecoding_dinov3-total_individual.log b/lambda0.02/elic-featurecoding-8bit-individual/cls_in1ka/dtufc_elic-featurecoding_dinov3-total_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..50976aebedca529b0691e12ca9a49d84e792b530 --- /dev/null +++ b/lambda0.02/elic-featurecoding-8bit-individual/cls_in1ka/dtufc_elic-featurecoding_dinov3-total_individual.log @@ -0,0 +1,9344 @@ +Experiment: dtufc_elic-featurecoding_dinov3-total_individual +Log file: output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/dtufc_elic-featurecoding_dinov3-total_individual.log +DTUFCCodecConfig: + arch: elic-featurecoding + handler: dinov3-total + checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.02_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.02_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 520 +Loaded elic-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.9' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.19' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.29' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.39' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json +Loaded per-key mappings: model=dinov3-total + Keys: ['layer.9', 'layer.19', 'layer.29', 'layer.39'] +---------------- ------------------------------------------------------------------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +Checkpoint codec_weights/elic_hybrid/elic2022-official_lambda0.02_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-DINOv3/imagenet1k-a +Output output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka +---------------- ------------------------------------------------------------------------------------------------------------------- +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01498041-0.006639_koala _ American bullfrog_0.6658246.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01498041-0.006639_koala _ American bullfrog_0.6658246.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 698,220B, BPFP=1.3253 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 623,960B, BPFP=1.1843 +⌛️ [2/4] FRONTEND: Frontend time: 3.027s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.589s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09594801 8.52583667 + layer.39.0 58.94484178 1821.88265306 + ------------------------------------------------------------------------------------- + TOTAL 29.52039490 915.20424487 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1322180 +BPFP 1.2548 bits/point +EBPFP 1.2548 equivalent bits/point +MSE 915.204245 +---------------------- -------------------------------------------------------- +Time: 5.688s Load: 0.072s, Pack+Encode: 3.027s, Decode+Unpack: 2.589s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 915.2042 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01498041-0.006639_koala _ American bullfrog_0.6658246.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n01498041-0.006639_koala _ American bullfrog_0.6658246.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01531178-0.000765_fox squirrel _ ant_0.9486805.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01531178-0.000765_fox squirrel _ ant_0.9486805.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.087s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 678,304B, BPFP=1.2875 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 683,316B, BPFP=1.2970 +⌛️ [2/4] FRONTEND: Frontend time: 2.639s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.538s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09773727 0.79718990 + layer.39.0 17.17825445 2631.72327502 + ------------------------------------------------------------------------------------- + TOTAL 8.63799586 1316.26023246 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1361620 +BPFP 1.2922 bits/point +EBPFP 1.2922 equivalent bits/point +MSE 1316.260232 +---------------------- -------------------------------------------------------- +Time: 5.264s Load: 0.087s, Pack+Encode: 2.639s, Decode+Unpack: 2.538s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1316.2602 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01531178-0.000765_fox squirrel _ ant_0.9486805.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n01531178-0.000765_fox squirrel _ ant_0.9486805.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01534433-0.004573_stingray _ stingray_0.97124094.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01534433-0.004573_stingray _ stingray_0.97124094.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 560,032B, BPFP=1.0630 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 472,592B, BPFP=0.8970 +⌛️ [2/4] FRONTEND: Frontend time: 2.608s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.513s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09515371 8.67624818 + layer.39.0 6.87362484 687.95717930 + ------------------------------------------------------------------------------------- + TOTAL 3.48438928 348.31671374 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1032624 +BPFP 0.9800 bits/point +EBPFP 0.9800 equivalent bits/point +MSE 348.316714 +---------------------- -------------------------------------------------------- +Time: 5.171s Load: 0.051s, Pack+Encode: 2.608s, Decode+Unpack: 2.513s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 348.3167 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01534433-0.004573_stingray _ stingray_0.97124094.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n01534433-0.004573_stingray _ stingray_0.97124094.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01558993-0.000522_bow _ bow_0.9033333.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01558993-0.000522_bow _ bow_0.9033333.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 714,620B, BPFP=1.3564 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 494,800B, BPFP=0.9392 +⌛️ [2/4] FRONTEND: Frontend time: 2.684s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.534s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09874929 20.93376078 + layer.39.0 7.31778236 1274.69412051 + ------------------------------------------------------------------------------------- + TOTAL 3.70826583 647.81394064 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1209420 +BPFP 1.1478 bits/point +EBPFP 1.1478 equivalent bits/point +MSE 647.813941 +---------------------- -------------------------------------------------------- +Time: 5.270s Load: 0.051s, Pack+Encode: 2.684s, Decode+Unpack: 2.534s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 647.8139 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01558993-0.000522_bow _ bow_0.9033333.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n01558993-0.000522_bow _ bow_0.9033333.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01580077-0.004583_African bush elephant _ African bush elephant_0.59939015.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01580077-0.004583_African bush elephant _ African bush elephant_0.59939015.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.056s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 684,200B, BPFP=1.2987 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 586,172B, BPFP=1.1126 +⌛️ [2/4] FRONTEND: Frontend time: 2.627s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.520s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10720986 12.10777587 + layer.39.0 24.46209533 1429.26445578 + ------------------------------------------------------------------------------------- + TOTAL 12.28465260 720.68611582 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1270372 +BPFP 1.2056 bits/point +EBPFP 1.2056 equivalent bits/point +MSE 720.686116 +---------------------- -------------------------------------------------------- +Time: 5.202s Load: 0.056s, Pack+Encode: 2.627s, Decode+Unpack: 2.520s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 720.6861 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01580077-0.004583_African bush elephant _ African bush elephant_0.59939015.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n01580077-0.004583_African bush elephant _ African bush elephant_0.59939015.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01614925-0.049724_white-headed capuchin _ white-headed capuchin_0.9309422.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01614925-0.049724_white-headed capuchin _ white-headed capuchin_0.9309422.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 740,792B, BPFP=1.4061 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 530,136B, BPFP=1.0062 +⌛️ [2/4] FRONTEND: Frontend time: 2.640s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.545s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09739119 20.26430394 + layer.39.0 8.81423010 2010.10398445 + ------------------------------------------------------------------------------------- + TOTAL 4.45581065 1015.18414419 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1270928 +BPFP 1.2062 bits/point +EBPFP 1.2062 equivalent bits/point +MSE 1015.184144 +---------------------- -------------------------------------------------------- +Time: 5.237s Load: 0.052s, Pack+Encode: 2.640s, Decode+Unpack: 2.545s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1015.1841 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01614925-0.049724_white-headed capuchin _ white-headed capuchin_0.9309422.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n01614925-0.049724_white-headed capuchin _ white-headed capuchin_0.9309422.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01631663-0.004606_American bullfrog _ American bullfrog_0.8789855.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01631663-0.004606_American bullfrog _ American bullfrog_0.8789855.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 639,780B, BPFP=1.2144 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 616,060B, BPFP=1.1693 +⌛️ [2/4] FRONTEND: Frontend time: 2.633s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.526s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09716670 1.14413443 + layer.39.0 20.45897868 2014.26530612 + ------------------------------------------------------------------------------------- + TOTAL 10.27807269 1007.70472028 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1255840 +BPFP 1.1918 bits/point +EBPFP 1.1918 equivalent bits/point +MSE 1007.704720 +---------------------- -------------------------------------------------------- +Time: 5.210s Load: 0.051s, Pack+Encode: 2.633s, Decode+Unpack: 2.526s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1007.7047 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01631663-0.004606_American bullfrog _ American bullfrog_0.8789855.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n01631663-0.004606_American bullfrog _ American bullfrog_0.8789855.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01669191-0.029754_sandal _ sandal_0.38198605.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01669191-0.029754_sandal _ sandal_0.38198605.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 818,832B, BPFP=1.5542 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 520,816B, BPFP=0.9886 +⌛️ [2/4] FRONTEND: Frontend time: 2.648s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.550s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10877632 8.74124795 + layer.39.0 13.16500205 1623.34147230 + ------------------------------------------------------------------------------------- + TOTAL 6.63688918 816.04136013 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1339648 +BPFP 1.2714 bits/point +EBPFP 1.2714 equivalent bits/point +MSE 816.041360 +---------------------- -------------------------------------------------------- +Time: 5.250s Load: 0.052s, Pack+Encode: 2.648s, Decode+Unpack: 2.550s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 816.0414 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01669191-0.029754_sandal _ sandal_0.38198605.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n01669191-0.029754_sandal _ sandal_0.38198605.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770081-0.000571_syringe _ syringe_0.7369336.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770081-0.000571_syringe _ syringe_0.7369336.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.089s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 587,172B, BPFP=1.1145 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 614,884B, BPFP=1.1671 +⌛️ [2/4] FRONTEND: Frontend time: 2.656s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.534s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09508557 8.43738422 + layer.39.0 60.03878538 1811.24987852 + ------------------------------------------------------------------------------------- + TOTAL 30.06693547 909.84363137 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1202056 +BPFP 1.1408 bits/point +EBPFP 1.1408 equivalent bits/point +MSE 909.843631 +---------------------- -------------------------------------------------------- +Time: 5.278s Load: 0.089s, Pack+Encode: 2.656s, Decode+Unpack: 2.534s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 909.8436 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770081-0.000571_syringe _ syringe_0.7369336.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n01770081-0.000571_syringe _ syringe_0.7369336.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770393-0.000908_harvestman _ harvestman_0.8782497.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770393-0.000908_harvestman _ harvestman_0.8782497.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 680,252B, BPFP=1.2912 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 690,608B, BPFP=1.3108 +⌛️ [2/4] FRONTEND: Frontend time: 2.644s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.542s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11350316 0.86660366 + layer.39.0 19.73148992 2447.07312925 + ------------------------------------------------------------------------------------- + TOTAL 9.92249654 1223.96986646 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1370860 +BPFP 1.3010 bits/point +EBPFP 1.3010 equivalent bits/point +MSE 1223.969866 +---------------------- -------------------------------------------------------- +Time: 5.256s Load: 0.071s, Pack+Encode: 2.644s, Decode+Unpack: 2.542s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1223.9699 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770393-0.000908_harvestman _ harvestman_0.8782497.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n01770393-0.000908_harvestman _ harvestman_0.8782497.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01784675-0.027853_syringe _ syringe_0.9584382.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01784675-0.027853_syringe _ syringe_0.9584382.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 799,412B, BPFP=1.5173 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 479,092B, BPFP=0.9094 +⌛️ [2/4] FRONTEND: Frontend time: 2.648s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.532s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11002613 8.49998387 + layer.39.0 26.08665877 1880.83722060 + ------------------------------------------------------------------------------------- + TOTAL 13.09834245 944.66860223 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1278504 +BPFP 1.2134 bits/point +EBPFP 1.2134 equivalent bits/point +MSE 944.668602 +---------------------- -------------------------------------------------------- +Time: 5.240s Load: 0.059s, Pack+Encode: 2.648s, Decode+Unpack: 2.532s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 944.6686 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01784675-0.027853_syringe _ syringe_0.9584382.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n01784675-0.027853_syringe _ syringe_0.9584382.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01819313-0.053742_koala _ koala_0.98647016.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01819313-0.053742_koala _ koala_0.98647016.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.081s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 773,960B, BPFP=1.4690 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 649,584B, BPFP=1.2330 +⌛️ [2/4] FRONTEND: Frontend time: 2.644s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.547s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14565475 13.18707483 + layer.39.0 25.01023445 2611.46671526 + ------------------------------------------------------------------------------------- + TOTAL 12.57794460 1312.32689504 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1423544 +BPFP 1.3510 bits/point +EBPFP 1.3510 equivalent bits/point +MSE 1312.326895 +---------------------- -------------------------------------------------------- +Time: 5.272s Load: 0.081s, Pack+Encode: 2.644s, Decode+Unpack: 2.547s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1312.3269 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01819313-0.053742_koala _ koala_0.98647016.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n01819313-0.053742_koala _ koala_0.98647016.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01820546-0.012522_toucan _ toucan_0.63882655.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01820546-0.012522_toucan _ toucan_0.63882655.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 662,372B, BPFP=1.2572 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 656,900B, BPFP=1.2468 +⌛️ [2/4] FRONTEND: Frontend time: 2.703s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.523s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09696376 8.39071136 + layer.39.0 16.65489097 2950.41933916 + ------------------------------------------------------------------------------------- + TOTAL 8.37592737 1479.40502526 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1319272 +BPFP 1.2520 bits/point +EBPFP 1.2520 equivalent bits/point +MSE 1479.405025 +---------------------- -------------------------------------------------------- +Time: 5.288s Load: 0.062s, Pack+Encode: 2.703s, Decode+Unpack: 2.523s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1479.4050 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01820546-0.012522_toucan _ toucan_0.63882655.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n01820546-0.012522_toucan _ toucan_0.63882655.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01833805-0.013248_toucan _ lorikeet_0.77773976.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01833805-0.013248_toucan _ lorikeet_0.77773976.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.081s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 635,368B, BPFP=1.2060 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 675,772B, BPFP=1.2827 +⌛️ [2/4] FRONTEND: Frontend time: 2.653s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.536s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09866240 0.80669425 + layer.39.0 7.67772963 2713.73639456 + ------------------------------------------------------------------------------------- + TOTAL 3.88819601 1357.27154440 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1311140 +BPFP 1.2443 bits/point +EBPFP 1.2443 equivalent bits/point +MSE 1357.271544 +---------------------- -------------------------------------------------------- +Time: 5.270s Load: 0.081s, Pack+Encode: 2.653s, Decode+Unpack: 2.536s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1357.2715 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01833805-0.013248_toucan _ lorikeet_0.77773976.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n01833805-0.013248_toucan _ lorikeet_0.77773976.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01843383-0.013605_white-headed capuchin _ white-headed capuchin_0.9984768.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01843383-0.013605_white-headed capuchin _ white-headed capuchin_0.9984768.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 748,956B, BPFP=1.4216 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 673,416B, BPFP=1.2782 +⌛️ [2/4] FRONTEND: Frontend time: 2.643s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.559s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11910487 14.35755189 + layer.39.0 9.20068692 2890.89285714 + ------------------------------------------------------------------------------------- + TOTAL 4.65989589 1452.62520452 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1422372 +BPFP 1.3499 bits/point +EBPFP 1.3499 equivalent bits/point +MSE 1452.625205 +---------------------- -------------------------------------------------------- +Time: 5.281s Load: 0.080s, Pack+Encode: 2.643s, Decode+Unpack: 2.559s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1452.6252 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01843383-0.013605_white-headed capuchin _ white-headed capuchin_0.9984768.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n01843383-0.013605_white-headed capuchin _ white-headed capuchin_0.9984768.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01924916-0.000644_jay _ jay_0.82223135.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01924916-0.000644_jay _ jay_0.82223135.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.081s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 763,008B, BPFP=1.4483 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 542,524B, BPFP=1.0298 +⌛️ [2/4] FRONTEND: Frontend time: 2.648s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.523s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11488669 8.79927987 + layer.39.0 141.08750911 1235.39261419 + ------------------------------------------------------------------------------------- + TOTAL 70.60119790 622.09594703 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1305532 +BPFP 1.2390 bits/point +EBPFP 1.2390 equivalent bits/point +MSE 622.095947 +---------------------- -------------------------------------------------------- +Time: 5.252s Load: 0.081s, Pack+Encode: 2.648s, Decode+Unpack: 2.523s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 622.0959 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01924916-0.000644_jay _ jay_0.82223135.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n01924916-0.000644_jay _ jay_0.82223135.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01944390-0.002567_American robin _ American robin_0.5629079.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01944390-0.002567_American robin _ American robin_0.5629079.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 693,024B, BPFP=1.3154 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 655,012B, BPFP=1.2433 +⌛️ [2/4] FRONTEND: Frontend time: 2.642s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.543s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10732387 13.66491948 + layer.39.0 16.74672581 2957.52623907 + ------------------------------------------------------------------------------------- + TOTAL 8.42702484 1485.59557928 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1348036 +BPFP 1.2793 bits/point +EBPFP 1.2793 equivalent bits/point +MSE 1485.595579 +---------------------- -------------------------------------------------------- +Time: 5.256s Load: 0.071s, Pack+Encode: 2.642s, Decode+Unpack: 2.543s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1485.5956 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01944390-0.002567_American robin _ American robin_0.5629079.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n01944390-0.002567_American robin _ American robin_0.5629079.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01985128-0.001579_centipede _ centipede_0.85936093.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01985128-0.001579_centipede _ centipede_0.85936093.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 694,648B, BPFP=1.3185 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 544,648B, BPFP=1.0338 +⌛️ [2/4] FRONTEND: Frontend time: 2.648s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.517s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09645609 12.55601900 + layer.39.0 23.47999613 1680.92602041 + ------------------------------------------------------------------------------------- + TOTAL 11.78822611 846.74101971 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1239296 +BPFP 1.1761 bits/point +EBPFP 1.1761 equivalent bits/point +MSE 846.741020 +---------------------- -------------------------------------------------------- +Time: 5.244s Load: 0.080s, Pack+Encode: 2.648s, Decode+Unpack: 2.517s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 846.7410 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01985128-0.001579_centipede _ centipede_0.85936093.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n01985128-0.001579_centipede _ centipede_0.85936093.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02037110-0.003503_snowmobile _ snowmobile_0.5200631.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02037110-0.003503_snowmobile _ snowmobile_0.5200631.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 483,144B, BPFP=0.9170 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 483,036B, BPFP=0.9168 +⌛️ [2/4] FRONTEND: Frontend time: 2.651s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.515s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09471867 12.55851403 + layer.39.0 17.04498261 1141.53146259 + ------------------------------------------------------------------------------------- + TOTAL 8.56985064 577.04498831 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 966180 +BPFP 0.9169 bits/point +EBPFP 0.9169 equivalent bits/point +MSE 577.044988 +---------------------- -------------------------------------------------------- +Time: 5.247s Load: 0.080s, Pack+Encode: 2.651s, Decode+Unpack: 2.515s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 577.0450 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02037110-0.003503_snowmobile _ snowmobile_0.5200631.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n02037110-0.003503_snowmobile _ snowmobile_0.5200631.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02123394-0.015363_marmot _ marmot_0.82052565.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02123394-0.015363_marmot _ marmot_0.82052565.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 618,820B, BPFP=1.1746 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 617,616B, BPFP=1.1723 +⌛️ [2/4] FRONTEND: Frontend time: 2.662s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.538s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10209646 1.17675758 + layer.39.0 11.38238543 2358.58843537 + ------------------------------------------------------------------------------------- + TOTAL 5.74224095 1179.88259647 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1236436 +BPFP 1.1734 bits/point +EBPFP 1.1734 equivalent bits/point +MSE 1179.882596 +---------------------- -------------------------------------------------------- +Time: 5.272s Load: 0.071s, Pack+Encode: 2.662s, Decode+Unpack: 2.538s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1179.8826 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02123394-0.015363_marmot _ marmot_0.82052565.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n02123394-0.015363_marmot _ marmot_0.82052565.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02165456-0.000157_corn _ corn_0.9868978.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02165456-0.000157_corn _ corn_0.9868978.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 709,824B, BPFP=1.3473 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 660,248B, BPFP=1.2532 +⌛️ [2/4] FRONTEND: Frontend time: 2.668s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.534s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10346756 1.19075058 + layer.39.0 776.17699223 1816.03620019 + ------------------------------------------------------------------------------------- + TOTAL 388.14022989 908.61347539 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1370072 +BPFP 1.3003 bits/point +EBPFP 1.3003 equivalent bits/point +MSE 908.613475 +---------------------- -------------------------------------------------------- +Time: 5.271s Load: 0.069s, Pack+Encode: 2.668s, Decode+Unpack: 2.534s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 908.6135 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02165456-0.000157_corn _ corn_0.9868978.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n02165456-0.000157_corn _ corn_0.9868978.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02219486-0.000060_cliff _ cliff_0.99684334.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02219486-0.000060_cliff _ cliff_0.99684334.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 648,864B, BPFP=1.2316 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 468,840B, BPFP=0.8899 +⌛️ [2/4] FRONTEND: Frontend time: 2.626s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.532s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09584527 0.75781873 + layer.39.0 31.94620460 1111.43027211 + ------------------------------------------------------------------------------------- + TOTAL 16.02102494 556.09404542 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1117704 +BPFP 1.0607 bits/point +EBPFP 1.0607 equivalent bits/point +MSE 556.094045 +---------------------- -------------------------------------------------------- +Time: 5.229s Load: 0.071s, Pack+Encode: 2.626s, Decode+Unpack: 2.532s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 556.0940 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02219486-0.000060_cliff _ cliff_0.99684334.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n02219486-0.000060_cliff _ cliff_0.99684334.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02226429-0.000085_stick insect _ stick insect_0.99900275.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02226429-0.000085_stick insect _ stick insect_0.99900275.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 655,952B, BPFP=1.2450 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 646,112B, BPFP=1.2264 +⌛️ [2/4] FRONTEND: Frontend time: 2.627s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.534s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09547379 12.40725219 + layer.39.0 19.16722850 2811.85325559 + ------------------------------------------------------------------------------------- + TOTAL 9.63135114 1412.13025389 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1302064 +BPFP 1.2357 bits/point +EBPFP 1.2357 equivalent bits/point +MSE 1412.130254 +---------------------- -------------------------------------------------------- +Time: 5.212s Load: 0.051s, Pack+Encode: 2.627s, Decode+Unpack: 2.534s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1412.1303 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02226429-0.000085_stick insect _ stick insect_0.99900275.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n02226429-0.000085_stick insect _ stick insect_0.99900275.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02231487-0.000080_manhole cover _ manhole cover_0.7554716.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02231487-0.000080_manhole cover _ manhole cover_0.7554716.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 630,240B, BPFP=1.1962 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 675,400B, BPFP=1.2820 +⌛️ [2/4] FRONTEND: Frontend time: 2.632s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.538s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09512618 12.79037218 + layer.39.0 210.79875790 2215.61151603 + ------------------------------------------------------------------------------------- + TOTAL 105.44694204 1114.20094411 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1305640 +BPFP 1.2391 bits/point +EBPFP 1.2391 equivalent bits/point +MSE 1114.200944 +---------------------- -------------------------------------------------------- +Time: 5.220s Load: 0.050s, Pack+Encode: 2.632s, Decode+Unpack: 2.538s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1114.2009 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02231487-0.000080_manhole cover _ manhole cover_0.7554716.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n02231487-0.000080_manhole cover _ manhole cover_0.7554716.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02233338-0.002901_manhole cover _ manhole cover_0.69458175.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02233338-0.002901_manhole cover _ manhole cover_0.69458175.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 593,884B, BPFP=1.1272 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 581,584B, BPFP=1.1039 +⌛️ [2/4] FRONTEND: Frontend time: 2.630s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.520s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09539769 12.29759153 + layer.39.0 58.97704841 1320.38374636 + ------------------------------------------------------------------------------------- + TOTAL 29.53622305 666.34066894 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1175468 +BPFP 1.1156 bits/point +EBPFP 1.1156 equivalent bits/point +MSE 666.340669 +---------------------- -------------------------------------------------------- +Time: 5.220s Load: 0.070s, Pack+Encode: 2.630s, Decode+Unpack: 2.520s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 666.3407 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02233338-0.002901_manhole cover _ manhole cover_0.69458175.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n02233338-0.002901_manhole cover _ manhole cover_0.69458175.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02236044-0.000522_sundial _ sundial_0.96381366.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02236044-0.000522_sundial _ sundial_0.96381366.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.079s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 700,944B, BPFP=1.3304 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 705,112B, BPFP=1.3384 +⌛️ [2/4] FRONTEND: Frontend time: 2.659s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.546s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09795647 0.79816623 + layer.39.0 53.12385356 2623.16812439 + ------------------------------------------------------------------------------------- + TOTAL 26.61090502 1311.98314531 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1406056 +BPFP 1.3344 bits/point +EBPFP 1.3344 equivalent bits/point +MSE 1311.983145 +---------------------- -------------------------------------------------------- +Time: 5.283s Load: 0.079s, Pack+Encode: 2.659s, Decode+Unpack: 2.546s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1311.9831 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02236044-0.000522_sundial _ sundial_0.96381366.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n02236044-0.000522_sundial _ sundial_0.96381366.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02259212-0.000032_chain _ chain_0.6590295.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02259212-0.000032_chain _ chain_0.6590295.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 637,256B, BPFP=1.2096 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 675,096B, BPFP=1.2814 +⌛️ [2/4] FRONTEND: Frontend time: 2.634s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.529s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09523673 0.75823939 + layer.39.0 80.66082058 2261.07458698 + ------------------------------------------------------------------------------------- + TOTAL 40.37802865 1130.91641318 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1312352 +BPFP 1.2455 bits/point +EBPFP 1.2455 equivalent bits/point +MSE 1130.916413 +---------------------- -------------------------------------------------------- +Time: 5.215s Load: 0.052s, Pack+Encode: 2.634s, Decode+Unpack: 2.529s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1130.9164 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02259212-0.000032_chain _ chain_0.6590295.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n02259212-0.000032_chain _ chain_0.6590295.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02279972-0.000576_apron _ apron_0.7661352.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02279972-0.000576_apron _ apron_0.7661352.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 685,580B, BPFP=1.3013 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 569,640B, BPFP=1.0812 +⌛️ [2/4] FRONTEND: Frontend time: 2.630s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.515s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12772729 1.22724972 + layer.39.0 1038.59135083 1894.73821672 + ------------------------------------------------------------------------------------- + TOTAL 519.35953906 947.98273322 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1255220 +BPFP 1.1913 bits/point +EBPFP 1.1913 equivalent bits/point +MSE 947.982733 +---------------------- -------------------------------------------------------- +Time: 5.195s Load: 0.050s, Pack+Encode: 2.630s, Decode+Unpack: 2.515s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 947.9827 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02279972-0.000576_apron _ apron_0.7661352.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n02279972-0.000576_apron _ apron_0.7661352.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02280649-0.001599_custard apple _ leafhopper_0.9128452.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02280649-0.001599_custard apple _ leafhopper_0.9128452.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 628,032B, BPFP=1.1921 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 640,936B, BPFP=1.2165 +⌛️ [2/4] FRONTEND: Frontend time: 2.631s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.531s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09488542 8.59718078 + layer.39.0 1031.59973275 2591.82118562 + ------------------------------------------------------------------------------------- + TOTAL 515.84730909 1300.20918320 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1268968 +BPFP 1.2043 bits/point +EBPFP 1.2043 equivalent bits/point +MSE 1300.209183 +---------------------- -------------------------------------------------------- +Time: 5.222s Load: 0.060s, Pack+Encode: 2.631s, Decode+Unpack: 2.531s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1300.2092 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02280649-0.001599_custard apple _ leafhopper_0.9128452.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n02280649-0.001599_custard apple _ leafhopper_0.9128452.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02317335-0.033681_flatworm _ flatworm_0.6070924.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02317335-0.033681_flatworm _ flatworm_0.6070924.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.055s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 620,616B, BPFP=1.1780 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 514,252B, BPFP=0.9761 +⌛️ [2/4] FRONTEND: Frontend time: 2.639s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.523s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09575805 0.78361732 + layer.39.0 62.35741238 1328.80041302 + ------------------------------------------------------------------------------------- + TOTAL 31.22658522 664.79201517 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1134868 +BPFP 1.0770 bits/point +EBPFP 1.0770 equivalent bits/point +MSE 664.792015 +---------------------- -------------------------------------------------------- +Time: 5.218s Load: 0.055s, Pack+Encode: 2.639s, Decode+Unpack: 2.523s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 664.7920 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02317335-0.033681_flatworm _ flatworm_0.6070924.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n02317335-0.033681_flatworm _ flatworm_0.6070924.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02325366-0.000350_flatworm _ flatworm_0.87634724.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02325366-0.000350_flatworm _ flatworm_0.87634724.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 571,608B, BPFP=1.0850 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 482,284B, BPFP=0.9154 +⌛️ [2/4] FRONTEND: Frontend time: 2.608s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.528s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09712043 12.70056069 + layer.39.0 30.59439155 877.65676628 + ------------------------------------------------------------------------------------- + TOTAL 15.34575599 445.17866349 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1053892 +BPFP 1.0002 bits/point +EBPFP 1.0002 equivalent bits/point +MSE 445.178663 +---------------------- -------------------------------------------------------- +Time: 5.186s Load: 0.051s, Pack+Encode: 2.608s, Decode+Unpack: 2.528s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 445.1787 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02325366-0.000350_flatworm _ flatworm_0.87634724.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n02325366-0.000350_flatworm _ flatworm_0.87634724.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02346627-0.011107_fountain _ skunk_0.28641737.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02346627-0.011107_fountain _ skunk_0.28641737.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 521,880B, BPFP=0.9906 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 395,616B, BPFP=0.7509 +⌛️ [2/4] FRONTEND: Frontend time: 2.608s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.505s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09705289 0.79431710 + layer.39.0 9.52721088 612.15506560 + ------------------------------------------------------------------------------------- + TOTAL 4.81213189 306.47469135 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 917496 +BPFP 0.8707 bits/point +EBPFP 0.8707 equivalent bits/point +MSE 306.474691 +---------------------- -------------------------------------------------------- +Time: 5.183s Load: 0.070s, Pack+Encode: 2.608s, Decode+Unpack: 2.505s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 306.4747 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02346627-0.011107_fountain _ skunk_0.28641737.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n02346627-0.011107_fountain _ skunk_0.28641737.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02445715-0.036432_shovel _ tarantula_0.26785344.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02445715-0.036432_shovel _ tarantula_0.26785344.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 724,712B, BPFP=1.3756 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 446,704B, BPFP=0.8479 +⌛️ [2/4] FRONTEND: Frontend time: 2.634s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.518s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09708806 12.02727542 + layer.39.0 8.00606437 740.10106900 + ------------------------------------------------------------------------------------- + TOTAL 4.05157622 376.06417221 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1171416 +BPFP 1.1117 bits/point +EBPFP 1.1117 equivalent bits/point +MSE 376.064172 +---------------------- -------------------------------------------------------- +Time: 5.212s Load: 0.060s, Pack+Encode: 2.634s, Decode+Unpack: 2.518s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 376.0642 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02445715-0.036432_shovel _ tarantula_0.26785344.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n02445715-0.036432_shovel _ tarantula_0.26785344.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02454379-0.082010_koala _ koala_0.7052893.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02454379-0.082010_koala _ koala_0.7052893.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 826,728B, BPFP=1.5692 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 554,788B, BPFP=1.0530 +⌛️ [2/4] FRONTEND: Frontend time: 2.648s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.537s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14585212 14.05235666 + layer.39.0 44.19989826 1443.02660350 + ------------------------------------------------------------------------------------- + TOTAL 22.17287519 728.53948008 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1381516 +BPFP 1.3111 bits/point +EBPFP 1.3111 equivalent bits/point +MSE 728.539480 +---------------------- -------------------------------------------------------- +Time: 5.236s Load: 0.051s, Pack+Encode: 2.648s, Decode+Unpack: 2.537s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 728.5395 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02454379-0.082010_koala _ koala_0.7052893.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n02454379-0.082010_koala _ koala_0.7052893.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02782093-0.007542_American alligator _ American alligator_0.49872985.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02782093-0.007542_American alligator _ American alligator_0.49872985.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 567,576B, BPFP=1.0773 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 561,908B, BPFP=1.0665 +⌛️ [2/4] FRONTEND: Frontend time: 2.634s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.510s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09848133 0.78187536 + layer.39.0 9.18780844 1926.95225948 + ------------------------------------------------------------------------------------- + TOTAL 4.64314488 963.86706742 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1129484 +BPFP 1.0719 bits/point +EBPFP 1.0719 equivalent bits/point +MSE 963.867067 +---------------------- -------------------------------------------------------- +Time: 5.194s Load: 0.050s, Pack+Encode: 2.634s, Decode+Unpack: 2.510s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 963.8671 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02782093-0.007542_American alligator _ American alligator_0.49872985.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n02782093-0.007542_American alligator _ American alligator_0.49872985.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02787622-0.004599_marimba _ accordion_0.25991488.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02787622-0.004599_marimba _ accordion_0.25991488.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 712,624B, BPFP=1.3526 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 717,132B, BPFP=1.3612 +⌛️ [2/4] FRONTEND: Frontend time: 2.633s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.535s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12856446 0.84623044 + layer.39.0 1004.59450923 2383.84499514 + ------------------------------------------------------------------------------------- + TOTAL 502.36153685 1192.34561279 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1429756 +BPFP 1.3569 bits/point +EBPFP 1.3569 equivalent bits/point +MSE 1192.345613 +---------------------- -------------------------------------------------------- +Time: 5.219s Load: 0.051s, Pack+Encode: 2.633s, Decode+Unpack: 2.535s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1192.3456 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02787622-0.004599_marimba _ accordion_0.25991488.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n02787622-0.004599_marimba _ accordion_0.25991488.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02793495-0.000130_flatworm _ stingray_0.5528234.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02793495-0.000130_flatworm _ stingray_0.5528234.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 609,792B, BPFP=1.1574 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 397,104B, BPFP=0.7537 +⌛️ [2/4] FRONTEND: Frontend time: 2.606s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.512s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09706621 8.72783421 + layer.39.0 8.05872662 525.51068999 + ------------------------------------------------------------------------------------- + TOTAL 4.07789641 267.11926210 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1006896 +BPFP 0.9556 bits/point +EBPFP 0.9556 equivalent bits/point +MSE 267.119262 +---------------------- -------------------------------------------------------- +Time: 5.170s Load: 0.051s, Pack+Encode: 2.606s, Decode+Unpack: 2.512s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 267.1193 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02793495-0.000130_flatworm _ stingray_0.5528234.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n02793495-0.000130_flatworm _ stingray_0.5528234.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02797295-0.002775_hair dryer _ box turtle_0.2407761.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02797295-0.002775_hair dryer _ box turtle_0.2407761.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 715,392B, BPFP=1.3579 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 742,640B, BPFP=1.4096 +⌛️ [2/4] FRONTEND: Frontend time: 2.646s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.536s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11161610 1.21646542 + layer.39.0 373.09438776 2521.47619048 + ------------------------------------------------------------------------------------- + TOTAL 186.60300193 1261.34632795 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1458032 +BPFP 1.3837 bits/point +EBPFP 1.3837 equivalent bits/point +MSE 1261.346328 +---------------------- -------------------------------------------------------- +Time: 5.233s Load: 0.051s, Pack+Encode: 2.646s, Decode+Unpack: 2.536s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1261.3463 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02797295-0.002775_hair dryer _ box turtle_0.2407761.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n02797295-0.002775_hair dryer _ box turtle_0.2407761.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02814860-0.006340_fountain _ fountain_0.7891514.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02814860-0.006340_fountain _ fountain_0.7891514.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 455,176B, BPFP=0.8640 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 527,396B, BPFP=1.0010 +⌛️ [2/4] FRONTEND: Frontend time: 2.597s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.499s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.04615183 8.88647200 + layer.39.0 7.48662090 1255.19545675 + ------------------------------------------------------------------------------------- + TOTAL 7.76638637 632.04096438 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 982572 +BPFP 0.9325 bits/point +EBPFP 0.9325 equivalent bits/point +MSE 632.040964 +---------------------- -------------------------------------------------------- +Time: 5.145s Load: 0.050s, Pack+Encode: 2.597s, Decode+Unpack: 2.499s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 632.0410 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02814860-0.006340_fountain _ fountain_0.7891514.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n02814860-0.006340_fountain _ fountain_0.7891514.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02879718-0.003578_maraca _ maraca_0.6809677.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02879718-0.003578_maraca _ maraca_0.6809677.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 645,520B, BPFP=1.2252 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 762,180B, BPFP=1.4467 +⌛️ [2/4] FRONTEND: Frontend time: 2.628s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 3.736s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10989876 12.16508651 + layer.39.0 33.03751367 2282.66836735 + ------------------------------------------------------------------------------------- + TOTAL 16.57370621 1147.41672693 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1407700 +BPFP 1.3360 bits/point +EBPFP 1.3360 equivalent bits/point +MSE 1147.416727 +---------------------- -------------------------------------------------------- +Time: 6.425s Load: 0.061s, Pack+Encode: 2.628s, Decode+Unpack: 3.736s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1147.4167 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02879718-0.003578_maraca _ maraca_0.6809677.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n02879718-0.003578_maraca _ maraca_0.6809677.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02883205-0.000262_syringe _ syringe_0.7098205.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02883205-0.000262_syringe _ syringe_0.7098205.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 571,440B, BPFP=1.0846 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 587,744B, BPFP=1.1156 +⌛️ [2/4] FRONTEND: Frontend time: 2.609s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.512s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09610580 12.06869439 + layer.39.0 8.14318931 1624.09572400 + ------------------------------------------------------------------------------------- + TOTAL 4.11964755 818.08220920 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1159184 +BPFP 1.1001 bits/point +EBPFP 1.1001 equivalent bits/point +MSE 818.082209 +---------------------- -------------------------------------------------------- +Time: 5.192s Load: 0.070s, Pack+Encode: 2.609s, Decode+Unpack: 2.512s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 818.0822 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02883205-0.000262_syringe _ syringe_0.7098205.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n02883205-0.000262_syringe _ syringe_0.7098205.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02906734-0.000163_stethoscope _ stethoscope_0.9290994.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02906734-0.000163_stethoscope _ stethoscope_0.9290994.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.049s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 662,384B, BPFP=1.2573 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 662,976B, BPFP=1.2584 +⌛️ [2/4] FRONTEND: Frontend time: 2.652s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.533s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12024398 11.93206864 + layer.39.0 47.23105336 2649.38848397 + ------------------------------------------------------------------------------------- + TOTAL 23.67564867 1330.66027630 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1325360 +BPFP 1.2578 bits/point +EBPFP 1.2578 equivalent bits/point +MSE 1330.660276 +---------------------- -------------------------------------------------------- +Time: 5.234s Load: 0.049s, Pack+Encode: 2.652s, Decode+Unpack: 2.533s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1330.6603 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02906734-0.000163_stethoscope _ stethoscope_0.9290994.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n02906734-0.000163_stethoscope _ stethoscope_0.9290994.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02948072-0.002303_digital clock _ digital clock_0.928374.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02948072-0.002303_digital clock _ digital clock_0.928374.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.056s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 622,088B, BPFP=1.1808 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 661,952B, BPFP=1.2564 +⌛️ [2/4] FRONTEND: Frontend time: 2.645s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.549s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09670976 0.79651140 + layer.39.0 81.62974520 1951.48821672 + ------------------------------------------------------------------------------------- + TOTAL 40.86322748 976.14236406 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1284040 +BPFP 1.2186 bits/point +EBPFP 1.2186 equivalent bits/point +MSE 976.142364 +---------------------- -------------------------------------------------------- +Time: 5.250s Load: 0.056s, Pack+Encode: 2.645s, Decode+Unpack: 2.549s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 976.1424 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02948072-0.002303_digital clock _ digital clock_0.928374.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n02948072-0.002303_digital clock _ digital clock_0.928374.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02999410-0.000148_chest _ chest_0.9948565.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02999410-0.000148_chest _ chest_0.9948565.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 613,564B, BPFP=1.1646 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 532,572B, BPFP=1.0109 +⌛️ [2/4] FRONTEND: Frontend time: 2.620s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.537s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10256943 1.14545372 + layer.39.0 13.72598738 837.04288144 + ------------------------------------------------------------------------------------- + TOTAL 6.91427841 419.09416758 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1146136 +BPFP 1.0877 bits/point +EBPFP 1.0877 equivalent bits/point +MSE 419.094168 +---------------------- -------------------------------------------------------- +Time: 5.228s Load: 0.070s, Pack+Encode: 2.620s, Decode+Unpack: 2.537s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 419.0942 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02999410-0.000148_chest _ chest_0.9948565.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n02999410-0.000148_chest _ chest_0.9948565.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03026506-0.001828_basketball _ basketball_0.6904969.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03026506-0.001828_basketball _ basketball_0.6904969.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 615,444B, BPFP=1.1682 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 633,132B, BPFP=1.2017 +⌛️ [2/4] FRONTEND: Frontend time: 2.627s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.537s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09484169 12.41979470 + layer.39.0 87.31533194 2108.06681244 + ------------------------------------------------------------------------------------- + TOTAL 43.70508681 1060.24330357 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1248576 +BPFP 1.1849 bits/point +EBPFP 1.1849 equivalent bits/point +MSE 1060.243304 +---------------------- -------------------------------------------------------- +Time: 5.213s Load: 0.050s, Pack+Encode: 2.627s, Decode+Unpack: 2.537s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1060.2433 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03026506-0.001828_basketball _ basketball_0.6904969.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n03026506-0.001828_basketball _ basketball_0.6904969.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03124043-0.001154_bow tie _ bow tie_0.9622156.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03124043-0.001154_bow tie _ bow tie_0.9622156.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 607,640B, BPFP=1.1533 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 609,052B, BPFP=1.1560 +⌛️ [2/4] FRONTEND: Frontend time: 2.640s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.519s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09893820 8.58147132 + layer.39.0 13.24554141 1925.99380466 + ------------------------------------------------------------------------------------- + TOTAL 6.67223981 967.28763799 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1216692 +BPFP 1.1547 bits/point +EBPFP 1.1547 equivalent bits/point +MSE 967.287638 +---------------------- -------------------------------------------------------- +Time: 5.211s Load: 0.052s, Pack+Encode: 2.640s, Decode+Unpack: 2.519s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 967.2876 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03124043-0.001154_bow tie _ bow tie_0.9622156.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n03124043-0.001154_bow tie _ bow tie_0.9622156.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03196217-0.015958_soap dispenser _ envelope_0.80274254.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03196217-0.015958_soap dispenser _ envelope_0.80274254.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 427,064B, BPFP=0.8106 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 573,492B, BPFP=1.0885 +⌛️ [2/4] FRONTEND: Frontend time: 2.600s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.524s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10340443 8.50632156 + layer.39.0 8.70910111 1977.77174441 + ------------------------------------------------------------------------------------- + TOTAL 4.40625277 993.13903298 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1000556 +BPFP 0.9496 bits/point +EBPFP 0.9496 equivalent bits/point +MSE 993.139033 +---------------------- -------------------------------------------------------- +Time: 5.185s Load: 0.061s, Pack+Encode: 2.600s, Decode+Unpack: 2.524s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 993.1390 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03196217-0.015958_soap dispenser _ envelope_0.80274254.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n03196217-0.015958_soap dispenser _ envelope_0.80274254.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03223299-0.001528_hot dog _ hot dog_0.9147216.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03223299-0.001528_hot dog _ hot dog_0.9147216.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 664,876B, BPFP=1.2620 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 598,788B, BPFP=1.1365 +⌛️ [2/4] FRONTEND: Frontend time: 2.652s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.521s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10130972 24.97224626 + layer.39.0 352.09596696 1866.27137998 + ------------------------------------------------------------------------------------- + TOTAL 176.09863834 945.62181312 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1263664 +BPFP 1.1993 bits/point +EBPFP 1.1993 equivalent bits/point +MSE 945.621813 +---------------------- -------------------------------------------------------- +Time: 5.224s Load: 0.051s, Pack+Encode: 2.652s, Decode+Unpack: 2.521s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 945.6218 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03223299-0.001528_hot dog _ hot dog_0.9147216.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n03223299-0.001528_hot dog _ hot dog_0.9147216.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03255030-0.005469_bubble _ bubble_0.9381716.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03255030-0.005469_bubble _ bubble_0.9381716.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 629,132B, BPFP=1.1941 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 752,708B, BPFP=1.4287 +⌛️ [2/4] FRONTEND: Frontend time: 2.641s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.546s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09675161 0.78850998 + layer.39.0 42.23478499 2443.17031098 + ------------------------------------------------------------------------------------- + TOTAL 21.16576830 1221.97941048 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1381840 +BPFP 1.3114 bits/point +EBPFP 1.3114 equivalent bits/point +MSE 1221.979410 +---------------------- -------------------------------------------------------- +Time: 5.247s Load: 0.060s, Pack+Encode: 2.641s, Decode+Unpack: 2.546s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1221.9794 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03255030-0.005469_bubble _ bubble_0.9381716.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n03255030-0.005469_bubble _ bubble_0.9381716.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03325584-0.000773_candle _ candle_0.810919.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03325584-0.000773_candle _ candle_0.810919.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 693,440B, BPFP=1.3162 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 720,024B, BPFP=1.3667 +⌛️ [2/4] FRONTEND: Frontend time: 2.654s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.526s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10394677 0.81573552 + layer.39.0 140.58187561 2687.88945578 + ------------------------------------------------------------------------------------- + TOTAL 70.34291119 1344.35259565 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1413464 +BPFP 1.3414 bits/point +EBPFP 1.3414 equivalent bits/point +MSE 1344.352596 +---------------------- -------------------------------------------------------- +Time: 5.230s Load: 0.050s, Pack+Encode: 2.654s, Decode+Unpack: 2.526s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1344.3526 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03325584-0.000773_candle _ candle_0.810919.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n03325584-0.000773_candle _ candle_0.810919.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03355925-0.004997_spider web _ spider web_0.9142101.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03355925-0.004997_spider web _ spider web_0.9142101.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 482,704B, BPFP=0.9162 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 395,508B, BPFP=0.7507 +⌛️ [2/4] FRONTEND: Frontend time: 2.608s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.517s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09873271 12.59204932 + layer.39.0 6.60211199 636.12591108 + ------------------------------------------------------------------------------------- + TOTAL 3.35042235 324.35898020 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 878212 +BPFP 0.8335 bits/point +EBPFP 0.8335 equivalent bits/point +MSE 324.358980 +---------------------- -------------------------------------------------------- +Time: 5.184s Load: 0.059s, Pack+Encode: 2.608s, Decode+Unpack: 2.517s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 324.3590 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03355925-0.004997_spider web _ spider web_0.9142101.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n03355925-0.004997_spider web _ spider web_0.9142101.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03384352-0.002273_viaduct _ viaduct_0.6161024.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03384352-0.002273_viaduct _ viaduct_0.6161024.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 638,492B, BPFP=1.2119 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 585,412B, BPFP=1.1112 +⌛️ [2/4] FRONTEND: Frontend time: 2.619s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.521s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09647940 12.69040122 + layer.39.0 175.50411504 1856.31547619 + ------------------------------------------------------------------------------------- + TOTAL 87.80029722 934.50293870 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1223904 +BPFP 1.1615 bits/point +EBPFP 1.1615 equivalent bits/point +MSE 934.502939 +---------------------- -------------------------------------------------------- +Time: 5.190s Load: 0.050s, Pack+Encode: 2.619s, Decode+Unpack: 2.521s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 934.5029 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03384352-0.002273_viaduct _ viaduct_0.6161024.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n03384352-0.002273_viaduct _ viaduct_0.6161024.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03388043-0.005154_candle _ candle_0.9636924.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03388043-0.005154_candle _ candle_0.9636924.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 620,280B, BPFP=1.1773 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 637,984B, BPFP=1.2109 +⌛️ [2/4] FRONTEND: Frontend time: 2.617s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.547s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09640297 8.57266232 + layer.39.0 7.87377147 2188.65792031 + ------------------------------------------------------------------------------------- + TOTAL 3.98508722 1098.61529132 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1258264 +BPFP 1.1941 bits/point +EBPFP 1.1941 equivalent bits/point +MSE 1098.615291 +---------------------- -------------------------------------------------------- +Time: 5.214s Load: 0.050s, Pack+Encode: 2.617s, Decode+Unpack: 2.547s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1098.6153 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03388043-0.005154_candle _ candle_0.9636924.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n03388043-0.005154_candle _ candle_0.9636924.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03417042-0.001187_tank _ tank_0.70379025.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03417042-0.001187_tank _ tank_0.70379025.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 594,688B, BPFP=1.1288 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 592,396B, BPFP=1.1244 +⌛️ [2/4] FRONTEND: Frontend time: 2.617s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.525s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09848782 12.25556233 + layer.39.0 16.63742104 1893.44108358 + ------------------------------------------------------------------------------------- + TOTAL 8.36795443 952.84832295 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1187084 +BPFP 1.1266 bits/point +EBPFP 1.1266 equivalent bits/point +MSE 952.848323 +---------------------- -------------------------------------------------------- +Time: 5.194s Load: 0.051s, Pack+Encode: 2.617s, Decode+Unpack: 2.525s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 952.8483 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03417042-0.001187_tank _ tank_0.70379025.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n03417042-0.001187_tank _ tank_0.70379025.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03444034-0.002100_maraca _ maraca_0.502369.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03444034-0.002100_maraca _ maraca_0.502369.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 707,172B, BPFP=1.3423 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 790,928B, BPFP=1.5012 +⌛️ [2/4] FRONTEND: Frontend time: 2.648s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.541s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11197850 12.14154367 + layer.39.0 347.54634354 2785.42881438 + ------------------------------------------------------------------------------------- + TOTAL 173.82916102 1398.78517903 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1498100 +BPFP 1.4218 bits/point +EBPFP 1.4218 equivalent bits/point +MSE 1398.785179 +---------------------- -------------------------------------------------------- +Time: 5.241s Load: 0.051s, Pack+Encode: 2.648s, Decode+Unpack: 2.541s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1398.7852 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03444034-0.002100_maraca _ maraca_0.502369.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n03444034-0.002100_maraca _ maraca_0.502369.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03445924-0.003505_baseball player _ baseball player_0.6723684.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03445924-0.003505_baseball player _ baseball player_0.6723684.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 608,692B, BPFP=1.1553 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 655,524B, BPFP=1.2442 +⌛️ [2/4] FRONTEND: Frontend time: 2.635s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.529s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09665277 12.31477105 + layer.39.0 26.28463618 2434.53887269 + ------------------------------------------------------------------------------------- + TOTAL 13.19064447 1223.42682187 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1264216 +BPFP 1.1998 bits/point +EBPFP 1.1998 equivalent bits/point +MSE 1223.426822 +---------------------- -------------------------------------------------------- +Time: 5.224s Load: 0.060s, Pack+Encode: 2.635s, Decode+Unpack: 2.529s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1223.4268 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03445924-0.003505_baseball player _ baseball player_0.6723684.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n03445924-0.003505_baseball player _ baseball player_0.6723684.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03452741-0.002771_chain _ chain_0.9575044.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03452741-0.002771_chain _ chain_0.9575044.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 683,772B, BPFP=1.2979 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 729,992B, BPFP=1.3856 +⌛️ [2/4] FRONTEND: Frontend time: 2.624s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.523s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12351380 50.18489583 + layer.39.0 42.82565370 2195.89212828 + ------------------------------------------------------------------------------------- + TOTAL 21.47458375 1123.03851206 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1413764 +BPFP 1.3417 bits/point +EBPFP 1.3417 equivalent bits/point +MSE 1123.038512 +---------------------- -------------------------------------------------------- +Time: 5.209s Load: 0.062s, Pack+Encode: 2.624s, Decode+Unpack: 2.523s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1123.0385 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03452741-0.002771_chain _ chain_0.9575044.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n03452741-0.002771_chain _ chain_0.9575044.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03483316-0.004974_lighter _ lighter_0.27796906.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03483316-0.004974_lighter _ lighter_0.27796906.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 718,848B, BPFP=1.3644 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 663,140B, BPFP=1.2587 +⌛️ [2/4] FRONTEND: Frontend time: 2.638s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.518s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12993333 0.90554563 + layer.39.0 87.07173986 2318.27453839 + ------------------------------------------------------------------------------------- + TOTAL 43.60083660 1159.59004201 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1381988 +BPFP 1.3116 bits/point +EBPFP 1.3116 equivalent bits/point +MSE 1159.590042 +---------------------- -------------------------------------------------------- +Time: 5.207s Load: 0.050s, Pack+Encode: 2.638s, Decode+Unpack: 2.518s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1159.5900 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03483316-0.004974_lighter _ lighter_0.27796906.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n03483316-0.004974_lighter _ lighter_0.27796906.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03584829-0.104805_golf cart _ golf cart_0.73568964.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03584829-0.104805_golf cart _ golf cart_0.73568964.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 659,576B, BPFP=1.2519 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 602,296B, BPFP=1.1432 +⌛️ [2/4] FRONTEND: Frontend time: 2.625s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.531s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09917131 12.35212054 + layer.39.0 24.34873246 1854.91800292 + ------------------------------------------------------------------------------------- + TOTAL 12.22395189 933.63506173 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1261872 +BPFP 1.1976 bits/point +EBPFP 1.1976 equivalent bits/point +MSE 933.635062 +---------------------- -------------------------------------------------------- +Time: 5.217s Load: 0.060s, Pack+Encode: 2.625s, Decode+Unpack: 2.531s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 933.6351 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03584829-0.104805_golf cart _ golf cart_0.73568964.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n03584829-0.104805_golf cart _ golf cart_0.73568964.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03590841-0.001115_rocking chair _ rocking chair_0.2192492.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03590841-0.001115_rocking chair _ rocking chair_0.2192492.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 655,728B, BPFP=1.2446 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 560,248B, BPFP=1.0634 +⌛️ [2/4] FRONTEND: Frontend time: 2.620s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.522s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11329899 0.84909304 + layer.39.0 19.97532495 1710.33041788 + ------------------------------------------------------------------------------------- + TOTAL 10.04431197 855.58975546 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1215976 +BPFP 1.1540 bits/point +EBPFP 1.1540 equivalent bits/point +MSE 855.589755 +---------------------- -------------------------------------------------------- +Time: 5.193s Load: 0.050s, Pack+Encode: 2.620s, Decode+Unpack: 2.522s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 855.5898 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03590841-0.001115_rocking chair _ rocking chair_0.2192492.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n03590841-0.001115_rocking chair _ rocking chair_0.2192492.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03617480-0.003238_basketball _ basketball_0.67568874.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03617480-0.003238_basketball _ basketball_0.67568874.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 745,408B, BPFP=1.4148 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 753,856B, BPFP=1.4309 +⌛️ [2/4] FRONTEND: Frontend time: 2.633s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.539s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12967051 0.86693209 + layer.39.0 57.10576865 2107.59305151 + ------------------------------------------------------------------------------------- + TOTAL 28.61771958 1054.22999180 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1499264 +BPFP 1.4229 bits/point +EBPFP 1.4229 equivalent bits/point +MSE 1054.229992 +---------------------- -------------------------------------------------------- +Time: 5.223s Load: 0.051s, Pack+Encode: 2.633s, Decode+Unpack: 2.539s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1054.2300 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03617480-0.003238_basketball _ basketball_0.67568874.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n03617480-0.003238_basketball _ basketball_0.67568874.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03666591-0.004622_torch _ torch_0.99906796.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03666591-0.004622_torch _ torch_0.99906796.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 536,760B, BPFP=1.0188 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 535,608B, BPFP=1.0166 +⌛️ [2/4] FRONTEND: Frontend time: 2.587s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.497s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.05477861 0.86474532 + layer.39.0 7.78975672 1360.42055394 + ------------------------------------------------------------------------------------- + TOTAL 7.92226767 680.64264963 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1072368 +BPFP 1.0177 bits/point +EBPFP 1.0177 equivalent bits/point +MSE 680.642650 +---------------------- -------------------------------------------------------- +Time: 5.135s Load: 0.051s, Pack+Encode: 2.587s, Decode+Unpack: 2.497s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 680.6426 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03666591-0.004622_torch _ torch_0.99906796.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n03666591-0.004622_torch _ torch_0.99906796.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03670208-0.003899_hair dryer _ grand piano_0.7359066.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03670208-0.003899_hair dryer _ grand piano_0.7359066.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 631,772B, BPFP=1.1992 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 724,532B, BPFP=1.3752 +⌛️ [2/4] FRONTEND: Frontend time: 2.630s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.543s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11232473 12.78478423 + layer.39.0 36.60432231 2614.40281827 + ------------------------------------------------------------------------------------- + TOTAL 18.35832352 1313.59380125 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1356304 +BPFP 1.2872 bits/point +EBPFP 1.2872 equivalent bits/point +MSE 1313.593801 +---------------------- -------------------------------------------------------- +Time: 5.223s Load: 0.050s, Pack+Encode: 2.630s, Decode+Unpack: 2.543s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1313.5938 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03670208-0.003899_hair dryer _ grand piano_0.7359066.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n03670208-0.003899_hair dryer _ grand piano_0.7359066.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03717622-0.001175_sundial _ sundial_0.9998197.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03717622-0.001175_sundial _ sundial_0.9998197.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 719,600B, BPFP=1.3659 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 707,424B, BPFP=1.3427 +⌛️ [2/4] FRONTEND: Frontend time: 2.650s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.534s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13381931 0.85084135 + layer.39.0 773.52204810 2327.88022352 + ------------------------------------------------------------------------------------- + TOTAL 386.82793371 1164.36553243 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1427024 +BPFP 1.3543 bits/point +EBPFP 1.3543 equivalent bits/point +MSE 1164.365532 +---------------------- -------------------------------------------------------- +Time: 5.234s Load: 0.050s, Pack+Encode: 2.650s, Decode+Unpack: 2.534s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1164.3655 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03717622-0.001175_sundial _ sundial_0.9998197.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n03717622-0.001175_sundial _ sundial_0.9998197.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03720891-0.001854_African bush elephant _ African bush elephant_0.722115.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03720891-0.001854_African bush elephant _ African bush elephant_0.722115.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 647,196B, BPFP=1.2284 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 788,304B, BPFP=1.4963 +⌛️ [2/4] FRONTEND: Frontend time: 2.645s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.539s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09642763 11.99390811 + layer.39.0 155.23232507 2918.40913508 + ------------------------------------------------------------------------------------- + TOTAL 77.66437635 1465.20152160 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1435500 +BPFP 1.3623 bits/point +EBPFP 1.3623 equivalent bits/point +MSE 1465.201522 +---------------------- -------------------------------------------------------- +Time: 5.235s Load: 0.051s, Pack+Encode: 2.645s, Decode+Unpack: 2.539s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1465.2015 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03720891-0.001854_African bush elephant _ African bush elephant_0.722115.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n03720891-0.001854_African bush elephant _ African bush elephant_0.722115.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03721384-0.003327_chain _ chain_0.5599652.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03721384-0.003327_chain _ chain_0.5599652.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 537,296B, BPFP=1.0198 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 532,424B, BPFP=1.0106 +⌛️ [2/4] FRONTEND: Frontend time: 2.615s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.529s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09561452 12.49542942 + layer.39.0 742.66502672 1877.86588921 + ------------------------------------------------------------------------------------- + TOTAL 371.38032062 945.18065932 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1069720 +BPFP 1.0152 bits/point +EBPFP 1.0152 equivalent bits/point +MSE 945.180659 +---------------------- -------------------------------------------------------- +Time: 5.195s Load: 0.051s, Pack+Encode: 2.615s, Decode+Unpack: 2.529s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 945.1807 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03721384-0.003327_chain _ chain_0.5599652.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n03721384-0.003327_chain _ chain_0.5599652.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03724870-0.001366_Christmas stocking _ Christmas stocking_0.5709616.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03724870-0.001366_Christmas stocking _ Christmas stocking_0.5709616.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 666,368B, BPFP=1.2648 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 607,012B, BPFP=1.1522 +⌛️ [2/4] FRONTEND: Frontend time: 2.628s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.520s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10329660 0.80267820 + layer.39.0 513.92243683 2043.40063168 + ------------------------------------------------------------------------------------- + TOTAL 257.01286671 1022.10165494 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1273380 +BPFP 1.2085 bits/point +EBPFP 1.2085 equivalent bits/point +MSE 1022.101655 +---------------------- -------------------------------------------------------- +Time: 5.208s Load: 0.060s, Pack+Encode: 2.628s, Decode+Unpack: 2.520s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1022.1017 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03724870-0.001366_Christmas stocking _ Christmas stocking_0.5709616.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n03724870-0.001366_Christmas stocking _ Christmas stocking_0.5709616.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03775071-0.000145_Christmas stocking _ Christmas stocking_0.9986047.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03775071-0.000145_Christmas stocking _ Christmas stocking_0.9986047.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 649,444B, BPFP=1.2327 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 679,736B, BPFP=1.2902 +⌛️ [2/4] FRONTEND: Frontend time: 2.625s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.517s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09700392 0.80702161 + layer.39.0 284.92189018 1865.90755588 + ------------------------------------------------------------------------------------- + TOTAL 142.50944705 933.35728874 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1329180 +BPFP 1.2614 bits/point +EBPFP 1.2614 equivalent bits/point +MSE 933.357289 +---------------------- -------------------------------------------------------- +Time: 5.193s Load: 0.050s, Pack+Encode: 2.625s, Decode+Unpack: 2.517s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 933.3573 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03775071-0.000145_Christmas stocking _ Christmas stocking_0.9986047.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n03775071-0.000145_Christmas stocking _ Christmas stocking_0.9986047.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03788195-0.005975_steam locomotive _ steam locomotive_0.42419377.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03788195-0.005975_steam locomotive _ steam locomotive_0.42419377.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 723,964B, BPFP=1.3741 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 648,824B, BPFP=1.2315 +⌛️ [2/4] FRONTEND: Frontend time: 2.627s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.529s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10790903 13.13098275 + layer.39.0 10.34781284 1974.78462099 + ------------------------------------------------------------------------------------- + TOTAL 5.22786094 993.95780187 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1372788 +BPFP 1.3028 bits/point +EBPFP 1.3028 equivalent bits/point +MSE 993.957802 +---------------------- -------------------------------------------------------- +Time: 5.217s Load: 0.060s, Pack+Encode: 2.627s, Decode+Unpack: 2.529s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 993.9578 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03788195-0.005975_steam locomotive _ steam locomotive_0.42419377.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n03788195-0.005975_steam locomotive _ steam locomotive_0.42419377.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03837869-0.002023_lighthouse _ lighthouse_0.5898798.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03837869-0.002023_lighthouse _ lighthouse_0.5898798.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 625,344B, BPFP=1.1870 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 573,520B, BPFP=1.0886 +⌛️ [2/4] FRONTEND: Frontend time: 2.613s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.526s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12703056 12.69666393 + layer.39.0 141.21340500 2226.17662779 + ------------------------------------------------------------------------------------- + TOTAL 70.67021778 1119.43664586 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1198864 +BPFP 1.1378 bits/point +EBPFP 1.1378 equivalent bits/point +MSE 1119.436646 +---------------------- -------------------------------------------------------- +Time: 5.189s Load: 0.050s, Pack+Encode: 2.613s, Decode+Unpack: 2.526s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1119.4366 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03837869-0.002023_lighthouse _ lighthouse_0.5898798.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n03837869-0.002023_lighthouse _ lighthouse_0.5898798.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03840681-0.002188_ladybug _ ladybug_0.9972365.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03840681-0.002188_ladybug _ ladybug_0.9972365.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 548,240B, BPFP=1.0406 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 449,220B, BPFP=0.8527 +⌛️ [2/4] FRONTEND: Frontend time: 2.619s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.536s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09487485 0.82740996 + layer.39.0 29.40353574 705.18500972 + ------------------------------------------------------------------------------------- + TOTAL 14.74920530 353.00620984 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 997460 +BPFP 0.9466 bits/point +EBPFP 0.9466 equivalent bits/point +MSE 353.006210 +---------------------- -------------------------------------------------------- +Time: 5.205s Load: 0.050s, Pack+Encode: 2.619s, Decode+Unpack: 2.536s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 353.0062 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03840681-0.002188_ladybug _ ladybug_0.9972365.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n03840681-0.002188_ladybug _ ladybug_0.9972365.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03888257-0.004083_rugby ball _ snail_0.41588444.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03888257-0.004083_rugby ball _ snail_0.41588444.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 611,448B, BPFP=1.1606 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 482,144B, BPFP=0.9151 +⌛️ [2/4] FRONTEND: Frontend time: 2.615s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.507s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10005040 8.70930515 + layer.39.0 7.47115060 986.94169096 + ------------------------------------------------------------------------------------- + TOTAL 3.78560050 497.82549806 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1093592 +BPFP 1.0379 bits/point +EBPFP 1.0379 equivalent bits/point +MSE 497.825498 +---------------------- -------------------------------------------------------- +Time: 5.182s Load: 0.060s, Pack+Encode: 2.615s, Decode+Unpack: 2.507s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 497.8255 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03888257-0.004083_rugby ball _ snail_0.41588444.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n03888257-0.004083_rugby ball _ snail_0.41588444.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03891332-0.003727_syringe _ syringe_0.93799996.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03891332-0.003727_syringe _ syringe_0.93799996.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 609,776B, BPFP=1.1574 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 633,824B, BPFP=1.2030 +⌛️ [2/4] FRONTEND: Frontend time: 2.627s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.529s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09617506 0.79680969 + layer.39.0 18.45312310 2254.66083576 + ------------------------------------------------------------------------------------- + TOTAL 9.27464908 1127.72882273 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1243600 +BPFP 1.1802 bits/point +EBPFP 1.1802 equivalent bits/point +MSE 1127.728823 +---------------------- -------------------------------------------------------- +Time: 5.206s Load: 0.051s, Pack+Encode: 2.627s, Decode+Unpack: 2.529s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1127.7288 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03891332-0.003727_syringe _ syringe_0.93799996.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n03891332-0.003727_syringe _ syringe_0.93799996.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03982430-0.005102_couch _ couch_0.9976859.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03982430-0.005102_couch _ couch_0.9976859.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 481,636B, BPFP=0.9142 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 519,372B, BPFP=0.9858 +⌛️ [2/4] FRONTEND: Frontend time: 2.624s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.503s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09691652 0.78623810 + layer.39.0 169.89398081 1255.15646259 + ------------------------------------------------------------------------------------- + TOTAL 84.99544866 627.97135034 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1001008 +BPFP 0.9500 bits/point +EBPFP 0.9500 equivalent bits/point +MSE 627.971350 +---------------------- -------------------------------------------------------- +Time: 5.176s Load: 0.050s, Pack+Encode: 2.624s, Decode+Unpack: 2.503s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 627.9714 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03982430-0.005102_couch _ couch_0.9976859.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n03982430-0.005102_couch _ couch_0.9976859.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04033901-0.007476_envelope _ envelope_0.9990971.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04033901-0.007476_envelope _ envelope_0.9990971.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 547,820B, BPFP=1.0398 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 546,740B, BPFP=1.0378 +⌛️ [2/4] FRONTEND: Frontend time: 2.618s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.543s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10364226 8.63486243 + layer.39.0 7.34252906 1441.69679300 + ------------------------------------------------------------------------------------- + TOTAL 3.72308566 725.16582772 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1094560 +BPFP 1.0388 bits/point +EBPFP 1.0388 equivalent bits/point +MSE 725.165828 +---------------------- -------------------------------------------------------- +Time: 5.210s Load: 0.050s, Pack+Encode: 2.618s, Decode+Unpack: 2.543s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 725.1658 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04033901-0.007476_envelope _ envelope_0.9990971.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n04033901-0.007476_envelope _ envelope_0.9990971.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04039381-0.000054_hair dryer _ hair dryer_0.5667548.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04039381-0.000054_hair dryer _ hair dryer_0.5667548.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 580,280B, BPFP=1.1014 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 683,956B, BPFP=1.2982 +⌛️ [2/4] FRONTEND: Frontend time: 2.641s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.532s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09588603 12.30143799 + layer.39.0 26.21653304 2511.48542274 + ------------------------------------------------------------------------------------- + TOTAL 13.15620954 1261.89343036 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1264236 +BPFP 1.1998 bits/point +EBPFP 1.1998 equivalent bits/point +MSE 1261.893430 +---------------------- -------------------------------------------------------- +Time: 5.224s Load: 0.050s, Pack+Encode: 2.641s, Decode+Unpack: 2.532s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1261.8934 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04039381-0.000054_hair dryer _ hair dryer_0.5667548.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n04039381-0.000054_hair dryer _ hair dryer_0.5667548.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04118538-0.005804_cowboy boot _ cowboy boot_0.21208441.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04118538-0.005804_cowboy boot _ cowboy boot_0.21208441.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 649,704B, BPFP=1.2332 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 629,904B, BPFP=1.1956 +⌛️ [2/4] FRONTEND: Frontend time: 2.637s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.517s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09664223 11.91078262 + layer.39.0 8.64007266 1876.16593780 + ------------------------------------------------------------------------------------- + TOTAL 4.36835744 944.03836021 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1279608 +BPFP 1.2144 bits/point +EBPFP 1.2144 equivalent bits/point +MSE 944.038360 +---------------------- -------------------------------------------------------- +Time: 5.204s Load: 0.050s, Pack+Encode: 2.637s, Decode+Unpack: 2.517s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 944.0384 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04118538-0.005804_cowboy boot _ cowboy boot_0.21208441.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n04118538-0.005804_cowboy boot _ cowboy boot_0.21208441.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04146614-0.008793_marimba _ marimba_0.54555196.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04146614-0.008793_marimba _ marimba_0.54555196.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 656,268B, BPFP=1.2456 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 613,404B, BPFP=1.1643 +⌛️ [2/4] FRONTEND: Frontend time: 2.638s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.520s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09774729 0.79085553 + layer.39.0 155.07908163 1933.52113703 + ------------------------------------------------------------------------------------- + TOTAL 77.58841446 967.15599628 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1269672 +BPFP 1.2050 bits/point +EBPFP 1.2050 equivalent bits/point +MSE 967.155996 +---------------------- -------------------------------------------------------- +Time: 5.218s Load: 0.060s, Pack+Encode: 2.638s, Decode+Unpack: 2.520s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 967.1560 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04146614-0.008793_marimba _ marimba_0.54555196.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n04146614-0.008793_marimba _ marimba_0.54555196.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04179913-0.006024_guacamole _ custard apple_0.5550306.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04179913-0.006024_guacamole _ custard apple_0.5550306.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 679,224B, BPFP=1.2892 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 693,732B, BPFP=1.3168 +⌛️ [2/4] FRONTEND: Frontend time: 2.643s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.542s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11409367 8.96509430 + layer.39.0 68.43204871 2331.96598639 + ------------------------------------------------------------------------------------- + TOTAL 34.27307119 1170.46554035 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1372956 +BPFP 1.3030 bits/point +EBPFP 1.3030 equivalent bits/point +MSE 1170.465540 +---------------------- -------------------------------------------------------- +Time: 5.255s Load: 0.071s, Pack+Encode: 2.643s, Decode+Unpack: 2.542s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1170.4655 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04179913-0.006024_guacamole _ custard apple_0.5550306.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n04179913-0.006024_guacamole _ custard apple_0.5550306.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04208210-0.007213_doormat _ doormat_0.81736016.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04208210-0.007213_doormat _ doormat_0.81736016.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 725,064B, BPFP=1.3762 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 606,368B, BPFP=1.1509 +⌛️ [2/4] FRONTEND: Frontend time: 2.638s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.540s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10601767 12.10578668 + layer.39.0 349.44518343 1720.39613703 + ------------------------------------------------------------------------------------- + TOTAL 174.77560055 866.25096185 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1331432 +BPFP 1.2636 bits/point +EBPFP 1.2636 equivalent bits/point +MSE 866.250962 +---------------------- -------------------------------------------------------- +Time: 5.239s Load: 0.060s, Pack+Encode: 2.638s, Decode+Unpack: 2.540s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 866.2510 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04208210-0.007213_doormat _ doormat_0.81736016.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n04208210-0.007213_doormat _ doormat_0.81736016.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04270147-0.000435_rotary dial telephone _ rotary dial telephone_0.9268941.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04270147-0.000435_rotary dial telephone _ rotary dial telephone_0.9268941.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 581,192B, BPFP=1.1031 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 719,468B, BPFP=1.3656 +⌛️ [2/4] FRONTEND: Frontend time: 2.654s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.537s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09464848 12.39623668 + layer.39.0 229.78908528 3202.72060253 + ------------------------------------------------------------------------------------- + TOTAL 114.94186688 1607.55841960 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1300660 +BPFP 1.2344 bits/point +EBPFP 1.2344 equivalent bits/point +MSE 1607.558420 +---------------------- -------------------------------------------------------- +Time: 5.251s Load: 0.060s, Pack+Encode: 2.654s, Decode+Unpack: 2.537s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1607.5584 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04270147-0.000435_rotary dial telephone _ rotary dial telephone_0.9268941.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n04270147-0.000435_rotary dial telephone _ rotary dial telephone_0.9268941.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04317175-0.006894_bow tie _ bow tie_0.95877784.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04317175-0.006894_bow tie _ bow tie_0.95877784.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 616,408B, BPFP=1.1700 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 671,384B, BPFP=1.2743 +⌛️ [2/4] FRONTEND: Frontend time: 2.624s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.519s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09706025 0.80467332 + layer.39.0 10.87108806 2652.08965015 + ------------------------------------------------------------------------------------- + TOTAL 5.48407415 1326.44716173 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1287792 +BPFP 1.2222 bits/point +EBPFP 1.2222 equivalent bits/point +MSE 1326.447162 +---------------------- -------------------------------------------------------- +Time: 5.204s Load: 0.061s, Pack+Encode: 2.624s, Decode+Unpack: 2.519s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1326.4472 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04317175-0.006894_bow tie _ bow tie_0.95877784.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n04317175-0.006894_bow tie _ bow tie_0.95877784.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04347754-0.001405_viaduct _ viaduct_0.8445672.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04347754-0.001405_viaduct _ viaduct_0.8445672.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 498,336B, BPFP=0.9459 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 596,304B, BPFP=1.1318 +⌛️ [2/4] FRONTEND: Frontend time: 2.636s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.526s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09586499 0.79881899 + layer.39.0 267.55718537 1979.60811467 + ------------------------------------------------------------------------------------- + TOTAL 133.82652518 990.20346683 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1094640 +BPFP 1.0389 bits/point +EBPFP 1.0389 equivalent bits/point +MSE 990.203467 +---------------------- -------------------------------------------------------- +Time: 5.223s Load: 0.061s, Pack+Encode: 2.636s, Decode+Unpack: 2.526s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 990.2035 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04347754-0.001405_viaduct _ viaduct_0.8445672.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n04347754-0.001405_viaduct _ viaduct_0.8445672.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04355338-0.000791_envelope _ envelope_0.9969598.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04355338-0.000791_envelope _ envelope_0.9969598.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.058s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 615,688B, BPFP=1.1686 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 615,496B, BPFP=1.1683 +⌛️ [2/4] FRONTEND: Frontend time: 2.653s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.511s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10273007 12.41680618 + layer.39.0 331.89978134 1520.45128766 + ------------------------------------------------------------------------------------- + TOTAL 166.00125571 766.43404692 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1231184 +BPFP 1.1684 bits/point +EBPFP 1.1684 equivalent bits/point +MSE 766.434047 +---------------------- -------------------------------------------------------- +Time: 5.222s Load: 0.058s, Pack+Encode: 2.653s, Decode+Unpack: 2.511s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 766.4340 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04355338-0.000791_envelope _ envelope_0.9969598.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n04355338-0.000791_envelope _ envelope_0.9969598.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04366367-0.002021_parachute _ parachute_0.9226023.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04366367-0.002021_parachute _ parachute_0.9226023.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 542,660B, BPFP=1.0300 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 462,636B, BPFP=0.8781 +⌛️ [2/4] FRONTEND: Frontend time: 2.629s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.516s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09577132 1.24478016 + layer.39.0 47.60657343 1384.33029640 + ------------------------------------------------------------------------------------- + TOTAL 23.85117238 692.78753828 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1005296 +BPFP 0.9541 bits/point +EBPFP 0.9541 equivalent bits/point +MSE 692.787538 +---------------------- -------------------------------------------------------- +Time: 5.196s Load: 0.050s, Pack+Encode: 2.629s, Decode+Unpack: 2.516s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 692.7875 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04366367-0.002021_parachute _ parachute_0.9226023.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n04366367-0.002021_parachute _ parachute_0.9226023.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04376876-0.004615_lighter _ lighter_0.69176143.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04376876-0.004615_lighter _ lighter_0.69176143.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 599,964B, BPFP=1.1388 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 681,748B, BPFP=1.2940 +⌛️ [2/4] FRONTEND: Frontend time: 2.633s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.523s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09912059 8.61810712 + layer.39.0 173.01079628 2379.27575316 + ------------------------------------------------------------------------------------- + TOTAL 86.55495844 1193.94693014 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1281712 +BPFP 1.2164 bits/point +EBPFP 1.2164 equivalent bits/point +MSE 1193.946930 +---------------------- -------------------------------------------------------- +Time: 5.218s Load: 0.062s, Pack+Encode: 2.633s, Decode+Unpack: 2.523s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1193.9469 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04376876-0.004615_lighter _ lighter_0.69176143.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n04376876-0.004615_lighter _ lighter_0.69176143.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04442312-0.001690_washing machine _ washing machine_0.8494714.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04442312-0.001690_washing machine _ washing machine_0.8494714.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 517,324B, BPFP=0.9819 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 574,976B, BPFP=1.0914 +⌛️ [2/4] FRONTEND: Frontend time: 2.608s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.536s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.08302300 12.52163053 + layer.39.0 28.24609944 1350.29749757 + ------------------------------------------------------------------------------------- + TOTAL 18.16456122 681.40956405 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1092300 +BPFP 1.0366 bits/point +EBPFP 1.0366 equivalent bits/point +MSE 681.409564 +---------------------- -------------------------------------------------------- +Time: 5.206s Load: 0.061s, Pack+Encode: 2.608s, Decode+Unpack: 2.536s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 681.4096 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04442312-0.001690_washing machine _ washing machine_0.8494714.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n04442312-0.001690_washing machine _ washing machine_0.8494714.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04456115-0.002573_hair dryer _ envelope_0.34823084.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04456115-0.002573_hair dryer _ envelope_0.34823084.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 546,968B, BPFP=1.0382 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 646,920B, BPFP=1.2279 +⌛️ [2/4] FRONTEND: Frontend time: 2.627s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.530s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09444211 0.79251718 + layer.39.0 8.80792942 2585.23032070 + ------------------------------------------------------------------------------------- + TOTAL 4.45118577 1293.01141894 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1193888 +BPFP 1.1330 bits/point +EBPFP 1.1330 equivalent bits/point +MSE 1293.011419 +---------------------- -------------------------------------------------------- +Time: 5.207s Load: 0.050s, Pack+Encode: 2.627s, Decode+Unpack: 2.530s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1293.0114 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04456115-0.002573_hair dryer _ envelope_0.34823084.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n04456115-0.002573_hair dryer _ envelope_0.34823084.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04509417-0.000350_sundial _ sundial_0.99936897.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04509417-0.000350_sundial _ sundial_0.99936897.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 617,392B, BPFP=1.1719 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 562,304B, BPFP=1.0673 +⌛️ [2/4] FRONTEND: Frontend time: 2.623s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.522s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10319057 8.60417236 + layer.39.0 8.14296913 1429.14808066 + ------------------------------------------------------------------------------------- + TOTAL 4.12307985 718.87612651 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1179696 +BPFP 1.1196 bits/point +EBPFP 1.1196 equivalent bits/point +MSE 718.876127 +---------------------- -------------------------------------------------------- +Time: 5.196s Load: 0.052s, Pack+Encode: 2.623s, Decode+Unpack: 2.522s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 718.8761 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04509417-0.000350_sundial _ sundial_0.99936897.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n04509417-0.000350_sundial _ sundial_0.99936897.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04532670-0.000670_spider web _ spider web_0.70711935.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04532670-0.000670_spider web _ spider web_0.70711935.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 688,504B, BPFP=1.3068 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 719,684B, BPFP=1.3660 +⌛️ [2/4] FRONTEND: Frontend time: 2.651s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.525s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09618602 12.95803533 + layer.39.0 175.41615039 2166.32167153 + ------------------------------------------------------------------------------------- + TOTAL 87.75616821 1089.63985343 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1408188 +BPFP 1.3364 bits/point +EBPFP 1.3364 equivalent bits/point +MSE 1089.639853 +---------------------- -------------------------------------------------------- +Time: 5.236s Load: 0.060s, Pack+Encode: 2.651s, Decode+Unpack: 2.525s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1089.6399 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04532670-0.000670_spider web _ spider web_0.70711935.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n04532670-0.000670_spider web _ spider web_0.70711935.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04540053-0.000734_balance beam _ balance beam_0.99765223.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04540053-0.000734_balance beam _ balance beam_0.99765223.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 549,244B, BPFP=1.0425 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 574,952B, BPFP=1.0913 +⌛️ [2/4] FRONTEND: Frontend time: 2.632s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.520s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09941827 8.74014327 + layer.39.0 8.11341412 1837.58138970 + ------------------------------------------------------------------------------------- + TOTAL 4.10641619 923.16076648 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1124196 +BPFP 1.0669 bits/point +EBPFP 1.0669 equivalent bits/point +MSE 923.160766 +---------------------- -------------------------------------------------------- +Time: 5.213s Load: 0.061s, Pack+Encode: 2.632s, Decode+Unpack: 2.520s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 923.1608 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04540053-0.000734_balance beam _ balance beam_0.99765223.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n04540053-0.000734_balance beam _ balance beam_0.99765223.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04606251-0.003483_cliff _ cliff_0.9972174.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04606251-0.003483_cliff _ cliff_0.9972174.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 634,664B, BPFP=1.2046 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 508,672B, BPFP=0.9655 +⌛️ [2/4] FRONTEND: Frontend time: 2.633s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.526s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09940710 12.00835725 + layer.39.0 906.86880466 1560.29105928 + ------------------------------------------------------------------------------------- + TOTAL 453.48410588 786.14970827 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1143336 +BPFP 1.0851 bits/point +EBPFP 1.0851 equivalent bits/point +MSE 786.149708 +---------------------- -------------------------------------------------------- +Time: 5.219s Load: 0.060s, Pack+Encode: 2.633s, Decode+Unpack: 2.526s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 786.1497 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04606251-0.003483_cliff _ cliff_0.9972174.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n04606251-0.003483_cliff _ cliff_0.9972174.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07583066-0.003935_maraca _ maraca_0.5370013.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07583066-0.003935_maraca _ maraca_0.5370013.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 565,320B, BPFP=1.0730 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 515,816B, BPFP=0.9791 +⌛️ [2/4] FRONTEND: Frontend time: 2.615s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.540s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12045678 1.20810639 + layer.39.0 38.29438092 1587.90002430 + ------------------------------------------------------------------------------------- + TOTAL 19.20741885 794.55406534 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1081136 +BPFP 1.0260 bits/point +EBPFP 1.0260 equivalent bits/point +MSE 794.554065 +---------------------- -------------------------------------------------------- +Time: 5.216s Load: 0.061s, Pack+Encode: 2.615s, Decode+Unpack: 2.540s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 794.5541 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07583066-0.003935_maraca _ maraca_0.5370013.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n07583066-0.003935_maraca _ maraca_0.5370013.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07695742-0.003226_ant _ ant_0.64413536.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07695742-0.003226_ant _ ant_0.64413536.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 794,688B, BPFP=1.5084 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 668,496B, BPFP=1.2689 +⌛️ [2/4] FRONTEND: Frontend time: 2.651s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.555s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.16263347 13.08071968 + layer.39.0 172.10254191 2546.52988338 + ------------------------------------------------------------------------------------- + TOTAL 86.13258769 1279.80530153 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1463184 +BPFP 1.3886 bits/point +EBPFP 1.3886 equivalent bits/point +MSE 1279.805302 +---------------------- -------------------------------------------------------- +Time: 5.256s Load: 0.050s, Pack+Encode: 2.651s, Decode+Unpack: 2.555s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1279.8053 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07695742-0.003226_ant _ ant_0.64413536.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n07695742-0.003226_ant _ ant_0.64413536.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07718472-0.001330_spatula _ spatula_0.5388231.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07718472-0.001330_spatula _ spatula_0.5388231.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 612,408B, BPFP=1.1624 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 626,416B, BPFP=1.1890 +⌛️ [2/4] FRONTEND: Frontend time: 2.622s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.534s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09672572 12.59850944 + layer.39.0 34.52145211 1944.65087464 + ------------------------------------------------------------------------------------- + TOTAL 17.30908891 978.62469204 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1238824 +BPFP 1.1757 bits/point +EBPFP 1.1757 equivalent bits/point +MSE 978.624692 +---------------------- -------------------------------------------------------- +Time: 5.206s Load: 0.050s, Pack+Encode: 2.622s, Decode+Unpack: 2.534s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 978.6247 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07718472-0.001330_spatula _ spatula_0.5388231.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n07718472-0.001330_spatula _ spatula_0.5388231.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07831146-0.002710_guacamole _ guacamole_0.99510396.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07831146-0.002710_guacamole _ guacamole_0.99510396.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 649,468B, BPFP=1.2327 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 696,744B, BPFP=1.3225 +⌛️ [2/4] FRONTEND: Frontend time: 2.625s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.533s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09717902 12.34392083 + layer.39.0 26.55584533 2888.08066084 + ------------------------------------------------------------------------------------- + TOTAL 13.32651218 1450.21229083 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1346212 +BPFP 1.2776 bits/point +EBPFP 1.2776 equivalent bits/point +MSE 1450.212291 +---------------------- -------------------------------------------------------- +Time: 5.219s Load: 0.061s, Pack+Encode: 2.625s, Decode+Unpack: 2.533s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1450.2123 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07831146-0.002710_guacamole _ guacamole_0.99510396.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n07831146-0.002710_guacamole _ guacamole_0.99510396.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n09229709-0.002628_stethoscope _ stethoscope_0.9726458.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n09229709-0.002628_stethoscope _ stethoscope_0.9726458.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 603,644B, BPFP=1.1458 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 614,296B, BPFP=1.1660 +⌛️ [2/4] FRONTEND: Frontend time: 2.625s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.536s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10247729 12.43342008 + layer.39.0 58.71458181 1588.85860058 + ------------------------------------------------------------------------------------- + TOTAL 29.40852955 800.64601033 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1217940 +BPFP 1.1559 bits/point +EBPFP 1.1559 equivalent bits/point +MSE 800.646010 +---------------------- -------------------------------------------------------- +Time: 5.222s Load: 0.061s, Pack+Encode: 2.625s, Decode+Unpack: 2.536s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 800.6460 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n09229709-0.002628_stethoscope _ stethoscope_0.9726458.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n09229709-0.002628_stethoscope _ stethoscope_0.9726458.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12057211-0.000404_nail _ newt_0.31321314.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12057211-0.000404_nail _ newt_0.31321314.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 795,368B, BPFP=1.5097 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 549,364B, BPFP=1.0427 +⌛️ [2/4] FRONTEND: Frontend time: 2.628s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.535s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11577855 37.17789571 + layer.39.0 8.72387956 1409.01166181 + ------------------------------------------------------------------------------------- + TOTAL 4.41982905 723.09477876 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1344732 +BPFP 1.2762 bits/point +EBPFP 1.2762 equivalent bits/point +MSE 723.094779 +---------------------- -------------------------------------------------------- +Time: 5.223s Load: 0.061s, Pack+Encode: 2.628s, Decode+Unpack: 2.535s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 723.0948 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12057211-0.000404_nail _ newt_0.31321314.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n12057211-0.000404_nail _ newt_0.31321314.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12144580-0.002806_banana _ banana_0.999156.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12144580-0.002806_banana _ banana_0.999156.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 660,316B, BPFP=1.2533 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 678,644B, BPFP=1.2881 +⌛️ [2/4] FRONTEND: Frontend time: 2.624s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.537s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09629347 12.09124169 + layer.39.0 105.38953930 2244.80733722 + ------------------------------------------------------------------------------------- + TOTAL 52.74291638 1128.44928945 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1338960 +BPFP 1.2707 bits/point +EBPFP 1.2707 equivalent bits/point +MSE 1128.449289 +---------------------- -------------------------------------------------------- +Time: 5.223s Load: 0.062s, Pack+Encode: 2.624s, Decode+Unpack: 2.537s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1128.4493 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12144580-0.002806_banana _ banana_0.999156.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n12144580-0.002806_banana _ banana_0.999156.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12267677-0.004617_pomegranate _ pomegranate_0.9159373.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12267677-0.004617_pomegranate _ pomegranate_0.9159373.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 650,816B, BPFP=1.2353 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 635,492B, BPFP=1.2062 +⌛️ [2/4] FRONTEND: Frontend time: 2.638s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.536s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10323383 12.51407806 + layer.39.0 78.12042942 2348.01068999 + ------------------------------------------------------------------------------------- + TOTAL 39.11183162 1180.26238403 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1286308 +BPFP 1.2208 bits/point +EBPFP 1.2208 equivalent bits/point +MSE 1180.262384 +---------------------- -------------------------------------------------------- +Time: 5.225s Load: 0.051s, Pack+Encode: 2.638s, Decode+Unpack: 2.536s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1180.2624 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12267677-0.004617_pomegranate _ pomegranate_0.9159373.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1ka/n12267677-0.004617_pomegranate _ pomegranate_0.9159373.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 1.1838 bits/point +Avg EBPFP 1.1838 equivalent bits/point +Avg MSE 972.321753 +Avg Time 5.238s +------------------------ ---------------------------- diff --git a/lambda0.02/elic-featurecoding-8bit-individual/cls_in1kr/dtufc_elic-featurecoding_dinov3-total_individual.log b/lambda0.02/elic-featurecoding-8bit-individual/cls_in1kr/dtufc_elic-featurecoding_dinov3-total_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..bc484ffc56a2ab6e8947af21fadc298acaadfcb5 --- /dev/null +++ b/lambda0.02/elic-featurecoding-8bit-individual/cls_in1kr/dtufc_elic-featurecoding_dinov3-total_individual.log @@ -0,0 +1,9344 @@ +Experiment: dtufc_elic-featurecoding_dinov3-total_individual +Log file: output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/dtufc_elic-featurecoding_dinov3-total_individual.log +DTUFCCodecConfig: + arch: elic-featurecoding + handler: dinov3-total + checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.02_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.02_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 520 +Loaded elic-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.9' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.19' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.29' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.39' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json +Loaded per-key mappings: model=dinov3-total + Keys: ['layer.9', 'layer.19', 'layer.29', 'layer.39'] +---------------- ------------------------------------------------------------------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +Checkpoint codec_weights/elic_hybrid/elic2022-official_lambda0.02_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-DINOv3/imagenet1k-r +Output output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr +---------------- ------------------------------------------------------------------------------------------------------------------- +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01443537-graffiti_3.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01443537-graffiti_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 620,844B, BPFP=1.1784 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 581,068B, BPFP=1.1029 +⌛️ [2/4] FRONTEND: Frontend time: 3.106s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.710s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09690064 8.66496314 + layer.39.0 23.14008974 1740.24465500 + ------------------------------------------------------------------------------------- + TOTAL 11.61849519 874.45480907 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1201912 +BPFP 1.1407 bits/point +EBPFP 1.1407 equivalent bits/point +MSE 874.454809 +---------------------- -------------------------------------------------------- +Time: 5.888s Load: 0.072s, Pack+Encode: 3.106s, Decode+Unpack: 2.710s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 874.4548 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01443537-graffiti_3.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n01443537-graffiti_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01494475-misc_31.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01494475-misc_31.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 607,532B, BPFP=1.1531 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 690,468B, BPFP=1.3106 +⌛️ [2/4] FRONTEND: Frontend time: 2.640s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.544s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09558801 12.39054338 + layer.39.0 281.54433916 2565.18610301 + ------------------------------------------------------------------------------------- + TOTAL 140.81996359 1288.78832320 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1298000 +BPFP 1.2319 bits/point +EBPFP 1.2319 equivalent bits/point +MSE 1288.788323 +---------------------- -------------------------------------------------------- +Time: 5.265s Load: 0.080s, Pack+Encode: 2.640s, Decode+Unpack: 2.544s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1288.7883 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01494475-misc_31.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n01494475-misc_31.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01531178-cartoon_22.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01531178-cartoon_22.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.079s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 628,012B, BPFP=1.1920 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 637,568B, BPFP=1.2102 +⌛️ [2/4] FRONTEND: Frontend time: 2.647s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.546s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10319715 0.76950652 + layer.39.0 12.97479918 2177.46088435 + ------------------------------------------------------------------------------------- + TOTAL 6.53899817 1089.11519543 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1265580 +BPFP 1.2011 bits/point +EBPFP 1.2011 equivalent bits/point +MSE 1089.115195 +---------------------- -------------------------------------------------------- +Time: 5.272s Load: 0.079s, Pack+Encode: 2.647s, Decode+Unpack: 2.546s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1089.1152 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01531178-cartoon_22.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n01531178-cartoon_22.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01534433-painting_9.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01534433-painting_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 706,980B, BPFP=1.3419 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 539,464B, BPFP=1.0239 +⌛️ [2/4] FRONTEND: Frontend time: 2.633s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.534s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10660143 12.20374852 + layer.39.0 8.42910859 1419.18926142 + ------------------------------------------------------------------------------------- + TOTAL 4.26785501 715.69650497 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1246444 +BPFP 1.1829 bits/point +EBPFP 1.1829 equivalent bits/point +MSE 715.696505 +---------------------- -------------------------------------------------------- +Time: 5.226s Load: 0.059s, Pack+Encode: 2.633s, Decode+Unpack: 2.534s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 715.6965 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01534433-painting_9.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n01534433-painting_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01632777-toy_21.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01632777-toy_21.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 596,196B, BPFP=1.1316 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 644,512B, BPFP=1.2233 +⌛️ [2/4] FRONTEND: Frontend time: 2.619s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.521s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09516629 8.48013089 + layer.39.0 31.73491595 2108.52526725 + ------------------------------------------------------------------------------------- + TOTAL 15.91504112 1058.50269907 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1240708 +BPFP 1.1775 bits/point +EBPFP 1.1775 equivalent bits/point +MSE 1058.502699 +---------------------- -------------------------------------------------------- +Time: 5.191s Load: 0.051s, Pack+Encode: 2.619s, Decode+Unpack: 2.521s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1058.5027 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01632777-toy_21.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n01632777-toy_21.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01748264-misc_18.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01748264-misc_18.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 639,888B, BPFP=1.2146 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 558,076B, BPFP=1.0593 +⌛️ [2/4] FRONTEND: Frontend time: 2.635s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.527s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.16139180 36.66339438 + layer.39.0 362.83485180 1615.42614189 + ------------------------------------------------------------------------------------- + TOTAL 181.49812180 826.04476813 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1197964 +BPFP 1.1369 bits/point +EBPFP 1.1369 equivalent bits/point +MSE 826.044768 +---------------------- -------------------------------------------------------- +Time: 5.232s Load: 0.070s, Pack+Encode: 2.635s, Decode+Unpack: 2.527s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 826.0448 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01748264-misc_18.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n01748264-misc_18.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01784675-painting_2.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01784675-painting_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 741,000B, BPFP=1.4065 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 560,384B, BPFP=1.0637 +⌛️ [2/4] FRONTEND: Frontend time: 2.627s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.537s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13866578 0.89971888 + layer.39.0 232.10166120 1556.51797862 + ------------------------------------------------------------------------------------- + TOTAL 116.12016349 778.70884875 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1301384 +BPFP 1.2351 bits/point +EBPFP 1.2351 equivalent bits/point +MSE 778.708849 +---------------------- -------------------------------------------------------- +Time: 5.214s Load: 0.050s, Pack+Encode: 2.627s, Decode+Unpack: 2.537s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 778.7088 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01784675-painting_2.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n01784675-painting_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01820546-painting_29.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01820546-painting_29.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 713,656B, BPFP=1.3546 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 586,260B, BPFP=1.1128 +⌛️ [2/4] FRONTEND: Frontend time: 2.646s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.549s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10398871 0.85514347 + layer.39.0 202.99580904 1991.95663265 + ------------------------------------------------------------------------------------- + TOTAL 101.54989888 996.40588806 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1299916 +BPFP 1.2337 bits/point +EBPFP 1.2337 equivalent bits/point +MSE 996.405888 +---------------------- -------------------------------------------------------- +Time: 5.245s Load: 0.050s, Pack+Encode: 2.646s, Decode+Unpack: 2.549s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 996.4059 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01820546-painting_29.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n01820546-painting_29.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01833805-painting_23.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01833805-painting_23.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 633,856B, BPFP=1.2031 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 585,516B, BPFP=1.1114 +⌛️ [2/4] FRONTEND: Frontend time: 2.651s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.523s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09675035 12.36580475 + layer.39.0 56.43029868 2472.73347911 + ------------------------------------------------------------------------------------- + TOTAL 28.26352451 1242.54964193 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1219372 +BPFP 1.1572 bits/point +EBPFP 1.1572 equivalent bits/point +MSE 1242.549642 +---------------------- -------------------------------------------------------- +Time: 5.244s Load: 0.070s, Pack+Encode: 2.651s, Decode+Unpack: 2.523s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1242.5496 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01833805-painting_23.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n01833805-painting_23.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01860187-painting_2.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01860187-painting_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 654,228B, BPFP=1.2418 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 573,568B, BPFP=1.0887 +⌛️ [2/4] FRONTEND: Frontend time: 2.633s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.539s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09532418 0.77147198 + layer.39.0 11.39113179 1690.11103013 + ------------------------------------------------------------------------------------- + TOTAL 5.74322799 845.44125105 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1227796 +BPFP 1.1652 bits/point +EBPFP 1.1652 equivalent bits/point +MSE 845.441251 +---------------------- -------------------------------------------------------- +Time: 5.223s Load: 0.051s, Pack+Encode: 2.633s, Decode+Unpack: 2.539s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 845.4413 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01860187-painting_2.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n01860187-painting_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01944390-deviantart_6.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01944390-deviantart_6.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 513,300B, BPFP=0.9743 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 490,384B, BPFP=0.9308 +⌛️ [2/4] FRONTEND: Frontend time: 2.629s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.537s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10713051 12.39434429 + layer.39.0 82.30322218 1281.78462099 + ------------------------------------------------------------------------------------- + TOTAL 41.20517635 647.08948264 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1003684 +BPFP 0.9525 bits/point +EBPFP 0.9525 equivalent bits/point +MSE 647.089483 +---------------------- -------------------------------------------------------- +Time: 5.235s Load: 0.070s, Pack+Encode: 2.629s, Decode+Unpack: 2.537s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 647.0895 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01944390-deviantart_6.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n01944390-deviantart_6.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01983481-misc_6.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01983481-misc_6.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 638,776B, BPFP=1.2124 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 572,368B, BPFP=1.0864 +⌛️ [2/4] FRONTEND: Frontend time: 2.618s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.518s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10315659 8.41068961 + layer.39.0 236.29731535 1823.07094266 + ------------------------------------------------------------------------------------- + TOTAL 118.20023597 915.74081614 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1211144 +BPFP 1.1494 bits/point +EBPFP 1.1494 equivalent bits/point +MSE 915.740816 +---------------------- -------------------------------------------------------- +Time: 5.207s Load: 0.071s, Pack+Encode: 2.618s, Decode+Unpack: 2.518s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 915.7408 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01983481-misc_6.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n01983481-misc_6.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02051845-cartoon_2.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02051845-cartoon_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 619,560B, BPFP=1.1760 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 626,084B, BPFP=1.1884 +⌛️ [2/4] FRONTEND: Frontend time: 2.628s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.528s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11657756 2.74573098 + layer.39.0 123.57765428 2179.63435374 + ------------------------------------------------------------------------------------- + TOTAL 61.84711592 1091.19004236 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1245644 +BPFP 1.1822 bits/point +EBPFP 1.1822 equivalent bits/point +MSE 1091.190042 +---------------------- -------------------------------------------------------- +Time: 5.227s Load: 0.071s, Pack+Encode: 2.628s, Decode+Unpack: 2.528s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1091.1900 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02051845-cartoon_2.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n02051845-cartoon_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02056570-art_2.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02056570-art_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 570,408B, BPFP=1.0827 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 562,064B, BPFP=1.0668 +⌛️ [2/4] FRONTEND: Frontend time: 2.603s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.518s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09569211 0.79369405 + layer.39.0 33.39981930 1324.54178814 + ------------------------------------------------------------------------------------- + TOTAL 16.74775571 662.66774110 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1132472 +BPFP 1.0748 bits/point +EBPFP 1.0748 equivalent bits/point +MSE 662.667741 +---------------------- -------------------------------------------------------- +Time: 5.181s Load: 0.061s, Pack+Encode: 2.603s, Decode+Unpack: 2.518s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 662.6677 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02056570-art_2.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n02056570-art_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02085620-misc_90.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02085620-misc_90.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 642,408B, BPFP=1.2193 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 704,872B, BPFP=1.3379 +⌛️ [2/4] FRONTEND: Frontend time: 2.620s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.549s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09843166 0.78933766 + layer.39.0 72.76188958 2734.82045675 + ------------------------------------------------------------------------------------- + TOTAL 36.43016062 1367.80489721 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1347280 +BPFP 1.2786 bits/point +EBPFP 1.2786 equivalent bits/point +MSE 1367.804897 +---------------------- -------------------------------------------------------- +Time: 5.219s Load: 0.050s, Pack+Encode: 2.620s, Decode+Unpack: 2.549s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1367.8049 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02085620-misc_90.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n02085620-misc_90.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088094-misc_39.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088094-misc_39.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 655,544B, BPFP=1.2443 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 567,972B, BPFP=1.0781 +⌛️ [2/4] FRONTEND: Frontend time: 2.640s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.545s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09820385 8.92287434 + layer.39.0 12.32374423 1503.36467444 + ------------------------------------------------------------------------------------- + TOTAL 6.21097404 756.14377439 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1223516 +BPFP 1.1612 bits/point +EBPFP 1.1612 equivalent bits/point +MSE 756.143774 +---------------------- -------------------------------------------------------- +Time: 5.256s Load: 0.070s, Pack+Encode: 2.640s, Decode+Unpack: 2.545s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 756.1438 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088094-misc_39.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n02088094-misc_39.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088466-sketch_11.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088466-sketch_11.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 573,812B, BPFP=1.0891 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 620,032B, BPFP=1.1769 +⌛️ [2/4] FRONTEND: Frontend time: 2.616s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.540s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09459993 12.13920144 + layer.39.0 16.33682960 2198.84863946 + ------------------------------------------------------------------------------------- + TOTAL 8.21571477 1105.49392045 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1193844 +BPFP 1.1330 bits/point +EBPFP 1.1330 equivalent bits/point +MSE 1105.493920 +---------------------- -------------------------------------------------------- +Time: 5.225s Load: 0.069s, Pack+Encode: 2.616s, Decode+Unpack: 2.540s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1105.4939 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088466-sketch_11.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n02088466-sketch_11.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02094433-misc_20.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02094433-misc_20.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 646,224B, BPFP=1.2266 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 595,520B, BPFP=1.1303 +⌛️ [2/4] FRONTEND: Frontend time: 2.627s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.549s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09538842 0.75682740 + layer.39.0 94.83275632 1752.05855199 + ------------------------------------------------------------------------------------- + TOTAL 47.46407237 876.40768969 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1241744 +BPFP 1.1785 bits/point +EBPFP 1.1785 equivalent bits/point +MSE 876.407690 +---------------------- -------------------------------------------------------- +Time: 5.247s Load: 0.071s, Pack+Encode: 2.627s, Decode+Unpack: 2.549s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 876.4077 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02094433-misc_20.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n02094433-misc_20.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02097298-misc_9.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02097298-misc_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.055s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 749,412B, BPFP=1.4224 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 631,328B, BPFP=1.1983 +⌛️ [2/4] FRONTEND: Frontend time: 2.636s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.526s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11199322 0.86033519 + layer.39.0 26.16675018 1566.82422255 + ------------------------------------------------------------------------------------- + TOTAL 13.13937170 783.84227887 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1380740 +BPFP 1.3104 bits/point +EBPFP 1.3104 equivalent bits/point +MSE 783.842279 +---------------------- -------------------------------------------------------- +Time: 5.217s Load: 0.055s, Pack+Encode: 2.636s, Decode+Unpack: 2.526s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 783.8423 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02097298-misc_9.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n02097298-misc_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02106662-misc_55.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02106662-misc_55.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 649,100B, BPFP=1.2320 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 625,716B, BPFP=1.1877 +⌛️ [2/4] FRONTEND: Frontend time: 2.616s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.534s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09642073 12.14053105 + layer.39.0 14.86428154 1803.27818270 + ------------------------------------------------------------------------------------- + TOTAL 7.48035113 907.70935687 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1274816 +BPFP 1.2099 bits/point +EBPFP 1.2099 equivalent bits/point +MSE 907.709357 +---------------------- -------------------------------------------------------- +Time: 5.221s Load: 0.070s, Pack+Encode: 2.616s, Decode+Unpack: 2.534s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 907.7094 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02106662-misc_55.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n02106662-misc_55.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02109525-sketch_23.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02109525-sketch_23.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 580,852B, BPFP=1.1025 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 645,372B, BPFP=1.2250 +⌛️ [2/4] FRONTEND: Frontend time: 2.647s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.519s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09568003 12.34612260 + layer.39.0 14.01675815 2266.79178814 + ------------------------------------------------------------------------------------- + TOTAL 7.05621909 1139.56895537 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1226224 +BPFP 1.1637 bits/point +EBPFP 1.1637 equivalent bits/point +MSE 1139.568955 +---------------------- -------------------------------------------------------- +Time: 5.225s Load: 0.059s, Pack+Encode: 2.647s, Decode+Unpack: 2.519s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1139.5690 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02109525-sketch_23.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n02109525-sketch_23.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110185-painting_33.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110185-painting_33.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 618,568B, BPFP=1.1741 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 679,304B, BPFP=1.2894 +⌛️ [2/4] FRONTEND: Frontend time: 2.651s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.544s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09599521 0.77710940 + layer.39.0 22.05506522 2011.57653061 + ------------------------------------------------------------------------------------- + TOTAL 11.07553021 1006.17682000 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1297872 +BPFP 1.2317 bits/point +EBPFP 1.2317 equivalent bits/point +MSE 1006.176820 +---------------------- -------------------------------------------------------- +Time: 5.265s Load: 0.069s, Pack+Encode: 2.651s, Decode+Unpack: 2.544s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1006.1768 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110185-painting_33.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n02110185-painting_33.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110341-misc_162.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110341-misc_162.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 576,960B, BPFP=1.0951 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 585,484B, BPFP=1.1113 +⌛️ [2/4] FRONTEND: Frontend time: 2.638s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.532s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11124049 8.62141832 + layer.39.0 14.33747210 1929.68245870 + ------------------------------------------------------------------------------------- + TOTAL 7.22435629 969.15193851 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1162444 +BPFP 1.1032 bits/point +EBPFP 1.1032 equivalent bits/point +MSE 969.151939 +---------------------- -------------------------------------------------------- +Time: 5.241s Load: 0.071s, Pack+Encode: 2.638s, Decode+Unpack: 2.532s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 969.1519 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110341-misc_162.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n02110341-misc_162.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02165456-tattoo_37.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02165456-tattoo_37.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 697,740B, BPFP=1.3244 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 641,864B, BPFP=1.2183 +⌛️ [2/4] FRONTEND: Frontend time: 2.642s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.529s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09780899 0.79515552 + layer.39.0 88.96013271 1679.61236638 + ------------------------------------------------------------------------------------- + TOTAL 44.52897085 840.20376095 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1339604 +BPFP 1.2713 bits/point +EBPFP 1.2713 equivalent bits/point +MSE 840.203761 +---------------------- -------------------------------------------------------- +Time: 5.242s Load: 0.071s, Pack+Encode: 2.642s, Decode+Unpack: 2.529s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 840.2038 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02165456-tattoo_37.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n02165456-tattoo_37.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02219486-misc_3.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02219486-misc_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 589,380B, BPFP=1.1187 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 548,472B, BPFP=1.0410 +⌛️ [2/4] FRONTEND: Frontend time: 2.625s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.529s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10021695 12.78730393 + layer.39.0 75.73793580 1092.47983479 + ------------------------------------------------------------------------------------- + TOTAL 37.91907638 552.63356936 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1137852 +BPFP 1.0799 bits/point +EBPFP 1.0799 equivalent bits/point +MSE 552.633569 +---------------------- -------------------------------------------------------- +Time: 5.226s Load: 0.071s, Pack+Encode: 2.625s, Decode+Unpack: 2.529s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 552.6336 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02219486-misc_3.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n02219486-misc_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02226429-tattoo_0.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02226429-tattoo_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 626,148B, BPFP=1.1885 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 615,036B, BPFP=1.1674 +⌛️ [2/4] FRONTEND: Frontend time: 2.648s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.538s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09506506 12.13753113 + layer.39.0 201.13660107 2218.95213800 + ------------------------------------------------------------------------------------- + TOTAL 100.61583306 1115.54483456 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1241184 +BPFP 1.1779 bits/point +EBPFP 1.1779 equivalent bits/point +MSE 1115.544835 +---------------------- -------------------------------------------------------- +Time: 5.238s Load: 0.051s, Pack+Encode: 2.648s, Decode+Unpack: 2.538s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1115.5448 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02226429-tattoo_0.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n02226429-tattoo_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02233338-tattoo_5.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02233338-tattoo_5.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 623,472B, BPFP=1.1834 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 689,812B, BPFP=1.3093 +⌛️ [2/4] FRONTEND: Frontend time: 2.645s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.533s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09502332 8.52059133 + layer.39.0 172.43500972 3525.23542274 + ------------------------------------------------------------------------------------- + TOTAL 86.26501652 1766.87800703 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1313284 +BPFP 1.2464 bits/point +EBPFP 1.2464 equivalent bits/point +MSE 1766.878007 +---------------------- -------------------------------------------------------- +Time: 5.228s Load: 0.050s, Pack+Encode: 2.645s, Decode+Unpack: 2.533s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1766.8780 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02233338-tattoo_5.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n02233338-tattoo_5.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02279972-painting_2.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02279972-painting_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 767,844B, BPFP=1.4574 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 682,672B, BPFP=1.2958 +⌛️ [2/4] FRONTEND: Frontend time: 2.642s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.543s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11337867 24.24922938 + layer.39.0 361.17623299 2324.82021380 + ------------------------------------------------------------------------------------- + TOTAL 180.64480583 1174.53472159 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1450516 +BPFP 1.3766 bits/point +EBPFP 1.3766 equivalent bits/point +MSE 1174.534722 +---------------------- -------------------------------------------------------- +Time: 5.255s Load: 0.070s, Pack+Encode: 2.642s, Decode+Unpack: 2.543s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1174.5347 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02279972-painting_2.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n02279972-painting_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02317335-sculpture_0.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02317335-sculpture_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 624,296B, BPFP=1.1850 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 723,804B, BPFP=1.3738 +⌛️ [2/4] FRONTEND: Frontend time: 2.636s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.529s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09546056 0.77825287 + layer.39.0 1163.18707483 2807.48226433 + ------------------------------------------------------------------------------------- + TOTAL 581.64126769 1404.13025860 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1348100 +BPFP 1.2794 bits/point +EBPFP 1.2794 equivalent bits/point +MSE 1404.130259 +---------------------- -------------------------------------------------------- +Time: 5.226s Load: 0.061s, Pack+Encode: 2.636s, Decode+Unpack: 2.529s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1404.1303 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02317335-sculpture_0.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n02317335-sculpture_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02346627-painting_9.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02346627-painting_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 690,872B, BPFP=1.3113 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 679,124B, BPFP=1.2890 +⌛️ [2/4] FRONTEND: Frontend time: 2.667s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.539s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13205896 0.83200118 + layer.39.0 503.01482021 2477.58794947 + ------------------------------------------------------------------------------------- + TOTAL 251.57343959 1239.20997532 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1369996 +BPFP 1.3002 bits/point +EBPFP 1.3002 equivalent bits/point +MSE 1239.209975 +---------------------- -------------------------------------------------------- +Time: 5.258s Load: 0.052s, Pack+Encode: 2.667s, Decode+Unpack: 2.539s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1239.2100 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02346627-painting_9.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n02346627-painting_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02391049-deviantart_0.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02391049-deviantart_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 587,716B, BPFP=1.1155 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 551,808B, BPFP=1.0474 +⌛️ [2/4] FRONTEND: Frontend time: 2.635s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.517s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10116939 0.82657344 + layer.39.0 17.42674737 1803.51712828 + ------------------------------------------------------------------------------------- + TOTAL 8.76395838 902.17185086 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1139524 +BPFP 1.0815 bits/point +EBPFP 1.0815 equivalent bits/point +MSE 902.171851 +---------------------- -------------------------------------------------------- +Time: 5.203s Load: 0.052s, Pack+Encode: 2.635s, Decode+Unpack: 2.517s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 902.1719 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02391049-deviantart_0.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n02391049-deviantart_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02395406-sculpture_31.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02395406-sculpture_31.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 769,540B, BPFP=1.4606 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 508,972B, BPFP=0.9661 +⌛️ [2/4] FRONTEND: Frontend time: 2.654s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.526s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11469608 12.64204571 + layer.39.0 30.55020044 1266.15306122 + ------------------------------------------------------------------------------------- + TOTAL 15.33244826 639.39755347 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1278512 +BPFP 1.2134 bits/point +EBPFP 1.2134 equivalent bits/point +MSE 639.397553 +---------------------- -------------------------------------------------------- +Time: 5.232s Load: 0.052s, Pack+Encode: 2.654s, Decode+Unpack: 2.526s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 639.3976 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02395406-sculpture_31.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n02395406-sculpture_31.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02445715-art_3.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02445715-art_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 627,516B, BPFP=1.1911 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 623,040B, BPFP=1.1826 +⌛️ [2/4] FRONTEND: Frontend time: 2.637s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.540s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09587883 12.71899675 + layer.39.0 77.63827138 1792.02793975 + ------------------------------------------------------------------------------------- + TOTAL 38.86707511 902.37346825 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1250556 +BPFP 1.1868 bits/point +EBPFP 1.1868 equivalent bits/point +MSE 902.373468 +---------------------- -------------------------------------------------------- +Time: 5.228s Load: 0.051s, Pack+Encode: 2.637s, Decode+Unpack: 2.540s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 902.3735 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02445715-art_3.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n02445715-art_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02672831-sculpture_2.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02672831-sculpture_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 711,860B, BPFP=1.3512 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 666,488B, BPFP=1.2650 +⌛️ [2/4] FRONTEND: Frontend time: 2.661s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.531s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11638676 0.85341313 + layer.39.0 42.74346681 1956.72764820 + ------------------------------------------------------------------------------------- + TOTAL 21.42992678 978.79053067 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1378348 +BPFP 1.3081 bits/point +EBPFP 1.3081 equivalent bits/point +MSE 978.790531 +---------------------- -------------------------------------------------------- +Time: 5.273s Load: 0.080s, Pack+Encode: 2.661s, Decode+Unpack: 2.531s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 978.7905 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02672831-sculpture_2.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n02672831-sculpture_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02701002-videogame_1.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02701002-videogame_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 557,392B, BPFP=1.0580 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 643,300B, BPFP=1.2210 +⌛️ [2/4] FRONTEND: Frontend time: 2.629s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.540s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10320827 12.19313920 + layer.39.0 160.61054422 2049.86127308 + ------------------------------------------------------------------------------------- + TOTAL 80.35687624 1031.02720614 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1200692 +BPFP 1.1395 bits/point +EBPFP 1.1395 equivalent bits/point +MSE 1031.027206 +---------------------- -------------------------------------------------------- +Time: 5.219s Load: 0.051s, Pack+Encode: 2.629s, Decode+Unpack: 2.540s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1031.0272 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02701002-videogame_1.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n02701002-videogame_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02749479-misc_35.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02749479-misc_35.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 525,492B, BPFP=0.9974 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 561,636B, BPFP=1.0660 +⌛️ [2/4] FRONTEND: Frontend time: 2.642s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.509s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09764870 8.54899136 + layer.39.0 172.65676628 1559.11309524 + ------------------------------------------------------------------------------------- + TOTAL 86.37720749 783.83104330 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1087128 +BPFP 1.0317 bits/point +EBPFP 1.0317 equivalent bits/point +MSE 783.831043 +---------------------- -------------------------------------------------------- +Time: 5.232s Load: 0.080s, Pack+Encode: 2.642s, Decode+Unpack: 2.509s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 783.8310 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02749479-misc_35.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n02749479-misc_35.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02769748-cartoon_11.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02769748-cartoon_11.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 544,980B, BPFP=1.0344 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 655,972B, BPFP=1.2451 +⌛️ [2/4] FRONTEND: Frontend time: 2.656s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.522s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12263774 12.09859011 + layer.39.0 11.02823964 2162.68999028 + ------------------------------------------------------------------------------------- + TOTAL 5.57543869 1087.39429019 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1200952 +BPFP 1.1398 bits/point +EBPFP 1.1398 equivalent bits/point +MSE 1087.394290 +---------------------- -------------------------------------------------------- +Time: 5.228s Load: 0.050s, Pack+Encode: 2.656s, Decode+Unpack: 2.522s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1087.3943 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02769748-cartoon_11.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n02769748-cartoon_11.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02793495-sketch_11.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02793495-sketch_11.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.054s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 551,364B, BPFP=1.0465 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 512,136B, BPFP=0.9721 +⌛️ [2/4] FRONTEND: Frontend time: 2.617s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.527s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09793751 0.79280350 + layer.39.0 182.75789602 1101.51457726 + ------------------------------------------------------------------------------------- + TOTAL 91.42791676 551.15369038 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1063500 +BPFP 1.0093 bits/point +EBPFP 1.0093 equivalent bits/point +MSE 551.153690 +---------------------- -------------------------------------------------------- +Time: 5.198s Load: 0.054s, Pack+Encode: 2.617s, Decode+Unpack: 2.527s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 551.1537 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02793495-sketch_11.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n02793495-sketch_11.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02797295-misc_13.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02797295-misc_13.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 812,512B, BPFP=1.5422 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 686,040B, BPFP=1.3022 +⌛️ [2/4] FRONTEND: Frontend time: 2.657s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.550s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.17140635 1.54678828 + layer.39.0 172.50999150 2129.39917396 + ------------------------------------------------------------------------------------- + TOTAL 86.34069892 1065.47298112 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1498552 +BPFP 1.4222 bits/point +EBPFP 1.4222 equivalent bits/point +MSE 1065.472981 +---------------------- -------------------------------------------------------- +Time: 5.278s Load: 0.071s, Pack+Encode: 2.657s, Decode+Unpack: 2.550s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1065.4730 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02797295-misc_13.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n02797295-misc_13.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02802426-art_1.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02802426-art_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 758,108B, BPFP=1.4390 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 757,684B, BPFP=1.4381 +⌛️ [2/4] FRONTEND: Frontend time: 2.640s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.543s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.16523854 12.96304437 + layer.39.0 477.65184645 2336.95116618 + ------------------------------------------------------------------------------------- + TOTAL 238.90854250 1174.95710528 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1515792 +BPFP 1.4385 bits/point +EBPFP 1.4385 equivalent bits/point +MSE 1174.957105 +---------------------- -------------------------------------------------------- +Time: 5.235s Load: 0.052s, Pack+Encode: 2.640s, Decode+Unpack: 2.543s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1174.9571 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02802426-art_1.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n02802426-art_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02814860-sticker_8.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02814860-sticker_8.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 651,956B, BPFP=1.2375 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 627,436B, BPFP=1.1909 +⌛️ [2/4] FRONTEND: Frontend time: 2.626s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.525s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12757226 24.49361865 + layer.39.0 19.27598852 1906.39492225 + ------------------------------------------------------------------------------------- + TOTAL 9.70178039 965.44427045 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1279392 +BPFP 1.2142 bits/point +EBPFP 1.2142 equivalent bits/point +MSE 965.444270 +---------------------- -------------------------------------------------------- +Time: 5.213s Load: 0.061s, Pack+Encode: 2.626s, Decode+Unpack: 2.525s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 965.4443 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02814860-sticker_8.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n02814860-sticker_8.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02841315-graphic_4.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02841315-graphic_4.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 690,008B, BPFP=1.3097 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 691,360B, BPFP=1.3123 +⌛️ [2/4] FRONTEND: Frontend time: 2.635s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.544s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11826141 8.54479281 + layer.39.0 55.46440340 3187.07458698 + ------------------------------------------------------------------------------------- + TOTAL 27.79133240 1597.80968989 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1381368 +BPFP 1.3110 bits/point +EBPFP 1.3110 equivalent bits/point +MSE 1597.809690 +---------------------- -------------------------------------------------------- +Time: 5.231s Load: 0.052s, Pack+Encode: 2.635s, Decode+Unpack: 2.544s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1597.8097 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02841315-graphic_4.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n02841315-graphic_4.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02843684-cartoon_9.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02843684-cartoon_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 712,376B, BPFP=1.3521 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 632,476B, BPFP=1.2005 +⌛️ [2/4] FRONTEND: Frontend time: 2.665s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.540s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12386809 8.58007718 + layer.39.0 312.00962707 2585.18707483 + ------------------------------------------------------------------------------------- + TOTAL 156.06674758 1296.88357600 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1344852 +BPFP 1.2763 bits/point +EBPFP 1.2763 equivalent bits/point +MSE 1296.883576 +---------------------- -------------------------------------------------------- +Time: 5.275s Load: 0.071s, Pack+Encode: 2.665s, Decode+Unpack: 2.540s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1296.8836 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02843684-cartoon_9.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n02843684-cartoon_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02883205-sketch_15.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02883205-sketch_15.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 554,840B, BPFP=1.0531 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 582,952B, BPFP=1.1065 +⌛️ [2/4] FRONTEND: Frontend time: 2.698s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.526s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09796664 9.35443430 + layer.39.0 103.64267493 1595.19606414 + ------------------------------------------------------------------------------------- + TOTAL 51.87032078 802.27524922 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1137792 +BPFP 1.0798 bits/point +EBPFP 1.0798 equivalent bits/point +MSE 802.275249 +---------------------- -------------------------------------------------------- +Time: 5.293s Load: 0.070s, Pack+Encode: 2.698s, Decode+Unpack: 2.526s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 802.2752 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02883205-sketch_15.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n02883205-sketch_15.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02906734-graffiti_3.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02906734-graffiti_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 827,152B, BPFP=1.5700 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 708,260B, BPFP=1.3443 +⌛️ [2/4] FRONTEND: Frontend time: 2.674s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.538s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.17339475 12.06776528 + layer.39.0 166.12656402 2708.36054422 + ------------------------------------------------------------------------------------- + TOTAL 83.14997939 1360.21415475 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1535412 +BPFP 1.4572 bits/point +EBPFP 1.4572 equivalent bits/point +MSE 1360.214155 +---------------------- -------------------------------------------------------- +Time: 5.282s Load: 0.070s, Pack+Encode: 2.674s, Decode+Unpack: 2.538s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1360.2142 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02906734-graffiti_3.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n02906734-graffiti_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02909870-sketch_0.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02909870-sketch_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 565,956B, BPFP=1.0742 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 581,592B, BPFP=1.1039 +⌛️ [2/4] FRONTEND: Frontend time: 2.638s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.533s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.15317524 48.74104106 + layer.39.0 167.75886783 1593.03243440 + ------------------------------------------------------------------------------------- + TOTAL 83.95602154 820.88673773 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1147548 +BPFP 1.0891 bits/point +EBPFP 1.0891 equivalent bits/point +MSE 820.886738 +---------------------- -------------------------------------------------------- +Time: 5.222s Load: 0.051s, Pack+Encode: 2.638s, Decode+Unpack: 2.533s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 820.8867 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02909870-sketch_0.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n02909870-sketch_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02939185-painting_0.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02939185-painting_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 600,796B, BPFP=1.1404 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 636,356B, BPFP=1.2079 +⌛️ [2/4] FRONTEND: Frontend time: 2.636s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.539s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09512242 12.31462016 + layer.39.0 131.28711127 2179.47181730 + ------------------------------------------------------------------------------------- + TOTAL 65.69111684 1095.89321873 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1237152 +BPFP 1.1741 bits/point +EBPFP 1.1741 equivalent bits/point +MSE 1095.893219 +---------------------- -------------------------------------------------------- +Time: 5.244s Load: 0.069s, Pack+Encode: 2.636s, Decode+Unpack: 2.539s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1095.8932 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02939185-painting_0.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n02939185-painting_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02948072-misc_10.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02948072-misc_10.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 590,788B, BPFP=1.1214 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 706,668B, BPFP=1.3413 +⌛️ [2/4] FRONTEND: Frontend time: 2.643s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.519s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09566823 8.61430242 + layer.39.0 102.81622783 2495.80903790 + ------------------------------------------------------------------------------------- + TOTAL 51.45594803 1252.21167016 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1297456 +BPFP 1.2313 bits/point +EBPFP 1.2313 equivalent bits/point +MSE 1252.211670 +---------------------- -------------------------------------------------------- +Time: 5.233s Load: 0.071s, Pack+Encode: 2.643s, Decode+Unpack: 2.519s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1252.2117 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02948072-misc_10.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n02948072-misc_10.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02950826-art_1.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02950826-art_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 631,648B, BPFP=1.1989 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 741,304B, BPFP=1.4071 +⌛️ [2/4] FRONTEND: Frontend time: 2.649s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.561s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09506074 0.79930472 + layer.39.0 1071.96149174 3033.19897959 + ------------------------------------------------------------------------------------- + TOTAL 536.02827624 1516.99914216 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1372952 +BPFP 1.3030 bits/point +EBPFP 1.3030 equivalent bits/point +MSE 1516.999142 +---------------------- -------------------------------------------------------- +Time: 5.261s Load: 0.051s, Pack+Encode: 2.649s, Decode+Unpack: 2.561s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1516.9991 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02950826-art_1.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n02950826-art_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02951358-misc_1.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02951358-misc_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 556,648B, BPFP=1.0566 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 442,596B, BPFP=0.8401 +⌛️ [2/4] FRONTEND: Frontend time: 2.631s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.517s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09568294 0.81204345 + layer.39.0 598.97078474 1227.32252187 + ------------------------------------------------------------------------------------- + TOTAL 299.53323384 614.06728266 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 999244 +BPFP 0.9483 bits/point +EBPFP 0.9483 equivalent bits/point +MSE 614.067283 +---------------------- -------------------------------------------------------- +Time: 5.219s Load: 0.071s, Pack+Encode: 2.631s, Decode+Unpack: 2.517s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 614.0673 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02951358-misc_1.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n02951358-misc_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02966193-cartoon_22.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02966193-cartoon_22.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 772,328B, BPFP=1.4659 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 725,236B, BPFP=1.3766 +⌛️ [2/4] FRONTEND: Frontend time: 2.655s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.524s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10376222 0.87854728 + layer.39.0 767.85532070 2235.81972789 + ------------------------------------------------------------------------------------- + TOTAL 383.97954146 1118.34913758 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1497564 +BPFP 1.4212 bits/point +EBPFP 1.4212 equivalent bits/point +MSE 1118.349138 +---------------------- -------------------------------------------------------- +Time: 5.248s Load: 0.070s, Pack+Encode: 2.655s, Decode+Unpack: 2.524s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1118.3491 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02966193-cartoon_22.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n02966193-cartoon_22.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02980441-graphic_3.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02980441-graphic_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 543,660B, BPFP=1.0319 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 443,236B, BPFP=0.8413 +⌛️ [2/4] FRONTEND: Frontend time: 2.632s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.505s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09509088 8.55959309 + layer.39.0 13.13791359 743.59092566 + ------------------------------------------------------------------------------------- + TOTAL 6.61650224 376.07525937 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 986896 +BPFP 0.9366 bits/point +EBPFP 0.9366 equivalent bits/point +MSE 376.075259 +---------------------- -------------------------------------------------------- +Time: 5.208s Load: 0.070s, Pack+Encode: 2.632s, Decode+Unpack: 2.505s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 376.0753 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02980441-graphic_3.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n02980441-graphic_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03124170-painting_15.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03124170-painting_15.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.074s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 699,132B, BPFP=1.3270 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 784,100B, BPFP=1.4883 +⌛️ [2/4] FRONTEND: Frontend time: 2.686s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.567s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10783903 12.79173215 + layer.39.0 326.57091229 3260.56656948 + ------------------------------------------------------------------------------------- + TOTAL 163.33937566 1636.67915082 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1483232 +BPFP 1.4076 bits/point +EBPFP 1.4076 equivalent bits/point +MSE 1636.679151 +---------------------- -------------------------------------------------------- +Time: 5.327s Load: 0.074s, Pack+Encode: 2.686s, Decode+Unpack: 2.567s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1636.6792 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03124170-painting_15.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n03124170-painting_15.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03345487-toy_17.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03345487-toy_17.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 601,400B, BPFP=1.1415 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 594,460B, BPFP=1.1283 +⌛️ [2/4] FRONTEND: Frontend time: 2.845s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.512s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10662318 0.84540293 + layer.39.0 198.63900024 2619.33163265 + ------------------------------------------------------------------------------------- + TOTAL 99.37281171 1310.08851779 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1195860 +BPFP 1.1349 bits/point +EBPFP 1.1349 equivalent bits/point +MSE 1310.088518 +---------------------- -------------------------------------------------------- +Time: 5.426s Load: 0.070s, Pack+Encode: 2.845s, Decode+Unpack: 2.512s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1310.0885 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03345487-toy_17.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n03345487-toy_17.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03372029-sketch_14.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03372029-sketch_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 690,672B, BPFP=1.3110 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 701,656B, BPFP=1.3318 +⌛️ [2/4] FRONTEND: Frontend time: 2.665s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.536s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12162214 11.99934896 + layer.39.0 228.06095117 2476.15208941 + ------------------------------------------------------------------------------------- + TOTAL 114.09128665 1244.07571918 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1392328 +BPFP 1.3214 bits/point +EBPFP 1.3214 equivalent bits/point +MSE 1244.075719 +---------------------- -------------------------------------------------------- +Time: 5.251s Load: 0.050s, Pack+Encode: 2.665s, Decode+Unpack: 2.536s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1244.0757 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03372029-sketch_14.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n03372029-sketch_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03424325-misc_4.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03424325-misc_4.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.056s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 683,436B, BPFP=1.2972 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 702,616B, BPFP=1.3336 +⌛️ [2/4] FRONTEND: Frontend time: 2.691s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.538s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10761499 13.09719502 + layer.39.0 21.03287666 2496.30004859 + ------------------------------------------------------------------------------------- + TOTAL 10.57024582 1254.69862180 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1386052 +BPFP 1.3154 bits/point +EBPFP 1.3154 equivalent bits/point +MSE 1254.698622 +---------------------- -------------------------------------------------------- +Time: 5.285s Load: 0.056s, Pack+Encode: 2.691s, Decode+Unpack: 2.538s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1254.6986 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03424325-misc_4.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n03424325-misc_4.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03467068-sketch_17.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03467068-sketch_17.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 602,428B, BPFP=1.1435 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 668,708B, BPFP=1.2693 +⌛️ [2/4] FRONTEND: Frontend time: 2.640s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.527s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09564773 12.43165106 + layer.39.0 208.14688107 2425.13726919 + ------------------------------------------------------------------------------------- + TOTAL 104.12126440 1218.78446013 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1271136 +BPFP 1.2064 bits/point +EBPFP 1.2064 equivalent bits/point +MSE 1218.784460 +---------------------- -------------------------------------------------------- +Time: 5.227s Load: 0.060s, Pack+Encode: 2.640s, Decode+Unpack: 2.527s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1218.7845 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03467068-sketch_17.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n03467068-sketch_17.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03481172-sketch_9.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03481172-sketch_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 531,756B, BPFP=1.0093 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 614,344B, BPFP=1.1661 +⌛️ [2/4] FRONTEND: Frontend time: 2.639s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.524s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14641065 12.68489014 + layer.39.0 516.28267736 2618.73104956 + ------------------------------------------------------------------------------------- + TOTAL 258.21454400 1315.70796985 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1146100 +BPFP 1.0877 bits/point +EBPFP 1.0877 equivalent bits/point +MSE 1315.707970 +---------------------- -------------------------------------------------------- +Time: 5.222s Load: 0.059s, Pack+Encode: 2.639s, Decode+Unpack: 2.524s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1315.7080 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03481172-sketch_9.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n03481172-sketch_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03494278-deviantart_3.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03494278-deviantart_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.078s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 436,984B, BPFP=0.8294 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 543,680B, BPFP=1.0319 +⌛️ [2/4] FRONTEND: Frontend time: 2.636s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.515s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09714438 12.38585797 + layer.39.0 11.38600982 1110.64723032 + ------------------------------------------------------------------------------------- + TOTAL 5.74157710 561.51654415 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 980664 +BPFP 0.9307 bits/point +EBPFP 0.9307 equivalent bits/point +MSE 561.516544 +---------------------- -------------------------------------------------------- +Time: 5.229s Load: 0.078s, Pack+Encode: 2.636s, Decode+Unpack: 2.515s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 561.5165 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03494278-deviantart_3.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n03494278-deviantart_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03495258-painting_3.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03495258-painting_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 712,700B, BPFP=1.3528 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 672,940B, BPFP=1.2773 +⌛️ [2/4] FRONTEND: Frontend time: 2.665s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.530s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10398556 0.81427589 + layer.39.0 359.17207240 2425.21525753 + ------------------------------------------------------------------------------------- + TOTAL 179.63802898 1213.01476671 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1385640 +BPFP 1.3150 bits/point +EBPFP 1.3150 equivalent bits/point +MSE 1213.014767 +---------------------- -------------------------------------------------------- +Time: 5.247s Load: 0.051s, Pack+Encode: 2.665s, Decode+Unpack: 2.530s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1213.0148 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03495258-painting_3.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n03495258-painting_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03498962-sketch_8.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03498962-sketch_8.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 685,140B, BPFP=1.3005 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 633,064B, BPFP=1.2016 +⌛️ [2/4] FRONTEND: Frontend time: 2.651s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.532s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.16074808 8.38616926 + layer.39.0 476.99061589 2396.58284742 + ------------------------------------------------------------------------------------- + TOTAL 238.57568198 1202.48450834 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1318204 +BPFP 1.2510 bits/point +EBPFP 1.2510 equivalent bits/point +MSE 1202.484508 +---------------------- -------------------------------------------------------- +Time: 5.262s Load: 0.080s, Pack+Encode: 2.651s, Decode+Unpack: 2.532s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1202.4845 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03498962-sketch_8.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n03498962-sketch_8.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03602883-misc_12.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03602883-misc_12.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.067s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 430,416B, BPFP=0.8170 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 510,108B, BPFP=0.9682 +⌛️ [2/4] FRONTEND: Frontend time: 2.624s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.517s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.09080038 8.54423572 + layer.39.0 100.93773536 1936.00291545 + ------------------------------------------------------------------------------------- + TOTAL 54.51426787 972.27357559 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 940524 +BPFP 0.8926 bits/point +EBPFP 0.8926 equivalent bits/point +MSE 972.273576 +---------------------- -------------------------------------------------------- +Time: 5.208s Load: 0.067s, Pack+Encode: 2.624s, Decode+Unpack: 2.517s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 972.2736 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03602883-misc_12.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n03602883-misc_12.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03630383-toy_1.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03630383-toy_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.074s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 543,256B, BPFP=1.0311 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 650,408B, BPFP=1.2345 +⌛️ [2/4] FRONTEND: Frontend time: 2.725s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.535s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09574974 0.80312995 + layer.39.0 14.66923857 1933.60641399 + ------------------------------------------------------------------------------------- + TOTAL 7.38249415 967.20477197 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1193664 +BPFP 1.1328 bits/point +EBPFP 1.1328 equivalent bits/point +MSE 967.204772 +---------------------- -------------------------------------------------------- +Time: 5.334s Load: 0.074s, Pack+Encode: 2.725s, Decode+Unpack: 2.535s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 967.2048 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03630383-toy_1.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n03630383-toy_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03649909-toy_22.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03649909-toy_22.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 583,336B, BPFP=1.1072 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 414,760B, BPFP=0.7872 +⌛️ [2/4] FRONTEND: Frontend time: 2.616s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.511s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09878858 1.15196508 + layer.39.0 29.68475348 707.48876336 + ------------------------------------------------------------------------------------- + TOTAL 14.89177103 354.32036422 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 998096 +BPFP 0.9472 bits/point +EBPFP 0.9472 equivalent bits/point +MSE 354.320364 +---------------------- -------------------------------------------------------- +Time: 5.195s Load: 0.069s, Pack+Encode: 2.616s, Decode+Unpack: 2.511s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 354.3204 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03649909-toy_22.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n03649909-toy_22.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03676483-sculpture_3.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03676483-sculpture_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 577,296B, BPFP=1.0958 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 717,952B, BPFP=1.3627 +⌛️ [2/4] FRONTEND: Frontend time: 2.658s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.536s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09491264 0.82352809 + layer.39.0 32.22669916 3187.42395530 + ------------------------------------------------------------------------------------- + TOTAL 16.16080590 1594.12374169 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1295248 +BPFP 1.2292 bits/point +EBPFP 1.2292 equivalent bits/point +MSE 1594.123742 +---------------------- -------------------------------------------------------- +Time: 5.263s Load: 0.070s, Pack+Encode: 2.658s, Decode+Unpack: 2.536s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1594.1237 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03676483-sculpture_3.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n03676483-sculpture_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03710193-sketch_14.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03710193-sketch_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 545,240B, BPFP=1.0349 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 593,492B, BPFP=1.1265 +⌛️ [2/4] FRONTEND: Frontend time: 2.668s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.539s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.47394152 23.97348571 + layer.39.0 335.99814747 1994.25048591 + ------------------------------------------------------------------------------------- + TOTAL 168.23604450 1009.11198581 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1138732 +BPFP 1.0807 bits/point +EBPFP 1.0807 equivalent bits/point +MSE 1009.111986 +---------------------- -------------------------------------------------------- +Time: 5.277s Load: 0.070s, Pack+Encode: 2.668s, Decode+Unpack: 2.539s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1009.1120 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03710193-sketch_14.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n03710193-sketch_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03773504-graphic_4.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03773504-graphic_4.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.056s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 495,092B, BPFP=0.9397 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 471,000B, BPFP=0.8940 +⌛️ [2/4] FRONTEND: Frontend time: 2.622s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.515s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09681199 12.63488426 + layer.39.0 18.83313593 910.83309038 + ------------------------------------------------------------------------------------- + TOTAL 9.46497396 461.73398732 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 966092 +BPFP 0.9169 bits/point +EBPFP 0.9169 equivalent bits/point +MSE 461.733987 +---------------------- -------------------------------------------------------- +Time: 5.192s Load: 0.056s, Pack+Encode: 2.622s, Decode+Unpack: 2.515s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 461.7340 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03773504-graphic_4.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n03773504-graphic_4.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03775071-painting_1.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03775071-painting_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.057s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 662,260B, BPFP=1.2570 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 577,184B, BPFP=1.0955 +⌛️ [2/4] FRONTEND: Frontend time: 2.651s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.531s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11048905 12.42166052 + layer.39.0 386.73560496 1768.43634597 + ------------------------------------------------------------------------------------- + TOTAL 193.42304701 890.42900324 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1239444 +BPFP 1.1763 bits/point +EBPFP 1.1763 equivalent bits/point +MSE 890.429003 +---------------------- -------------------------------------------------------- +Time: 5.238s Load: 0.057s, Pack+Encode: 2.651s, Decode+Unpack: 2.531s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 890.4290 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03775071-painting_1.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n03775071-painting_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03888257-cartoon_30.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03888257-cartoon_30.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 644,360B, BPFP=1.2230 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 661,200B, BPFP=1.2550 +⌛️ [2/4] FRONTEND: Frontend time: 2.650s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.518s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13203045 24.81360848 + layer.39.0 375.96832483 1975.09244412 + ------------------------------------------------------------------------------------- + TOTAL 188.05017764 999.95302630 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1305560 +BPFP 1.2390 bits/point +EBPFP 1.2390 equivalent bits/point +MSE 999.953026 +---------------------- -------------------------------------------------------- +Time: 5.220s Load: 0.051s, Pack+Encode: 2.650s, Decode+Unpack: 2.518s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 999.9530 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03888257-cartoon_30.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n03888257-cartoon_30.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03930630-toy_2.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03930630-toy_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 530,308B, BPFP=1.0066 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 560,744B, BPFP=1.0643 +⌛️ [2/4] FRONTEND: Frontend time: 2.626s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.513s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09699417 1.13516209 + layer.39.0 46.17573949 1740.68719631 + ------------------------------------------------------------------------------------- + TOTAL 23.13636683 870.91117920 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1091052 +BPFP 1.0355 bits/point +EBPFP 1.0355 equivalent bits/point +MSE 870.911179 +---------------------- -------------------------------------------------------- +Time: 5.189s Load: 0.050s, Pack+Encode: 2.626s, Decode+Unpack: 2.513s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 870.9112 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03930630-toy_2.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n03930630-toy_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04086273-sticker_5.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04086273-sticker_5.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 588,708B, BPFP=1.1174 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 472,312B, BPFP=0.8965 +⌛️ [2/4] FRONTEND: Frontend time: 2.613s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.520s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10161624 13.12311046 + layer.39.0 24.98063198 782.73293246 + ------------------------------------------------------------------------------------- + TOTAL 12.54112411 397.92802146 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1061020 +BPFP 1.0070 bits/point +EBPFP 1.0070 equivalent bits/point +MSE 397.928021 +---------------------- -------------------------------------------------------- +Time: 5.204s Load: 0.071s, Pack+Encode: 2.613s, Decode+Unpack: 2.520s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 397.9280 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04086273-sticker_5.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n04086273-sticker_5.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04118538-sculpture_0.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04118538-sculpture_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 641,552B, BPFP=1.2177 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 661,624B, BPFP=1.2558 +⌛️ [2/4] FRONTEND: Frontend time: 2.640s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.540s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09846411 12.12329457 + layer.39.0 11.87055944 2017.41277940 + ------------------------------------------------------------------------------------- + TOTAL 5.98451177 1014.76803699 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1303176 +BPFP 1.2368 bits/point +EBPFP 1.2368 equivalent bits/point +MSE 1014.768037 +---------------------- -------------------------------------------------------- +Time: 5.231s Load: 0.051s, Pack+Encode: 2.640s, Decode+Unpack: 2.540s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1014.7680 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04118538-sculpture_0.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n04118538-sculpture_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04133789-cartoon_7.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04133789-cartoon_7.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 751,832B, BPFP=1.4270 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 707,404B, BPFP=1.3427 +⌛️ [2/4] FRONTEND: Frontend time: 2.645s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.542s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13739287 8.61517743 + layer.39.0 370.52532799 2222.81195335 + ------------------------------------------------------------------------------------- + TOTAL 185.33136043 1115.71356539 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1459236 +BPFP 1.3849 bits/point +EBPFP 1.3849 equivalent bits/point +MSE 1115.713565 +---------------------- -------------------------------------------------------- +Time: 5.249s Load: 0.062s, Pack+Encode: 2.645s, Decode+Unpack: 2.542s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1115.7136 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04133789-cartoon_7.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n04133789-cartoon_7.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04141076-cartoon_14.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04141076-cartoon_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 553,776B, BPFP=1.0511 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 664,900B, BPFP=1.2620 +⌛️ [2/4] FRONTEND: Frontend time: 2.622s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.527s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11960477 12.14808825 + layer.39.0 53.25505649 2339.78911565 + ------------------------------------------------------------------------------------- + TOTAL 26.68733063 1175.96860195 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1218676 +BPFP 1.1566 bits/point +EBPFP 1.1566 equivalent bits/point +MSE 1175.968602 +---------------------- -------------------------------------------------------- +Time: 5.208s Load: 0.060s, Pack+Encode: 2.622s, Decode+Unpack: 2.527s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1175.9686 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04141076-cartoon_14.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n04141076-cartoon_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04146614-misc_4.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04146614-misc_4.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 615,480B, BPFP=1.1682 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 593,572B, BPFP=1.1266 +⌛️ [2/4] FRONTEND: Frontend time: 2.633s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.538s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10047569 8.57403748 + layer.39.0 167.29959305 1974.59548105 + ------------------------------------------------------------------------------------- + TOTAL 83.70003437 991.58475927 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1209052 +BPFP 1.1474 bits/point +EBPFP 1.1474 equivalent bits/point +MSE 991.584759 +---------------------- -------------------------------------------------------- +Time: 5.232s Load: 0.061s, Pack+Encode: 2.633s, Decode+Unpack: 2.538s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 991.5848 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04146614-misc_4.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n04146614-misc_4.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04147183-art_9.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04147183-art_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 611,496B, BPFP=1.1607 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 533,128B, BPFP=1.0119 +⌛️ [2/4] FRONTEND: Frontend time: 2.634s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.520s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11332939 1.97567467 + layer.39.0 22.95352360 1339.65925656 + ------------------------------------------------------------------------------------- + TOTAL 11.53342649 670.81746562 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1144624 +BPFP 1.0863 bits/point +EBPFP 1.0863 equivalent bits/point +MSE 670.817466 +---------------------- -------------------------------------------------------- +Time: 5.216s Load: 0.062s, Pack+Encode: 2.634s, Decode+Unpack: 2.520s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 670.8175 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04147183-art_9.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n04147183-art_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04192698-videogame_16.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04192698-videogame_16.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 643,224B, BPFP=1.2209 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 635,088B, BPFP=1.2054 +⌛️ [2/4] FRONTEND: Frontend time: 2.635s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.519s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09706018 11.97941911 + layer.39.0 404.66927843 2186.98177843 + ------------------------------------------------------------------------------------- + TOTAL 202.38316930 1099.48059877 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1278312 +BPFP 1.2132 bits/point +EBPFP 1.2132 equivalent bits/point +MSE 1099.480599 +---------------------- -------------------------------------------------------- +Time: 5.205s Load: 0.052s, Pack+Encode: 2.635s, Decode+Unpack: 2.519s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1099.4806 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04192698-videogame_16.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n04192698-videogame_16.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04254680-deviantart_17.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04254680-deviantart_17.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 556,072B, BPFP=1.0555 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 631,884B, BPFP=1.1994 +⌛️ [2/4] FRONTEND: Frontend time: 2.637s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.513s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10685510 0.89768461 + layer.39.0 151.81593173 1947.80940233 + ------------------------------------------------------------------------------------- + TOTAL 75.96139341 974.35354347 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1187956 +BPFP 1.1274 bits/point +EBPFP 1.1274 equivalent bits/point +MSE 974.353543 +---------------------- -------------------------------------------------------- +Time: 5.201s Load: 0.052s, Pack+Encode: 2.637s, Decode+Unpack: 2.513s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 974.3535 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04254680-deviantart_17.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n04254680-deviantart_17.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04266014-painting_13.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04266014-painting_13.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 593,196B, BPFP=1.1259 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 490,152B, BPFP=0.9303 +⌛️ [2/4] FRONTEND: Frontend time: 2.629s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.523s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09568562 8.61398544 + layer.39.0 29.62437363 1261.10629252 + ------------------------------------------------------------------------------------- + TOTAL 14.86002963 634.86013898 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1083348 +BPFP 1.0281 bits/point +EBPFP 1.0281 equivalent bits/point +MSE 634.860139 +---------------------- -------------------------------------------------------- +Time: 5.204s Load: 0.051s, Pack+Encode: 2.629s, Decode+Unpack: 2.523s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 634.8601 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04266014-painting_13.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n04266014-painting_13.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04310018-art_3.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04310018-art_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 614,420B, BPFP=1.1662 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 566,768B, BPFP=1.0758 +⌛️ [2/4] FRONTEND: Frontend time: 2.618s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.529s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13375617 8.32969376 + layer.39.0 75.24515610 1626.17662779 + ------------------------------------------------------------------------------------- + TOTAL 37.68945614 817.25316078 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1181188 +BPFP 1.1210 bits/point +EBPFP 1.1210 equivalent bits/point +MSE 817.253161 +---------------------- -------------------------------------------------------- +Time: 5.199s Load: 0.052s, Pack+Encode: 2.618s, Decode+Unpack: 2.529s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 817.2532 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04310018-art_3.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n04310018-art_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04347754-sculpture_0.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04347754-sculpture_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 767,388B, BPFP=1.4566 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 600,572B, BPFP=1.1399 +⌛️ [2/4] FRONTEND: Frontend time: 2.641s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.550s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14257451 12.24800986 + layer.39.0 394.23636419 1828.56559767 + ------------------------------------------------------------------------------------- + TOTAL 197.18946935 920.40680377 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1367960 +BPFP 1.2982 bits/point +EBPFP 1.2982 equivalent bits/point +MSE 920.406804 +---------------------- -------------------------------------------------------- +Time: 5.241s Load: 0.050s, Pack+Encode: 2.641s, Decode+Unpack: 2.550s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 920.4068 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04347754-sculpture_0.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n04347754-sculpture_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04409515-deviantart_1.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04409515-deviantart_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 570,504B, BPFP=1.0829 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 539,824B, BPFP=1.0246 +⌛️ [2/4] FRONTEND: Frontend time: 2.632s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.526s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09627266 12.40149816 + layer.39.0 9.33068077 1143.04956268 + ------------------------------------------------------------------------------------- + TOTAL 4.71347671 577.72553042 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1110328 +BPFP 1.0537 bits/point +EBPFP 1.0537 equivalent bits/point +MSE 577.725530 +---------------------- -------------------------------------------------------- +Time: 5.227s Load: 0.069s, Pack+Encode: 2.632s, Decode+Unpack: 2.526s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 577.7255 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04409515-deviantart_1.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n04409515-deviantart_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04487394-deviantart_8.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04487394-deviantart_8.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 642,096B, BPFP=1.2188 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 610,760B, BPFP=1.1593 +⌛️ [2/4] FRONTEND: Frontend time: 2.632s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.519s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09911632 12.32126913 + layer.39.0 99.63155977 2146.92152575 + ------------------------------------------------------------------------------------- + TOTAL 49.86533804 1079.62139744 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1252856 +BPFP 1.1890 bits/point +EBPFP 1.1890 equivalent bits/point +MSE 1079.621397 +---------------------- -------------------------------------------------------- +Time: 5.221s Load: 0.070s, Pack+Encode: 2.632s, Decode+Unpack: 2.519s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1079.6214 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04487394-deviantart_8.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n04487394-deviantart_8.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04522168-painting_32.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04522168-painting_32.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 589,168B, BPFP=1.1183 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 542,184B, BPFP=1.0291 +⌛️ [2/4] FRONTEND: Frontend time: 2.635s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.528s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11740584 8.62751306 + layer.39.0 10.95138066 1707.95821186 + ------------------------------------------------------------------------------------- + TOTAL 5.53439325 858.29286246 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1131352 +BPFP 1.0737 bits/point +EBPFP 1.0737 equivalent bits/point +MSE 858.292862 +---------------------- -------------------------------------------------------- +Time: 5.214s Load: 0.051s, Pack+Encode: 2.635s, Decode+Unpack: 2.528s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 858.2929 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04522168-painting_32.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n04522168-painting_32.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04591713-painting_14.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04591713-painting_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 730,808B, BPFP=1.3871 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 690,720B, BPFP=1.3110 +⌛️ [2/4] FRONTEND: Frontend time: 2.649s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.551s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11212821 12.43754460 + layer.39.0 165.22564383 2345.49902818 + ------------------------------------------------------------------------------------- + TOTAL 82.66888602 1178.96828639 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1421528 +BPFP 1.3491 bits/point +EBPFP 1.3491 equivalent bits/point +MSE 1178.968286 +---------------------- -------------------------------------------------------- +Time: 5.271s Load: 0.071s, Pack+Encode: 2.649s, Decode+Unpack: 2.551s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1178.9683 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04591713-painting_14.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n04591713-painting_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07693725-sketch_15.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07693725-sketch_15.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 654,488B, BPFP=1.2423 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 597,104B, BPFP=1.1334 +⌛️ [2/4] FRONTEND: Frontend time: 2.640s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.522s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10569874 8.41893867 + layer.39.0 214.96065658 1905.66812439 + ------------------------------------------------------------------------------------- + TOTAL 107.53317766 957.04353153 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1251592 +BPFP 1.1878 bits/point +EBPFP 1.1878 equivalent bits/point +MSE 957.043532 +---------------------- -------------------------------------------------------- +Time: 5.223s Load: 0.061s, Pack+Encode: 2.640s, Decode+Unpack: 2.522s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 957.0435 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07693725-sketch_15.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n07693725-sketch_15.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07695742-videogame_1.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07695742-videogame_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 716,784B, BPFP=1.3605 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 653,332B, BPFP=1.2401 +⌛️ [2/4] FRONTEND: Frontend time: 2.631s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.541s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12460778 12.15786432 + layer.39.0 438.29433916 2833.85422741 + ------------------------------------------------------------------------------------- + TOTAL 219.20947347 1423.00604586 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1370116 +BPFP 1.3003 bits/point +EBPFP 1.3003 equivalent bits/point +MSE 1423.006046 +---------------------- -------------------------------------------------------- +Time: 5.223s Load: 0.050s, Pack+Encode: 2.631s, Decode+Unpack: 2.541s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1423.0060 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07695742-videogame_1.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n07695742-videogame_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697313-deviantart_7.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697313-deviantart_7.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 585,536B, BPFP=1.1114 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 505,912B, BPFP=0.9603 +⌛️ [2/4] FRONTEND: Frontend time: 2.635s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.521s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09520741 0.78729610 + layer.39.0 14.69109212 1286.33965015 + ------------------------------------------------------------------------------------- + TOTAL 7.39314977 643.56347312 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1091448 +BPFP 1.0358 bits/point +EBPFP 1.0358 equivalent bits/point +MSE 643.563473 +---------------------- -------------------------------------------------------- +Time: 5.207s Load: 0.052s, Pack+Encode: 2.635s, Decode+Unpack: 2.521s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 643.5635 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697313-deviantart_7.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n07697313-deviantart_7.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697537-deviantart_16.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697537-deviantart_16.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 642,272B, BPFP=1.2191 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 661,836B, BPFP=1.2562 +⌛️ [2/4] FRONTEND: Frontend time: 2.641s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.531s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09755328 12.75921139 + layer.39.0 90.32537658 2080.28668610 + ------------------------------------------------------------------------------------- + TOTAL 45.21146493 1046.52294874 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1304108 +BPFP 1.2377 bits/point +EBPFP 1.2377 equivalent bits/point +MSE 1046.522949 +---------------------- -------------------------------------------------------- +Time: 5.222s Load: 0.051s, Pack+Encode: 2.641s, Decode+Unpack: 2.531s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1046.5229 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697537-deviantart_16.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n07697537-deviantart_16.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714571-deviantart_0.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714571-deviantart_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 618,492B, BPFP=1.1739 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 676,476B, BPFP=1.2840 +⌛️ [2/4] FRONTEND: Frontend time: 2.627s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.542s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09528512 0.77762212 + layer.39.0 45.81401467 2392.71088435 + ------------------------------------------------------------------------------------- + TOTAL 22.95464989 1196.74425323 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1294968 +BPFP 1.2290 bits/point +EBPFP 1.2290 equivalent bits/point +MSE 1196.744253 +---------------------- -------------------------------------------------------- +Time: 5.230s Load: 0.061s, Pack+Encode: 2.627s, Decode+Unpack: 2.542s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1196.7443 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714571-deviantart_0.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n07714571-deviantart_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714990-toy_14.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714990-toy_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 628,968B, BPFP=1.1938 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 683,976B, BPFP=1.2982 +⌛️ [2/4] FRONTEND: Frontend time: 2.642s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.538s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09793257 12.17509699 + layer.39.0 322.50334062 2324.66083576 + ------------------------------------------------------------------------------------- + TOTAL 161.30063660 1168.41796638 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1312944 +BPFP 1.2460 bits/point +EBPFP 1.2460 equivalent bits/point +MSE 1168.417966 +---------------------- -------------------------------------------------------- +Time: 5.251s Load: 0.070s, Pack+Encode: 2.642s, Decode+Unpack: 2.538s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1168.4180 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714990-toy_14.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n07714990-toy_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07718472-cartoon_1.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07718472-cartoon_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.056s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 457,112B, BPFP=0.8676 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 474,672B, BPFP=0.9010 +⌛️ [2/4] FRONTEND: Frontend time: 2.603s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.530s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11235230 12.46477447 + layer.39.0 14.49942963 984.55496842 + ------------------------------------------------------------------------------------- + TOTAL 7.30589096 498.50987144 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 931784 +BPFP 0.8843 bits/point +EBPFP 0.8843 equivalent bits/point +MSE 498.509871 +---------------------- -------------------------------------------------------- +Time: 5.189s Load: 0.056s, Pack+Encode: 2.603s, Decode+Unpack: 2.530s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 498.5099 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07718472-cartoon_1.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n07718472-cartoon_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07742313-deviantart_8.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07742313-deviantart_8.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 483,976B, BPFP=0.9186 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 510,312B, BPFP=0.9686 +⌛️ [2/4] FRONTEND: Frontend time: 2.593s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.512s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09669835 12.55306654 + layer.39.0 8.77690150 1009.91982507 + ------------------------------------------------------------------------------------- + TOTAL 4.43679992 511.23644581 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 994288 +BPFP 0.9436 bits/point +EBPFP 0.9436 equivalent bits/point +MSE 511.236446 +---------------------- -------------------------------------------------------- +Time: 5.167s Load: 0.062s, Pack+Encode: 2.593s, Decode+Unpack: 2.512s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 511.2364 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07742313-deviantart_8.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n07742313-deviantart_8.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07749582-sticker_0.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07749582-sticker_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 658,576B, BPFP=1.2500 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 654,752B, BPFP=1.2428 +⌛️ [2/4] FRONTEND: Frontend time: 2.627s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.531s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09550123 12.35102819 + layer.39.0 34.64631545 1888.71793003 + ------------------------------------------------------------------------------------- + TOTAL 17.37090834 950.53447911 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1313328 +BPFP 1.2464 bits/point +EBPFP 1.2464 equivalent bits/point +MSE 950.534479 +---------------------- -------------------------------------------------------- +Time: 5.210s Load: 0.052s, Pack+Encode: 2.627s, Decode+Unpack: 2.531s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 950.5345 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07749582-sticker_0.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n07749582-sticker_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07753275-painting_2.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07753275-painting_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 799,296B, BPFP=1.5171 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 640,796B, BPFP=1.2163 +⌛️ [2/4] FRONTEND: Frontend time: 2.647s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.546s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10429548 1.23616655 + layer.39.0 540.43106171 1849.33831390 + ------------------------------------------------------------------------------------- + TOTAL 270.26767859 925.28724022 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1440092 +BPFP 1.3667 bits/point +EBPFP 1.3667 equivalent bits/point +MSE 925.287240 +---------------------- -------------------------------------------------------- +Time: 5.265s Load: 0.072s, Pack+Encode: 2.647s, Decode+Unpack: 2.546s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 925.2872 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07753275-painting_2.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n07753275-painting_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07768694-painting_12.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07768694-painting_12.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 694,612B, BPFP=1.3184 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 637,672B, BPFP=1.2104 +⌛️ [2/4] FRONTEND: Frontend time: 2.643s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.539s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09821300 0.81589175 + layer.39.0 635.68343052 2127.11078717 + ------------------------------------------------------------------------------------- + TOTAL 317.89082176 1063.96333946 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1332284 +BPFP 1.2644 bits/point +EBPFP 1.2644 equivalent bits/point +MSE 1063.963339 +---------------------- -------------------------------------------------------- +Time: 5.253s Load: 0.071s, Pack+Encode: 2.643s, Decode+Unpack: 2.539s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1063.9633 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07768694-painting_12.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n07768694-painting_12.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07920052-painting_1.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07920052-painting_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 646,244B, BPFP=1.2266 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 620,680B, BPFP=1.1781 +⌛️ [2/4] FRONTEND: Frontend time: 2.635s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.526s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09582097 12.33455285 + layer.39.0 9.59182155 1014.39522595 + ------------------------------------------------------------------------------------- + TOTAL 4.84382126 513.36488940 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1266924 +BPFP 1.2024 bits/point +EBPFP 1.2024 equivalent bits/point +MSE 513.364889 +---------------------- -------------------------------------------------------- +Time: 5.212s Load: 0.052s, Pack+Encode: 2.635s, Decode+Unpack: 2.526s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 513.3649 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07920052-painting_1.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n07920052-painting_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09472597-painting_15.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09472597-painting_15.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 548,656B, BPFP=1.0414 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 475,876B, BPFP=0.9033 +⌛️ [2/4] FRONTEND: Frontend time: 2.615s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.522s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09164813 12.60009908 + layer.39.0 9.11265014 775.96956997 + ------------------------------------------------------------------------------------- + TOTAL 4.60214913 394.28483453 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1024532 +BPFP 0.9723 bits/point +EBPFP 0.9723 equivalent bits/point +MSE 394.284835 +---------------------- -------------------------------------------------------- +Time: 5.209s Load: 0.071s, Pack+Encode: 2.615s, Decode+Unpack: 2.522s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 394.2848 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09472597-painting_15.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n09472597-painting_15.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09835506-videogame_3.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09835506-videogame_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 602,280B, BPFP=1.1432 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 647,176B, BPFP=1.2284 +⌛️ [2/4] FRONTEND: Frontend time: 2.630s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.511s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09585661 12.50807159 + layer.39.0 12.34450164 1943.86260933 + ------------------------------------------------------------------------------------- + TOTAL 6.22017912 978.18534046 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1249456 +BPFP 1.1858 bits/point +EBPFP 1.1858 equivalent bits/point +MSE 978.185340 +---------------------- -------------------------------------------------------- +Time: 5.202s Load: 0.061s, Pack+Encode: 2.630s, Decode+Unpack: 2.511s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 978.1853 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09835506-videogame_3.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n09835506-videogame_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n12267677-misc_105.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n12267677-misc_105.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.053s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 514,600B, BPFP=0.9768 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 641,876B, BPFP=1.2183 +⌛️ [2/4] FRONTEND: Frontend time: 2.640s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.525s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10166193 24.22053420 + layer.39.0 219.41089650 2568.08333333 + ------------------------------------------------------------------------------------- + TOTAL 109.75627921 1296.15193376 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1156476 +BPFP 1.0975 bits/point +EBPFP 1.0975 equivalent bits/point +MSE 1296.151934 +---------------------- -------------------------------------------------------- +Time: 5.218s Load: 0.053s, Pack+Encode: 2.640s, Decode+Unpack: 2.525s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1296.1519 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n12267677-misc_105.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kr/n12267677-misc_105.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 1.1763 bits/point +Avg EBPFP 1.1763 equivalent bits/point +Avg MSE 981.231811 +Avg Time 5.241s +------------------------ ---------------------------- diff --git a/lambda0.02/elic-featurecoding-8bit-individual/cls_in1kval/dtufc_elic-featurecoding_dinov3-total_individual.log b/lambda0.02/elic-featurecoding-8bit-individual/cls_in1kval/dtufc_elic-featurecoding_dinov3-total_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..acbd8d665ed8a4fec43860b1e95f464866606fdd --- /dev/null +++ b/lambda0.02/elic-featurecoding-8bit-individual/cls_in1kval/dtufc_elic-featurecoding_dinov3-total_individual.log @@ -0,0 +1,9344 @@ +Experiment: dtufc_elic-featurecoding_dinov3-total_individual +Log file: output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/dtufc_elic-featurecoding_dinov3-total_individual.log +DTUFCCodecConfig: + arch: elic-featurecoding + handler: dinov3-total + checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.02_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/elic_hybrid/elic2022-official_lambda0.02_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 520 +Loaded elic-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.9' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.19' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.29' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.39' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json +Loaded per-key mappings: model=dinov3-total + Keys: ['layer.9', 'layer.19', 'layer.29', 'layer.39'] +---------------- ------------------------------------------------------------------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +Checkpoint codec_weights/elic_hybrid/elic2022-official_lambda0.02_epochs600_lr0.0001_bs60_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-DINOv3/imagenet1k-val +Output output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval +---------------- ------------------------------------------------------------------------------------------------------------------- +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02825657-ILSVRC2012_val_00001103.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02825657-ILSVRC2012_val_00001103.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 561,936B, BPFP=1.0666 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 586,672B, BPFP=1.1136 +⌛️ [2/4] FRONTEND: Frontend time: 2.930s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.613s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10264289 1.18527130 + layer.39.0 9.47367932 1582.37973761 + ------------------------------------------------------------------------------------- + TOTAL 4.78816110 791.78250445 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1148608 +BPFP 1.0901 bits/point +EBPFP 1.0901 equivalent bits/point +MSE 791.782504 +---------------------- -------------------------------------------------------- +Time: 5.614s Load: 0.071s, Pack+Encode: 2.930s, Decode+Unpack: 2.613s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 791.7825 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02825657-ILSVRC2012_val_00001103.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n02825657-ILSVRC2012_val_00001103.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02834397-ILSVRC2012_val_00001252.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02834397-ILSVRC2012_val_00001252.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 768,112B, BPFP=1.4579 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 689,780B, BPFP=1.3093 +⌛️ [2/4] FRONTEND: Frontend time: 2.664s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.530s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14789204 12.57391980 + layer.39.0 415.43227648 2393.85058309 + ------------------------------------------------------------------------------------- + TOTAL 207.79008426 1203.21225145 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1457892 +BPFP 1.3836 bits/point +EBPFP 1.3836 equivalent bits/point +MSE 1203.212251 +---------------------- -------------------------------------------------------- +Time: 5.264s Load: 0.069s, Pack+Encode: 2.664s, Decode+Unpack: 2.530s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1203.2123 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02834397-ILSVRC2012_val_00001252.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n02834397-ILSVRC2012_val_00001252.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02840245-ILSVRC2012_val_00003446.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02840245-ILSVRC2012_val_00003446.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 561,996B, BPFP=1.0667 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 562,132B, BPFP=1.0670 +⌛️ [2/4] FRONTEND: Frontend time: 2.617s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.525s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10761288 12.43176685 + layer.39.0 28.71820525 1117.67359086 + ------------------------------------------------------------------------------------- + TOTAL 14.41290906 565.05267886 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1124128 +BPFP 1.0668 bits/point +EBPFP 1.0668 equivalent bits/point +MSE 565.052679 +---------------------- -------------------------------------------------------- +Time: 5.194s Load: 0.052s, Pack+Encode: 2.617s, Decode+Unpack: 2.525s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 565.0527 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02840245-ILSVRC2012_val_00003446.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n02840245-ILSVRC2012_val_00003446.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02843684-ILSVRC2012_val_00000514.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02843684-ILSVRC2012_val_00000514.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 2.503s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 625,892B, BPFP=1.1880 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 590,640B, BPFP=1.1211 +⌛️ [2/4] FRONTEND: Frontend time: 2.643s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.521s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11482661 13.02456306 + layer.39.0 84.54469600 1949.48979592 + ------------------------------------------------------------------------------------- + TOTAL 42.32976130 981.25717949 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1216532 +BPFP 1.1545 bits/point +EBPFP 1.1545 equivalent bits/point +MSE 981.257179 +---------------------- -------------------------------------------------------- +Time: 7.667s Load: 2.503s, Pack+Encode: 2.643s, Decode+Unpack: 2.521s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 981.2572 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02843684-ILSVRC2012_val_00000514.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n02843684-ILSVRC2012_val_00000514.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02859443-ILSVRC2012_val_00000193.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02859443-ILSVRC2012_val_00000193.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.056s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 423,112B, BPFP=0.8031 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 385,540B, BPFP=0.7318 +⌛️ [2/4] FRONTEND: Frontend time: 2.601s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.487s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11417333 12.45339073 + layer.39.0 9.67809406 666.54172741 + ------------------------------------------------------------------------------------- + TOTAL 4.89613370 339.49755907 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 808652 +BPFP 0.7674 bits/point +EBPFP 0.7674 equivalent bits/point +MSE 339.497559 +---------------------- -------------------------------------------------------- +Time: 5.144s Load: 0.056s, Pack+Encode: 2.601s, Decode+Unpack: 2.487s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 339.4976 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02859443-ILSVRC2012_val_00000193.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n02859443-ILSVRC2012_val_00000193.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02860847-ILSVRC2012_val_00000601.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02860847-ILSVRC2012_val_00000601.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 697,652B, BPFP=1.3242 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 554,364B, BPFP=1.0522 +⌛️ [2/4] FRONTEND: Frontend time: 2.644s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.531s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12653054 32.66741831 + layer.39.0 266.35249636 1478.55138484 + ------------------------------------------------------------------------------------- + TOTAL 133.23951345 755.60940157 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1252016 +BPFP 1.1882 bits/point +EBPFP 1.1882 equivalent bits/point +MSE 755.609402 +---------------------- -------------------------------------------------------- +Time: 5.235s Load: 0.059s, Pack+Encode: 2.644s, Decode+Unpack: 2.531s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 755.6094 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02860847-ILSVRC2012_val_00000601.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n02860847-ILSVRC2012_val_00000601.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02865351-ILSVRC2012_val_00000763.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02865351-ILSVRC2012_val_00000763.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.081s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 525,760B, BPFP=0.9979 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 626,160B, BPFP=1.1885 +⌛️ [2/4] FRONTEND: Frontend time: 2.646s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.513s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09467571 12.47698672 + layer.39.0 15.47581086 2657.97910593 + ------------------------------------------------------------------------------------- + TOTAL 7.78524328 1335.22804632 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1151920 +BPFP 1.0932 bits/point +EBPFP 1.0932 equivalent bits/point +MSE 1335.228046 +---------------------- -------------------------------------------------------- +Time: 5.239s Load: 0.081s, Pack+Encode: 2.646s, Decode+Unpack: 2.513s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1335.2280 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02865351-ILSVRC2012_val_00000763.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n02865351-ILSVRC2012_val_00000763.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02869837-ILSVRC2012_val_00000906.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02869837-ILSVRC2012_val_00000906.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 675,004B, BPFP=1.2812 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 725,920B, BPFP=1.3779 +⌛️ [2/4] FRONTEND: Frontend time: 2.638s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.540s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09659988 0.79418957 + layer.39.0 16.39405483 2118.34669582 + ------------------------------------------------------------------------------------- + TOTAL 8.24532736 1059.57044270 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1400924 +BPFP 1.3295 bits/point +EBPFP 1.3295 equivalent bits/point +MSE 1059.570443 +---------------------- -------------------------------------------------------- +Time: 5.249s Load: 0.070s, Pack+Encode: 2.638s, Decode+Unpack: 2.540s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1059.5704 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02869837-ILSVRC2012_val_00000906.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n02869837-ILSVRC2012_val_00000906.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02870880-ILSVRC2012_val_00003274.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02870880-ILSVRC2012_val_00003274.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 715,620B, BPFP=1.3583 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 622,752B, BPFP=1.1820 +⌛️ [2/4] FRONTEND: Frontend time: 2.649s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.531s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10254154 0.85406957 + layer.39.0 9.36513093 1783.97351798 + ------------------------------------------------------------------------------------- + TOTAL 4.73383623 892.41379377 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1338372 +BPFP 1.2702 bits/point +EBPFP 1.2702 equivalent bits/point +MSE 892.413794 +---------------------- -------------------------------------------------------- +Time: 5.231s Load: 0.051s, Pack+Encode: 2.649s, Decode+Unpack: 2.531s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 892.4138 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02870880-ILSVRC2012_val_00003274.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n02870880-ILSVRC2012_val_00003274.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02871525-ILSVRC2012_val_00000879.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02871525-ILSVRC2012_val_00000879.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 743,968B, BPFP=1.4121 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 673,664B, BPFP=1.2787 +⌛️ [2/4] FRONTEND: Frontend time: 2.641s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.536s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.17072899 10.44777146 + layer.39.0 20.29403547 1910.91994655 + ------------------------------------------------------------------------------------- + TOTAL 10.23238223 960.68385901 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1417632 +BPFP 1.3454 bits/point +EBPFP 1.3454 equivalent bits/point +MSE 960.683859 +---------------------- -------------------------------------------------------- +Time: 5.228s Load: 0.051s, Pack+Encode: 2.641s, Decode+Unpack: 2.536s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 960.6839 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02871525-ILSVRC2012_val_00000879.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n02871525-ILSVRC2012_val_00000879.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02877765-ILSVRC2012_val_00000634.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02877765-ILSVRC2012_val_00000634.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 624,368B, BPFP=1.1851 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 622,892B, BPFP=1.1823 +⌛️ [2/4] FRONTEND: Frontend time: 2.718s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.534s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10908128 12.00859071 + layer.39.0 364.97770894 2178.87414966 + ------------------------------------------------------------------------------------- + TOTAL 182.54339511 1095.44137019 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1247260 +BPFP 1.1837 bits/point +EBPFP 1.1837 equivalent bits/point +MSE 1095.441370 +---------------------- -------------------------------------------------------- +Time: 5.332s Load: 0.080s, Pack+Encode: 2.718s, Decode+Unpack: 2.534s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1095.4414 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02877765-ILSVRC2012_val_00000634.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n02877765-ILSVRC2012_val_00000634.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02879718-ILSVRC2012_val_00001354.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02879718-ILSVRC2012_val_00001354.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 698,060B, BPFP=1.3250 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 675,068B, BPFP=1.2813 +⌛️ [2/4] FRONTEND: Frontend time: 2.706s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.535s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10948122 12.88925838 + layer.39.0 55.92460444 2378.45724004 + ------------------------------------------------------------------------------------- + TOTAL 28.01704283 1195.67324921 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1373128 +BPFP 1.3032 bits/point +EBPFP 1.3032 equivalent bits/point +MSE 1195.673249 +---------------------- -------------------------------------------------------- +Time: 5.310s Load: 0.070s, Pack+Encode: 2.706s, Decode+Unpack: 2.535s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1195.6732 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02879718-ILSVRC2012_val_00001354.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n02879718-ILSVRC2012_val_00001354.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02883205-ILSVRC2012_val_00000126.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02883205-ILSVRC2012_val_00000126.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 489,420B, BPFP=0.9290 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 616,948B, BPFP=1.1710 +⌛️ [2/4] FRONTEND: Frontend time: 2.635s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.520s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.06711708 12.63684972 + layer.39.0 7.82069686 2236.90038873 + ------------------------------------------------------------------------------------- + TOTAL 7.94390697 1124.76861922 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1106368 +BPFP 1.0500 bits/point +EBPFP 1.0500 equivalent bits/point +MSE 1124.768619 +---------------------- -------------------------------------------------------- +Time: 5.214s Load: 0.059s, Pack+Encode: 2.635s, Decode+Unpack: 2.520s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1124.7686 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02883205-ILSVRC2012_val_00000126.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n02883205-ILSVRC2012_val_00000126.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892201-ILSVRC2012_val_00001145.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892201-ILSVRC2012_val_00001145.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 735,520B, BPFP=1.3961 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 621,464B, BPFP=1.1796 +⌛️ [2/4] FRONTEND: Frontend time: 2.635s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.535s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11297333 8.50917247 + layer.39.0 15.09638643 1968.02016521 + ------------------------------------------------------------------------------------- + TOTAL 7.60467988 988.26466884 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1356984 +BPFP 1.2878 bits/point +EBPFP 1.2878 equivalent bits/point +MSE 988.264669 +---------------------- -------------------------------------------------------- +Time: 5.222s Load: 0.052s, Pack+Encode: 2.635s, Decode+Unpack: 2.535s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 988.2647 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892201-ILSVRC2012_val_00001145.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n02892201-ILSVRC2012_val_00001145.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892767-ILSVRC2012_val_00000808.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892767-ILSVRC2012_val_00000808.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 619,440B, BPFP=1.1757 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 743,844B, BPFP=1.4119 +⌛️ [2/4] FRONTEND: Frontend time: 2.631s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.523s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09598007 12.11859588 + layer.39.0 31.15013059 2974.67930029 + ------------------------------------------------------------------------------------- + TOTAL 15.62305533 1493.39894808 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1363284 +BPFP 1.2938 bits/point +EBPFP 1.2938 equivalent bits/point +MSE 1493.398948 +---------------------- -------------------------------------------------------- +Time: 5.214s Load: 0.061s, Pack+Encode: 2.631s, Decode+Unpack: 2.523s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1493.3989 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892767-ILSVRC2012_val_00000808.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n02892767-ILSVRC2012_val_00000808.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02895154-ILSVRC2012_val_00000080.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02895154-ILSVRC2012_val_00000080.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.089s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 639,084B, BPFP=1.2130 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 713,188B, BPFP=1.3537 +⌛️ [2/4] FRONTEND: Frontend time: 2.647s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.524s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09530723 12.25822913 + layer.39.0 971.40427600 2329.01482021 + ------------------------------------------------------------------------------------- + TOTAL 485.74979162 1170.63652467 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1352272 +BPFP 1.2834 bits/point +EBPFP 1.2834 equivalent bits/point +MSE 1170.636525 +---------------------- -------------------------------------------------------- +Time: 5.260s Load: 0.089s, Pack+Encode: 2.647s, Decode+Unpack: 2.524s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1170.6365 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02895154-ILSVRC2012_val_00000080.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n02895154-ILSVRC2012_val_00000080.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02906734-ILSVRC2012_val_00002937.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02906734-ILSVRC2012_val_00002937.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 667,440B, BPFP=1.2669 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 538,968B, BPFP=1.0230 +⌛️ [2/4] FRONTEND: Frontend time: 2.627s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.527s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09767962 8.53198361 + layer.39.0 32.09536716 1189.82215743 + ------------------------------------------------------------------------------------- + TOTAL 16.09652339 599.17707052 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1206408 +BPFP 1.1449 bits/point +EBPFP 1.1449 equivalent bits/point +MSE 599.177071 +---------------------- -------------------------------------------------------- +Time: 5.204s Load: 0.050s, Pack+Encode: 2.627s, Decode+Unpack: 2.527s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 599.1771 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02906734-ILSVRC2012_val_00002937.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n02906734-ILSVRC2012_val_00002937.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02910353-ILSVRC2012_val_00000558.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02910353-ILSVRC2012_val_00000558.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 562,636B, BPFP=1.0679 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 592,484B, BPFP=1.1246 +⌛️ [2/4] FRONTEND: Frontend time: 2.623s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.514s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11017090 12.12639794 + layer.39.0 483.40066205 2163.99757046 + ------------------------------------------------------------------------------------- + TOTAL 241.75541648 1088.06198420 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1155120 +BPFP 1.0963 bits/point +EBPFP 1.0963 equivalent bits/point +MSE 1088.061984 +---------------------- -------------------------------------------------------- +Time: 5.188s Load: 0.051s, Pack+Encode: 2.623s, Decode+Unpack: 2.514s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1088.0620 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02910353-ILSVRC2012_val_00000558.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n02910353-ILSVRC2012_val_00000558.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02916936-ILSVRC2012_val_00000366.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02916936-ILSVRC2012_val_00000366.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.092s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 463,356B, BPFP=0.8795 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 613,032B, BPFP=1.1636 +⌛️ [2/4] FRONTEND: Frontend time: 2.623s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.523s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10657579 12.05664062 + layer.39.0 435.18944363 2539.39261419 + ------------------------------------------------------------------------------------- + TOTAL 217.64800971 1275.72462741 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1076388 +BPFP 1.0215 bits/point +EBPFP 1.0215 equivalent bits/point +MSE 1275.724627 +---------------------- -------------------------------------------------------- +Time: 5.237s Load: 0.092s, Pack+Encode: 2.623s, Decode+Unpack: 2.523s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1275.7246 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02916936-ILSVRC2012_val_00000366.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n02916936-ILSVRC2012_val_00000366.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02917067-ILSVRC2012_val_00000562.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02917067-ILSVRC2012_val_00000562.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 704,356B, BPFP=1.3369 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 606,212B, BPFP=1.1506 +⌛️ [2/4] FRONTEND: Frontend time: 2.655s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.539s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10760244 8.87107572 + layer.39.0 37.55795979 1937.00085034 + ------------------------------------------------------------------------------------- + TOTAL 18.83278111 972.93596303 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1310568 +BPFP 1.2438 bits/point +EBPFP 1.2438 equivalent bits/point +MSE 972.935963 +---------------------- -------------------------------------------------------- +Time: 5.252s Load: 0.059s, Pack+Encode: 2.655s, Decode+Unpack: 2.539s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 972.9360 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02917067-ILSVRC2012_val_00000562.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n02917067-ILSVRC2012_val_00000562.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02930766-ILSVRC2012_val_00000056.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02930766-ILSVRC2012_val_00000056.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 680,592B, BPFP=1.2918 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 674,804B, BPFP=1.2808 +⌛️ [2/4] FRONTEND: Frontend time: 2.655s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.522s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10591127 12.86935416 + layer.39.0 18.32421875 1564.68889699 + ------------------------------------------------------------------------------------- + TOTAL 9.21506501 788.77912557 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1355396 +BPFP 1.2863 bits/point +EBPFP 1.2863 equivalent bits/point +MSE 788.779126 +---------------------- -------------------------------------------------------- +Time: 5.248s Load: 0.071s, Pack+Encode: 2.655s, Decode+Unpack: 2.522s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 788.7791 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02930766-ILSVRC2012_val_00000056.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n02930766-ILSVRC2012_val_00000056.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02939185-ILSVRC2012_val_00000302.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02939185-ILSVRC2012_val_00000302.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.068s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 655,556B, BPFP=1.2443 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 608,128B, BPFP=1.1543 +⌛️ [2/4] FRONTEND: Frontend time: 2.670s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.525s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09694758 26.28810397 + layer.39.0 25.52453269 2089.66205053 + ------------------------------------------------------------------------------------- + TOTAL 12.81074014 1057.97507725 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1263684 +BPFP 1.1993 bits/point +EBPFP 1.1993 equivalent bits/point +MSE 1057.975077 +---------------------- -------------------------------------------------------- +Time: 5.263s Load: 0.068s, Pack+Encode: 2.670s, Decode+Unpack: 2.525s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1057.9751 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02939185-ILSVRC2012_val_00000302.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n02939185-ILSVRC2012_val_00000302.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02950826-ILSVRC2012_val_00000392.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02950826-ILSVRC2012_val_00000392.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 651,224B, BPFP=1.2361 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 602,532B, BPFP=1.1437 +⌛️ [2/4] FRONTEND: Frontend time: 2.646s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.545s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10873010 45.40484162 + layer.39.0 707.96944849 2311.48688047 + ------------------------------------------------------------------------------------- + TOTAL 354.03908930 1178.44586105 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1253756 +BPFP 1.1899 bits/point +EBPFP 1.1899 equivalent bits/point +MSE 1178.445861 +---------------------- -------------------------------------------------------- +Time: 5.242s Load: 0.052s, Pack+Encode: 2.646s, Decode+Unpack: 2.545s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1178.4459 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02950826-ILSVRC2012_val_00000392.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n02950826-ILSVRC2012_val_00000392.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951358-ILSVRC2012_val_00000347.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951358-ILSVRC2012_val_00000347.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 648,344B, BPFP=1.2306 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 533,776B, BPFP=1.0131 +⌛️ [2/4] FRONTEND: Frontend time: 2.610s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.517s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12200860 51.62623375 + layer.39.0 237.66299198 1249.73044218 + ------------------------------------------------------------------------------------- + TOTAL 118.89250029 650.67833796 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1182120 +BPFP 1.1219 bits/point +EBPFP 1.1219 equivalent bits/point +MSE 650.678338 +---------------------- -------------------------------------------------------- +Time: 5.197s Load: 0.070s, Pack+Encode: 2.610s, Decode+Unpack: 2.517s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 650.6783 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951358-ILSVRC2012_val_00000347.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n02951358-ILSVRC2012_val_00000347.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951585-ILSVRC2012_val_00000101.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951585-ILSVRC2012_val_00000101.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.092s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 367,040B, BPFP=0.6967 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 538,596B, BPFP=1.0223 +⌛️ [2/4] FRONTEND: Frontend time: 2.643s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.527s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.07385432 12.38459100 + layer.39.0 181.90962099 1439.12682216 + ------------------------------------------------------------------------------------- + TOTAL 94.99173765 725.75570658 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 905636 +BPFP 0.8595 bits/point +EBPFP 0.8595 equivalent bits/point +MSE 725.755707 +---------------------- -------------------------------------------------------- +Time: 5.262s Load: 0.092s, Pack+Encode: 2.643s, Decode+Unpack: 2.527s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 725.7557 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951585-ILSVRC2012_val_00000101.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n02951585-ILSVRC2012_val_00000101.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02963159-ILSVRC2012_val_00000061.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02963159-ILSVRC2012_val_00000061.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 647,468B, BPFP=1.2289 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 635,064B, BPFP=1.2054 +⌛️ [2/4] FRONTEND: Frontend time: 2.651s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.527s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11232698 0.79588159 + layer.39.0 24.77479842 2266.95213800 + ------------------------------------------------------------------------------------- + TOTAL 12.44356270 1133.87400980 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1282532 +BPFP 1.2172 bits/point +EBPFP 1.2172 equivalent bits/point +MSE 1133.874010 +---------------------- -------------------------------------------------------- +Time: 5.228s Load: 0.050s, Pack+Encode: 2.651s, Decode+Unpack: 2.527s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1133.8740 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02963159-ILSVRC2012_val_00000061.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n02963159-ILSVRC2012_val_00000061.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02965783-ILSVRC2012_val_00000213.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02965783-ILSVRC2012_val_00000213.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 584,940B, BPFP=1.1103 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 633,192B, BPFP=1.2018 +⌛️ [2/4] FRONTEND: Frontend time: 2.637s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.527s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09516161 0.76652463 + layer.39.0 223.32294704 1659.70505345 + ------------------------------------------------------------------------------------- + TOTAL 111.70905432 830.23578904 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1218132 +BPFP 1.1561 bits/point +EBPFP 1.1561 equivalent bits/point +MSE 830.235789 +---------------------- -------------------------------------------------------- +Time: 5.243s Load: 0.080s, Pack+Encode: 2.637s, Decode+Unpack: 2.527s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 830.2358 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02965783-ILSVRC2012_val_00000213.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n02965783-ILSVRC2012_val_00000213.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966193-ILSVRC2012_val_00000074.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966193-ILSVRC2012_val_00000074.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 752,316B, BPFP=1.4280 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 780,248B, BPFP=1.4810 +⌛️ [2/4] FRONTEND: Frontend time: 2.658s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.542s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12190965 1.51949708 + layer.39.0 378.75431244 2271.29761905 + ------------------------------------------------------------------------------------- + TOTAL 189.43811104 1136.40855807 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1532564 +BPFP 1.4545 bits/point +EBPFP 1.4545 equivalent bits/point +MSE 1136.408558 +---------------------- -------------------------------------------------------- +Time: 5.271s Load: 0.071s, Pack+Encode: 2.658s, Decode+Unpack: 2.542s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1136.4086 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966193-ILSVRC2012_val_00000074.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n02966193-ILSVRC2012_val_00000074.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966687-ILSVRC2012_val_00001041.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966687-ILSVRC2012_val_00001041.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 640,100B, BPFP=1.2150 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 685,888B, BPFP=1.3019 +⌛️ [2/4] FRONTEND: Frontend time: 2.649s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.527s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12487827 12.18041636 + layer.39.0 254.07423773 2524.84475219 + ------------------------------------------------------------------------------------- + TOTAL 127.09955800 1268.51258427 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1325988 +BPFP 1.2584 bits/point +EBPFP 1.2584 equivalent bits/point +MSE 1268.512584 +---------------------- -------------------------------------------------------- +Time: 5.244s Load: 0.069s, Pack+Encode: 2.649s, Decode+Unpack: 2.527s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1268.5126 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966687-ILSVRC2012_val_00001041.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n02966687-ILSVRC2012_val_00001041.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02971356-ILSVRC2012_val_00000019.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02971356-ILSVRC2012_val_00000019.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 543,560B, BPFP=1.0317 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 477,760B, BPFP=0.9068 +⌛️ [2/4] FRONTEND: Frontend time: 2.620s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.510s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09754465 0.79464695 + layer.39.0 24.51746044 928.37463557 + ------------------------------------------------------------------------------------- + TOTAL 12.30750255 464.58464126 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1021320 +BPFP 0.9693 bits/point +EBPFP 0.9693 equivalent bits/point +MSE 464.584641 +---------------------- -------------------------------------------------------- +Time: 5.179s Load: 0.050s, Pack+Encode: 2.620s, Decode+Unpack: 2.510s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 464.5846 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02971356-ILSVRC2012_val_00000019.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n02971356-ILSVRC2012_val_00000019.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02978881-ILSVRC2012_val_00000353.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02978881-ILSVRC2012_val_00000353.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 656,096B, BPFP=1.2453 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 720,328B, BPFP=1.3672 +⌛️ [2/4] FRONTEND: Frontend time: 2.640s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.525s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09975241 0.82684896 + layer.39.0 226.62124939 2726.46185617 + ------------------------------------------------------------------------------------- + TOTAL 113.36050090 1363.64435256 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1376424 +BPFP 1.3063 bits/point +EBPFP 1.3063 equivalent bits/point +MSE 1363.644353 +---------------------- -------------------------------------------------------- +Time: 5.218s Load: 0.052s, Pack+Encode: 2.640s, Decode+Unpack: 2.525s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1363.6444 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02978881-ILSVRC2012_val_00000353.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n02978881-ILSVRC2012_val_00000353.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02980441-ILSVRC2012_val_00000122.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02980441-ILSVRC2012_val_00000122.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 603,736B, BPFP=1.1459 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 570,644B, BPFP=1.0831 +⌛️ [2/4] FRONTEND: Frontend time: 2.630s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.512s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10186533 12.53268400 + layer.39.0 8.25151846 1640.18172983 + ------------------------------------------------------------------------------------- + TOTAL 4.17669190 826.35720692 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1174380 +BPFP 1.1145 bits/point +EBPFP 1.1145 equivalent bits/point +MSE 826.357207 +---------------------- -------------------------------------------------------- +Time: 5.201s Load: 0.059s, Pack+Encode: 2.630s, Decode+Unpack: 2.512s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 826.3572 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02980441-ILSVRC2012_val_00000122.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n02980441-ILSVRC2012_val_00000122.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02988304-ILSVRC2012_val_00003491.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02988304-ILSVRC2012_val_00003491.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.079s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 602,020B, BPFP=1.1427 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 543,512B, BPFP=1.0316 +⌛️ [2/4] FRONTEND: Frontend time: 2.636s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.508s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10176498 8.45469661 + layer.39.0 516.16180758 1556.34086492 + ------------------------------------------------------------------------------------- + TOTAL 258.13178628 782.39778076 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1145532 +BPFP 1.0872 bits/point +EBPFP 1.0872 equivalent bits/point +MSE 782.397781 +---------------------- -------------------------------------------------------- +Time: 5.223s Load: 0.079s, Pack+Encode: 2.636s, Decode+Unpack: 2.508s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 782.3978 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02988304-ILSVRC2012_val_00003491.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n02988304-ILSVRC2012_val_00003491.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992211-ILSVRC2012_val_00000108.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992211-ILSVRC2012_val_00000108.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 684,312B, BPFP=1.2989 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 620,592B, BPFP=1.1779 +⌛️ [2/4] FRONTEND: Frontend time: 2.660s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.517s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10107529 12.53487439 + layer.39.0 89.13089923 2390.06802721 + ------------------------------------------------------------------------------------- + TOTAL 44.61598726 1201.30145080 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1304904 +BPFP 1.2384 bits/point +EBPFP 1.2384 equivalent bits/point +MSE 1201.301451 +---------------------- -------------------------------------------------------- +Time: 5.227s Load: 0.051s, Pack+Encode: 2.660s, Decode+Unpack: 2.517s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1201.3015 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992211-ILSVRC2012_val_00000108.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n02992211-ILSVRC2012_val_00000108.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992529-ILSVRC2012_val_00000089.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992529-ILSVRC2012_val_00000089.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 619,484B, BPFP=1.1758 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 710,532B, BPFP=1.3486 +⌛️ [2/4] FRONTEND: Frontend time: 2.638s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.522s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11197385 12.37056039 + layer.39.0 964.25631681 2598.15864917 + ------------------------------------------------------------------------------------- + TOTAL 482.18414533 1305.26460478 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1330016 +BPFP 1.2622 bits/point +EBPFP 1.2622 equivalent bits/point +MSE 1305.264605 +---------------------- -------------------------------------------------------- +Time: 5.229s Load: 0.069s, Pack+Encode: 2.638s, Decode+Unpack: 2.522s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1305.2646 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992529-ILSVRC2012_val_00000089.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n02992529-ILSVRC2012_val_00000089.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02999410-ILSVRC2012_val_00000376.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02999410-ILSVRC2012_val_00000376.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 655,824B, BPFP=1.2448 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 651,268B, BPFP=1.2362 +⌛️ [2/4] FRONTEND: Frontend time: 2.657s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.559s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10398186 12.22855169 + layer.39.0 145.78410471 2568.63265306 + ------------------------------------------------------------------------------------- + TOTAL 72.94404329 1290.43060237 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1307092 +BPFP 1.2405 bits/point +EBPFP 1.2405 equivalent bits/point +MSE 1290.430602 +---------------------- -------------------------------------------------------- +Time: 5.266s Load: 0.050s, Pack+Encode: 2.657s, Decode+Unpack: 2.559s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1290.4306 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02999410-ILSVRC2012_val_00000376.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n02999410-ILSVRC2012_val_00000376.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000134-ILSVRC2012_val_00001094.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000134-ILSVRC2012_val_00001094.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 609,068B, BPFP=1.1561 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 730,668B, BPFP=1.3869 +⌛️ [2/4] FRONTEND: Frontend time: 2.632s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.528s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09696872 0.78363547 + layer.39.0 22.81329530 2456.41763848 + ------------------------------------------------------------------------------------- + TOTAL 11.45513201 1228.60063698 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1339736 +BPFP 1.2715 bits/point +EBPFP 1.2715 equivalent bits/point +MSE 1228.600637 +---------------------- -------------------------------------------------------- +Time: 5.231s Load: 0.071s, Pack+Encode: 2.632s, Decode+Unpack: 2.528s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1228.6006 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000134-ILSVRC2012_val_00001094.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03000134-ILSVRC2012_val_00001094.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000247-ILSVRC2012_val_00002280.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000247-ILSVRC2012_val_00002280.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.068s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 886,872B, BPFP=1.6834 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 525,136B, BPFP=0.9968 +⌛️ [2/4] FRONTEND: Frontend time: 2.642s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.536s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.29135144 60.88534454 + layer.39.0 428.26293732 1587.73700194 + ------------------------------------------------------------------------------------- + TOTAL 214.27714438 824.31117324 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1412008 +BPFP 1.3401 bits/point +EBPFP 1.3401 equivalent bits/point +MSE 824.311173 +---------------------- -------------------------------------------------------- +Time: 5.247s Load: 0.068s, Pack+Encode: 2.642s, Decode+Unpack: 2.536s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 824.3112 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000247-ILSVRC2012_val_00002280.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03000247-ILSVRC2012_val_00002280.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000684-ILSVRC2012_val_00000537.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000684-ILSVRC2012_val_00000537.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.049s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 757,528B, BPFP=1.4378 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 682,956B, BPFP=1.2963 +⌛️ [2/4] FRONTEND: Frontend time: 2.688s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.525s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13150742 1.26083543 + layer.39.0 55.24585459 2028.31122449 + ------------------------------------------------------------------------------------- + TOTAL 27.68868101 1014.78602996 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1440484 +BPFP 1.3671 bits/point +EBPFP 1.3671 equivalent bits/point +MSE 1014.786030 +---------------------- -------------------------------------------------------- +Time: 5.262s Load: 0.049s, Pack+Encode: 2.688s, Decode+Unpack: 2.525s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1014.7860 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000684-ILSVRC2012_val_00000537.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03000684-ILSVRC2012_val_00000537.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03014705-ILSVRC2012_val_00001168.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03014705-ILSVRC2012_val_00001168.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 571,464B, BPFP=1.0847 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 617,620B, BPFP=1.1723 +⌛️ [2/4] FRONTEND: Frontend time: 2.633s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.524s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09787338 12.25792069 + layer.39.0 322.89622813 1899.43841108 + ------------------------------------------------------------------------------------- + TOTAL 161.49705076 955.84816588 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1189084 +BPFP 1.1285 bits/point +EBPFP 1.1285 equivalent bits/point +MSE 955.848166 +---------------------- -------------------------------------------------------- +Time: 5.237s Load: 0.080s, Pack+Encode: 2.633s, Decode+Unpack: 2.524s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 955.8482 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03014705-ILSVRC2012_val_00001168.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03014705-ILSVRC2012_val_00001168.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03017168-ILSVRC2012_val_00001601.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03017168-ILSVRC2012_val_00001601.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 618,680B, BPFP=1.1743 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 677,416B, BPFP=1.2858 +⌛️ [2/4] FRONTEND: Frontend time: 2.643s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.525s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10213913 0.81870162 + layer.39.0 475.40952988 2195.35228377 + ------------------------------------------------------------------------------------- + TOTAL 237.75583451 1098.08549270 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1296096 +BPFP 1.2300 bits/point +EBPFP 1.2300 equivalent bits/point +MSE 1098.085493 +---------------------- -------------------------------------------------------- +Time: 5.227s Load: 0.060s, Pack+Encode: 2.643s, Decode+Unpack: 2.525s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1098.0855 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03017168-ILSVRC2012_val_00001601.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03017168-ILSVRC2012_val_00001601.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03018349-ILSVRC2012_val_00000346.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03018349-ILSVRC2012_val_00000346.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.089s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 648,340B, BPFP=1.2306 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 773,420B, BPFP=1.4680 +⌛️ [2/4] FRONTEND: Frontend time: 2.735s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.547s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09959339 12.11300128 + layer.39.0 56.59841169 2155.74295432 + ------------------------------------------------------------------------------------- + TOTAL 28.34900254 1083.92797780 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1421760 +BPFP 1.3493 bits/point +EBPFP 1.3493 equivalent bits/point +MSE 1083.927978 +---------------------- -------------------------------------------------------- +Time: 5.372s Load: 0.089s, Pack+Encode: 2.735s, Decode+Unpack: 2.547s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1083.9280 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03018349-ILSVRC2012_val_00000346.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03018349-ILSVRC2012_val_00000346.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03026506-ILSVRC2012_val_00001908.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03026506-ILSVRC2012_val_00001908.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.090s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 669,888B, BPFP=1.2715 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 753,936B, BPFP=1.4310 +⌛️ [2/4] FRONTEND: Frontend time: 2.655s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.534s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10977067 12.24747650 + layer.39.0 668.54063411 2272.31292517 + ------------------------------------------------------------------------------------- + TOTAL 334.32520239 1142.28020084 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1423824 +BPFP 1.3513 bits/point +EBPFP 1.3513 equivalent bits/point +MSE 1142.280201 +---------------------- -------------------------------------------------------- +Time: 5.279s Load: 0.090s, Pack+Encode: 2.655s, Decode+Unpack: 2.534s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1142.2802 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03026506-ILSVRC2012_val_00001908.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03026506-ILSVRC2012_val_00001908.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03028079-ILSVRC2012_val_00003351.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03028079-ILSVRC2012_val_00003351.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 622,480B, BPFP=1.1815 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 635,196B, BPFP=1.2057 +⌛️ [2/4] FRONTEND: Frontend time: 2.636s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.533s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10934904 12.29077172 + layer.39.0 15.31112010 2148.00583090 + ------------------------------------------------------------------------------------- + TOTAL 7.71023457 1080.14830131 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1257676 +BPFP 1.1936 bits/point +EBPFP 1.1936 equivalent bits/point +MSE 1080.148301 +---------------------- -------------------------------------------------------- +Time: 5.221s Load: 0.052s, Pack+Encode: 2.636s, Decode+Unpack: 2.533s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1080.1483 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03028079-ILSVRC2012_val_00003351.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03028079-ILSVRC2012_val_00003351.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03032252-ILSVRC2012_val_00000086.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03032252-ILSVRC2012_val_00000086.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.090s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 719,780B, BPFP=1.3662 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 687,760B, BPFP=1.3054 +⌛️ [2/4] FRONTEND: Frontend time: 2.672s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.529s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13507480 1.65320809 + layer.39.0 103.55165816 2236.85252672 + ------------------------------------------------------------------------------------- + TOTAL 51.84336648 1119.25286741 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1407540 +BPFP 1.3358 bits/point +EBPFP 1.3358 equivalent bits/point +MSE 1119.252867 +---------------------- -------------------------------------------------------- +Time: 5.291s Load: 0.090s, Pack+Encode: 2.672s, Decode+Unpack: 2.529s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1119.2529 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03032252-ILSVRC2012_val_00000086.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03032252-ILSVRC2012_val_00000086.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03041632-ILSVRC2012_val_00000564.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03041632-ILSVRC2012_val_00000564.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 509,260B, BPFP=0.9666 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 598,900B, BPFP=1.1368 +⌛️ [2/4] FRONTEND: Frontend time: 2.630s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.517s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10123130 12.40733475 + layer.39.0 371.34277818 1722.26931487 + ------------------------------------------------------------------------------------- + TOTAL 185.72200474 867.33832481 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1108160 +BPFP 1.0517 bits/point +EBPFP 1.0517 equivalent bits/point +MSE 867.338325 +---------------------- -------------------------------------------------------- +Time: 5.206s Load: 0.059s, Pack+Encode: 2.630s, Decode+Unpack: 2.517s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 867.3383 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03041632-ILSVRC2012_val_00000564.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03041632-ILSVRC2012_val_00000564.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03042490-ILSVRC2012_val_00001426.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03042490-ILSVRC2012_val_00001426.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 695,196B, BPFP=1.3195 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 603,344B, BPFP=1.1452 +⌛️ [2/4] FRONTEND: Frontend time: 2.638s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.516s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10706725 12.97758272 + layer.39.0 141.71039845 1836.51348397 + ------------------------------------------------------------------------------------- + TOTAL 70.90873285 924.74553334 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1298540 +BPFP 1.2324 bits/point +EBPFP 1.2324 equivalent bits/point +MSE 924.745533 +---------------------- -------------------------------------------------------- +Time: 5.206s Load: 0.052s, Pack+Encode: 2.638s, Decode+Unpack: 2.516s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 924.7455 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03042490-ILSVRC2012_val_00001426.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03042490-ILSVRC2012_val_00001426.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03047690-ILSVRC2012_val_00001500.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03047690-ILSVRC2012_val_00001500.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 586,964B, BPFP=1.1141 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 707,632B, BPFP=1.3431 +⌛️ [2/4] FRONTEND: Frontend time: 2.643s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.535s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09570478 12.31890223 + layer.39.0 226.76483540 2625.22157434 + ------------------------------------------------------------------------------------- + TOTAL 113.43027009 1318.77023829 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1294596 +BPFP 1.2286 bits/point +EBPFP 1.2286 equivalent bits/point +MSE 1318.770238 +---------------------- -------------------------------------------------------- +Time: 5.228s Load: 0.050s, Pack+Encode: 2.643s, Decode+Unpack: 2.535s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1318.7702 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03047690-ILSVRC2012_val_00001500.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03047690-ILSVRC2012_val_00001500.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03062245-ILSVRC2012_val_00000344.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03062245-ILSVRC2012_val_00000344.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 563,112B, BPFP=1.0688 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 573,312B, BPFP=1.0882 +⌛️ [2/4] FRONTEND: Frontend time: 2.624s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.522s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09619164 12.22752483 + layer.39.0 46.71096787 1766.86297376 + ------------------------------------------------------------------------------------- + TOTAL 23.40357976 889.54524929 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1136424 +BPFP 1.0785 bits/point +EBPFP 1.0785 equivalent bits/point +MSE 889.545249 +---------------------- -------------------------------------------------------- +Time: 5.207s Load: 0.060s, Pack+Encode: 2.624s, Decode+Unpack: 2.522s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 889.5452 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03062245-ILSVRC2012_val_00000344.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03062245-ILSVRC2012_val_00000344.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063599-ILSVRC2012_val_00000164.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063599-ILSVRC2012_val_00000164.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 633,924B, BPFP=1.2032 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 731,792B, BPFP=1.3890 +⌛️ [2/4] FRONTEND: Frontend time: 2.654s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.535s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10111790 12.08700517 + layer.39.0 9.80528160 2204.47181730 + ------------------------------------------------------------------------------------- + TOTAL 4.95319975 1108.27941123 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1365716 +BPFP 1.2961 bits/point +EBPFP 1.2961 equivalent bits/point +MSE 1108.279411 +---------------------- -------------------------------------------------------- +Time: 5.259s Load: 0.070s, Pack+Encode: 2.654s, Decode+Unpack: 2.535s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1108.2794 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063599-ILSVRC2012_val_00000164.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03063599-ILSVRC2012_val_00000164.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063689-ILSVRC2012_val_00001940.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063689-ILSVRC2012_val_00001940.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 602,968B, BPFP=1.1445 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 647,856B, BPFP=1.2297 +⌛️ [2/4] FRONTEND: Frontend time: 2.636s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.521s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09645106 8.88975283 + layer.39.0 18.48014797 2479.95578231 + ------------------------------------------------------------------------------------- + TOTAL 9.28829952 1244.42276757 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1250824 +BPFP 1.1871 bits/point +EBPFP 1.1871 equivalent bits/point +MSE 1244.422768 +---------------------- -------------------------------------------------------- +Time: 5.217s Load: 0.060s, Pack+Encode: 2.636s, Decode+Unpack: 2.521s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1244.4228 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063689-ILSVRC2012_val_00001940.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03063689-ILSVRC2012_val_00001940.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03065424-ILSVRC2012_val_00000915.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03065424-ILSVRC2012_val_00000915.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.089s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 726,616B, BPFP=1.3792 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 707,472B, BPFP=1.3428 +⌛️ [2/4] FRONTEND: Frontend time: 2.723s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.551s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12384982 13.11905901 + layer.39.0 2154.15986395 2766.09766764 + ------------------------------------------------------------------------------------- + TOTAL 1077.14185688 1389.60836332 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1434088 +BPFP 1.3610 bits/point +EBPFP 1.3610 equivalent bits/point +MSE 1389.608363 +---------------------- -------------------------------------------------------- +Time: 5.364s Load: 0.089s, Pack+Encode: 2.723s, Decode+Unpack: 2.551s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1389.6084 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03065424-ILSVRC2012_val_00000915.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03065424-ILSVRC2012_val_00000915.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03075370-ILSVRC2012_val_00004971.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03075370-ILSVRC2012_val_00004971.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 609,072B, BPFP=1.1561 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 570,284B, BPFP=1.0824 +⌛️ [2/4] FRONTEND: Frontend time: 2.632s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.521s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10672879 0.80901157 + layer.39.0 301.29020894 1409.79057337 + ------------------------------------------------------------------------------------- + TOTAL 150.69846886 705.29979247 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1179356 +BPFP 1.1193 bits/point +EBPFP 1.1193 equivalent bits/point +MSE 705.299792 +---------------------- -------------------------------------------------------- +Time: 5.222s Load: 0.070s, Pack+Encode: 2.632s, Decode+Unpack: 2.521s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 705.2998 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03075370-ILSVRC2012_val_00004971.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03075370-ILSVRC2012_val_00004971.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03089624-ILSVRC2012_val_00001190.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03089624-ILSVRC2012_val_00001190.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 669,196B, BPFP=1.2702 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 717,192B, BPFP=1.3613 +⌛️ [2/4] FRONTEND: Frontend time: 2.640s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.529s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10385029 0.82338359 + layer.39.0 606.38896987 2647.11224490 + ------------------------------------------------------------------------------------- + TOTAL 303.24641008 1323.96781425 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1386388 +BPFP 1.3157 bits/point +EBPFP 1.3157 equivalent bits/point +MSE 1323.967814 +---------------------- -------------------------------------------------------- +Time: 5.221s Load: 0.052s, Pack+Encode: 2.640s, Decode+Unpack: 2.529s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1323.9678 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03089624-ILSVRC2012_val_00001190.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03089624-ILSVRC2012_val_00001190.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03095699-ILSVRC2012_val_00000403.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03095699-ILSVRC2012_val_00000403.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 718,304B, BPFP=1.3634 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 660,432B, BPFP=1.2536 +⌛️ [2/4] FRONTEND: Frontend time: 2.638s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.538s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12139760 21.02861926 + layer.39.0 62.59250486 2953.92638484 + ------------------------------------------------------------------------------------- + TOTAL 31.35695123 1487.47750205 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1378736 +BPFP 1.3085 bits/point +EBPFP 1.3085 equivalent bits/point +MSE 1487.477502 +---------------------- -------------------------------------------------------- +Time: 5.226s Load: 0.050s, Pack+Encode: 2.638s, Decode+Unpack: 2.538s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1487.4775 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03095699-ILSVRC2012_val_00000403.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03095699-ILSVRC2012_val_00000403.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03100240-ILSVRC2012_val_00001201.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03100240-ILSVRC2012_val_00001201.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 659,020B, BPFP=1.2509 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 493,432B, BPFP=0.9366 +⌛️ [2/4] FRONTEND: Frontend time: 2.711s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.516s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10258218 12.31485647 + layer.39.0 42.98202138 1219.47145287 + ------------------------------------------------------------------------------------- + TOTAL 21.54230178 615.89315467 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1152452 +BPFP 1.0937 bits/point +EBPFP 1.0937 equivalent bits/point +MSE 615.893155 +---------------------- -------------------------------------------------------- +Time: 5.306s Load: 0.080s, Pack+Encode: 2.711s, Decode+Unpack: 2.516s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 615.8932 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03100240-ILSVRC2012_val_00001201.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03100240-ILSVRC2012_val_00001201.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03109150-ILSVRC2012_val_00000678.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03109150-ILSVRC2012_val_00000678.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 663,468B, BPFP=1.2593 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 681,004B, BPFP=1.2926 +⌛️ [2/4] FRONTEND: Frontend time: 2.653s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.531s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09720685 12.29265082 + layer.39.0 496.21158285 3054.30466472 + ------------------------------------------------------------------------------------- + TOTAL 248.15439485 1533.29865777 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1344472 +BPFP 1.2760 bits/point +EBPFP 1.2760 equivalent bits/point +MSE 1533.298658 +---------------------- -------------------------------------------------------- +Time: 5.245s Load: 0.060s, Pack+Encode: 2.653s, Decode+Unpack: 2.531s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1533.2987 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03109150-ILSVRC2012_val_00000678.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03109150-ILSVRC2012_val_00000678.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03110669-ILSVRC2012_val_00002171.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03110669-ILSVRC2012_val_00002171.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 757,232B, BPFP=1.4373 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 785,600B, BPFP=1.4911 +⌛️ [2/4] FRONTEND: Frontend time: 2.651s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.556s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.15128201 12.40180185 + layer.39.0 15.00769387 2452.26603499 + ------------------------------------------------------------------------------------- + TOTAL 7.57948794 1232.33391842 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1542832 +BPFP 1.4642 bits/point +EBPFP 1.4642 equivalent bits/point +MSE 1232.333918 +---------------------- -------------------------------------------------------- +Time: 5.258s Load: 0.051s, Pack+Encode: 2.651s, Decode+Unpack: 2.556s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1232.3339 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03110669-ILSVRC2012_val_00002171.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03110669-ILSVRC2012_val_00002171.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124043-ILSVRC2012_val_00000766.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124043-ILSVRC2012_val_00000766.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 550,684B, BPFP=1.0452 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 644,256B, BPFP=1.2228 +⌛️ [2/4] FRONTEND: Frontend time: 2.655s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.531s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11473456 8.83009805 + layer.39.0 54.83309418 2738.26749271 + ------------------------------------------------------------------------------------- + TOTAL 27.47391437 1373.54879538 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1194940 +BPFP 1.1340 bits/point +EBPFP 1.1340 equivalent bits/point +MSE 1373.548795 +---------------------- -------------------------------------------------------- +Time: 5.237s Load: 0.051s, Pack+Encode: 2.655s, Decode+Unpack: 2.531s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1373.5488 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124043-ILSVRC2012_val_00000766.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03124043-ILSVRC2012_val_00000766.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124170-ILSVRC2012_val_00001875.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124170-ILSVRC2012_val_00001875.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.058s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 632,068B, BPFP=1.1997 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 518,920B, BPFP=0.9850 +⌛️ [2/4] FRONTEND: Frontend time: 2.639s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.518s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11393612 8.51546841 + layer.39.0 9.06747107 1834.73505831 + ------------------------------------------------------------------------------------- + TOTAL 4.59070360 921.62526336 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1150988 +BPFP 1.0923 bits/point +EBPFP 1.0923 equivalent bits/point +MSE 921.625263 +---------------------- -------------------------------------------------------- +Time: 5.215s Load: 0.058s, Pack+Encode: 2.639s, Decode+Unpack: 2.518s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 921.6253 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124170-ILSVRC2012_val_00001875.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03124170-ILSVRC2012_val_00001875.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03126707-ILSVRC2012_val_00000020.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03126707-ILSVRC2012_val_00000020.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.088s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 658,948B, BPFP=1.2507 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 516,108B, BPFP=0.9796 +⌛️ [2/4] FRONTEND: Frontend time: 2.634s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.533s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.15273996 12.23906231 + layer.39.0 1033.15269679 1553.03875121 + ------------------------------------------------------------------------------------- + TOTAL 516.65271838 782.63890676 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1175056 +BPFP 1.1152 bits/point +EBPFP 1.1152 equivalent bits/point +MSE 782.638907 +---------------------- -------------------------------------------------------- +Time: 5.254s Load: 0.088s, Pack+Encode: 2.634s, Decode+Unpack: 2.533s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 782.6389 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03126707-ILSVRC2012_val_00000020.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03126707-ILSVRC2012_val_00000020.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03127747-ILSVRC2012_val_00001689.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03127747-ILSVRC2012_val_00001689.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 511,644B, BPFP=0.9711 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 598,060B, BPFP=1.1352 +⌛️ [2/4] FRONTEND: Frontend time: 2.645s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.539s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10152024 12.24134570 + layer.39.0 322.92343902 1617.10762877 + ------------------------------------------------------------------------------------- + TOTAL 161.51247963 814.67448723 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1109704 +BPFP 1.0532 bits/point +EBPFP 1.0532 equivalent bits/point +MSE 814.674487 +---------------------- -------------------------------------------------------- +Time: 5.254s Load: 0.070s, Pack+Encode: 2.645s, Decode+Unpack: 2.539s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 814.6745 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03127747-ILSVRC2012_val_00001689.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03127747-ILSVRC2012_val_00001689.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03131574-ILSVRC2012_val_00003036.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03131574-ILSVRC2012_val_00003036.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 601,616B, BPFP=1.1419 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 653,800B, BPFP=1.2410 +⌛️ [2/4] FRONTEND: Frontend time: 2.713s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.514s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09568423 8.47512281 + layer.39.0 163.24681122 2323.50947522 + ------------------------------------------------------------------------------------- + TOTAL 81.67124773 1165.99229901 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1255416 +BPFP 1.1914 bits/point +EBPFP 1.1914 equivalent bits/point +MSE 1165.992299 +---------------------- -------------------------------------------------------- +Time: 5.297s Load: 0.070s, Pack+Encode: 2.713s, Decode+Unpack: 2.514s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1165.9923 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03131574-ILSVRC2012_val_00003036.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03131574-ILSVRC2012_val_00003036.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03133878-ILSVRC2012_val_00000534.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03133878-ILSVRC2012_val_00000534.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 724,636B, BPFP=1.3754 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 690,320B, BPFP=1.3103 +⌛️ [2/4] FRONTEND: Frontend time: 2.680s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.522s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11186348 12.49697446 + layer.39.0 28.46096218 2421.47740525 + ------------------------------------------------------------------------------------- + TOTAL 14.28641283 1216.98718985 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1414956 +BPFP 1.3429 bits/point +EBPFP 1.3429 equivalent bits/point +MSE 1216.987190 +---------------------- -------------------------------------------------------- +Time: 5.273s Load: 0.071s, Pack+Encode: 2.680s, Decode+Unpack: 2.522s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1216.9872 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03133878-ILSVRC2012_val_00000534.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03133878-ILSVRC2012_val_00000534.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03134739-ILSVRC2012_val_00000249.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03134739-ILSVRC2012_val_00000249.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 616,396B, BPFP=1.1700 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 737,688B, BPFP=1.4002 +⌛️ [2/4] FRONTEND: Frontend time: 2.631s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.524s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09967384 0.80395294 + layer.39.0 372.24465500 2495.63411079 + ------------------------------------------------------------------------------------- + TOTAL 186.17216442 1248.21903186 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1354084 +BPFP 1.2851 bits/point +EBPFP 1.2851 equivalent bits/point +MSE 1248.219032 +---------------------- -------------------------------------------------------- +Time: 5.206s Load: 0.050s, Pack+Encode: 2.631s, Decode+Unpack: 2.524s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1248.2190 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03134739-ILSVRC2012_val_00000249.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03134739-ILSVRC2012_val_00000249.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03141823-ILSVRC2012_val_00001337.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03141823-ILSVRC2012_val_00001337.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 677,372B, BPFP=1.2857 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 756,992B, BPFP=1.4368 +⌛️ [2/4] FRONTEND: Frontend time: 2.642s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.535s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10422104 8.49636423 + layer.39.0 29.45558301 2062.02696793 + ------------------------------------------------------------------------------------- + TOTAL 14.77990203 1035.26166608 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1434364 +BPFP 1.3613 bits/point +EBPFP 1.3613 equivalent bits/point +MSE 1035.261666 +---------------------- -------------------------------------------------------- +Time: 5.237s Load: 0.060s, Pack+Encode: 2.642s, Decode+Unpack: 2.535s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1035.2617 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03141823-ILSVRC2012_val_00001337.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03141823-ILSVRC2012_val_00001337.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03160309-ILSVRC2012_val_00000330.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03160309-ILSVRC2012_val_00000330.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 620,044B, BPFP=1.1769 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 419,364B, BPFP=0.7960 +⌛️ [2/4] FRONTEND: Frontend time: 2.619s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.509s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09980877 8.57477584 + layer.39.0 30.04123011 888.34754616 + ------------------------------------------------------------------------------------- + TOTAL 15.07051944 448.46116100 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1039408 +BPFP 0.9864 bits/point +EBPFP 0.9864 equivalent bits/point +MSE 448.461161 +---------------------- -------------------------------------------------------- +Time: 5.178s Load: 0.050s, Pack+Encode: 2.619s, Decode+Unpack: 2.509s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 448.4612 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03160309-ILSVRC2012_val_00000330.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03160309-ILSVRC2012_val_00000330.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03187595-ILSVRC2012_val_00000137.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03187595-ILSVRC2012_val_00000137.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 627,244B, BPFP=1.1906 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 709,048B, BPFP=1.3458 +⌛️ [2/4] FRONTEND: Frontend time: 2.643s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.534s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10716813 0.83424803 + layer.39.0 12.39187394 2315.70675413 + ------------------------------------------------------------------------------------- + TOTAL 6.24952103 1158.27050108 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1336292 +BPFP 1.2682 bits/point +EBPFP 1.2682 equivalent bits/point +MSE 1158.270501 +---------------------- -------------------------------------------------------- +Time: 5.247s Load: 0.070s, Pack+Encode: 2.643s, Decode+Unpack: 2.534s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1158.2705 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03187595-ILSVRC2012_val_00000137.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03187595-ILSVRC2012_val_00000137.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03188531-ILSVRC2012_val_00000493.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03188531-ILSVRC2012_val_00000493.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.088s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 544,748B, BPFP=1.0340 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 618,760B, BPFP=1.1745 +⌛️ [2/4] FRONTEND: Frontend time: 2.645s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.513s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09509044 12.17611626 + layer.39.0 10.77256154 1507.57847425 + ------------------------------------------------------------------------------------- + TOTAL 5.43382599 759.87729525 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1163508 +BPFP 1.1042 bits/point +EBPFP 1.1042 equivalent bits/point +MSE 759.877295 +---------------------- -------------------------------------------------------- +Time: 5.246s Load: 0.088s, Pack+Encode: 2.645s, Decode+Unpack: 2.513s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 759.8773 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03188531-ILSVRC2012_val_00000493.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03188531-ILSVRC2012_val_00000493.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03196217-ILSVRC2012_val_00003643.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03196217-ILSVRC2012_val_00003643.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 570,184B, BPFP=1.0823 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 643,088B, BPFP=1.2206 +⌛️ [2/4] FRONTEND: Frontend time: 2.637s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.512s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09478207 0.80767039 + layer.39.0 65.57403274 1686.52623907 + ------------------------------------------------------------------------------------- + TOTAL 32.83440740 843.66695473 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1213272 +BPFP 1.1514 bits/point +EBPFP 1.1514 equivalent bits/point +MSE 843.666955 +---------------------- -------------------------------------------------------- +Time: 5.220s Load: 0.070s, Pack+Encode: 2.637s, Decode+Unpack: 2.512s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 843.6670 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03196217-ILSVRC2012_val_00003643.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03196217-ILSVRC2012_val_00003643.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03201208-ILSVRC2012_val_00000241.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03201208-ILSVRC2012_val_00000241.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 680,192B, BPFP=1.2911 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 605,160B, BPFP=1.1486 +⌛️ [2/4] FRONTEND: Frontend time: 2.635s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.546s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10331685 24.97928055 + layer.39.0 136.59314261 1560.23299320 + ------------------------------------------------------------------------------------- + TOTAL 68.34822973 792.60613687 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1285352 +BPFP 1.2199 bits/point +EBPFP 1.2199 equivalent bits/point +MSE 792.606137 +---------------------- -------------------------------------------------------- +Time: 5.232s Load: 0.051s, Pack+Encode: 2.635s, Decode+Unpack: 2.546s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 792.6061 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03201208-ILSVRC2012_val_00000241.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03201208-ILSVRC2012_val_00000241.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03207743-ILSVRC2012_val_00000256.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03207743-ILSVRC2012_val_00000256.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 753,096B, BPFP=1.4294 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 552,804B, BPFP=1.0493 +⌛️ [2/4] FRONTEND: Frontend time: 2.640s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.529s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09674843 24.79702305 + layer.39.0 189.63590258 1372.94752187 + ------------------------------------------------------------------------------------- + TOTAL 94.86632550 698.87227246 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1305900 +BPFP 1.2394 bits/point +EBPFP 1.2394 equivalent bits/point +MSE 698.872272 +---------------------- -------------------------------------------------------- +Time: 5.220s Load: 0.051s, Pack+Encode: 2.640s, Decode+Unpack: 2.529s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 698.8723 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03207743-ILSVRC2012_val_00000256.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03207743-ILSVRC2012_val_00000256.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03216828-ILSVRC2012_val_00001729.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03216828-ILSVRC2012_val_00001729.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 660,448B, BPFP=1.2536 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 592,400B, BPFP=1.1244 +⌛️ [2/4] FRONTEND: Frontend time: 2.634s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.523s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10800209 12.67416314 + layer.39.0 31.30713223 1715.90913508 + ------------------------------------------------------------------------------------- + TOTAL 15.70756716 864.29164911 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1252848 +BPFP 1.1890 bits/point +EBPFP 1.1890 equivalent bits/point +MSE 864.291649 +---------------------- -------------------------------------------------------- +Time: 5.209s Load: 0.051s, Pack+Encode: 2.634s, Decode+Unpack: 2.523s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 864.2916 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03216828-ILSVRC2012_val_00001729.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03216828-ILSVRC2012_val_00001729.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03218198-ILSVRC2012_val_00002266.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03218198-ILSVRC2012_val_00002266.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 723,792B, BPFP=1.3738 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 739,336B, BPFP=1.4033 +⌛️ [2/4] FRONTEND: Frontend time: 2.642s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.525s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11617067 12.26525867 + layer.39.0 195.83184524 2876.84523810 + ------------------------------------------------------------------------------------- + TOTAL 97.97400795 1444.55524838 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1463128 +BPFP 1.3886 bits/point +EBPFP 1.3886 equivalent bits/point +MSE 1444.555248 +---------------------- -------------------------------------------------------- +Time: 5.219s Load: 0.052s, Pack+Encode: 2.642s, Decode+Unpack: 2.525s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1444.5552 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03218198-ILSVRC2012_val_00002266.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03218198-ILSVRC2012_val_00002266.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03220513-ILSVRC2012_val_00001868.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03220513-ILSVRC2012_val_00001868.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 877,776B, BPFP=1.6661 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 684,704B, BPFP=1.2996 +⌛️ [2/4] FRONTEND: Frontend time: 2.703s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.539s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.20032125 48.90168701 + layer.39.0 377.00176142 2559.04931973 + ------------------------------------------------------------------------------------- + TOTAL 188.60104134 1303.97550337 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1562480 +BPFP 1.4829 bits/point +EBPFP 1.4829 equivalent bits/point +MSE 1303.975503 +---------------------- -------------------------------------------------------- +Time: 5.292s Load: 0.050s, Pack+Encode: 2.703s, Decode+Unpack: 2.539s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1303.9755 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03220513-ILSVRC2012_val_00001868.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03220513-ILSVRC2012_val_00001868.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03223299-ILSVRC2012_val_00001893.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03223299-ILSVRC2012_val_00001893.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.087s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 562,892B, BPFP=1.0684 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 542,104B, BPFP=1.0290 +⌛️ [2/4] FRONTEND: Frontend time: 2.668s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.528s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10735053 12.20777720 + layer.39.0 354.51621720 1403.38811953 + ------------------------------------------------------------------------------------- + TOTAL 177.31178386 707.79794836 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1104996 +BPFP 1.0487 bits/point +EBPFP 1.0487 equivalent bits/point +MSE 707.797948 +---------------------- -------------------------------------------------------- +Time: 5.284s Load: 0.087s, Pack+Encode: 2.668s, Decode+Unpack: 2.528s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 707.7979 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03223299-ILSVRC2012_val_00001893.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03223299-ILSVRC2012_val_00001893.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03240683-ILSVRC2012_val_00000504.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03240683-ILSVRC2012_val_00000504.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 569,004B, BPFP=1.0800 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 635,444B, BPFP=1.2061 +⌛️ [2/4] FRONTEND: Frontend time: 2.636s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.529s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10065408 12.02572279 + layer.39.0 443.53838678 2281.19047619 + ------------------------------------------------------------------------------------- + TOTAL 221.81952043 1146.60809949 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1204448 +BPFP 1.1431 bits/point +EBPFP 1.1431 equivalent bits/point +MSE 1146.608099 +---------------------- -------------------------------------------------------- +Time: 5.235s Load: 0.070s, Pack+Encode: 2.636s, Decode+Unpack: 2.529s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1146.6081 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03240683-ILSVRC2012_val_00000504.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03240683-ILSVRC2012_val_00000504.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03250847-ILSVRC2012_val_00000542.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03250847-ILSVRC2012_val_00000542.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 674,248B, BPFP=1.2798 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 754,472B, BPFP=1.4320 +⌛️ [2/4] FRONTEND: Frontend time: 2.627s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.527s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10136319 12.32095785 + layer.39.0 140.24735787 2588.93610301 + ------------------------------------------------------------------------------------- + TOTAL 70.17436053 1300.62853043 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1428720 +BPFP 1.3559 bits/point +EBPFP 1.3559 equivalent bits/point +MSE 1300.628530 +---------------------- -------------------------------------------------------- +Time: 5.205s Load: 0.051s, Pack+Encode: 2.627s, Decode+Unpack: 2.527s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1300.6285 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03250847-ILSVRC2012_val_00000542.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03250847-ILSVRC2012_val_00000542.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03255030-ILSVRC2012_val_00001045.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03255030-ILSVRC2012_val_00001045.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.057s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 622,248B, BPFP=1.1811 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 605,688B, BPFP=1.1496 +⌛️ [2/4] FRONTEND: Frontend time: 2.626s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.518s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10050351 0.79453917 + layer.39.0 12.06722622 2011.48347911 + ------------------------------------------------------------------------------------- + TOTAL 6.08386487 1006.13900914 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1227936 +BPFP 1.1654 bits/point +EBPFP 1.1654 equivalent bits/point +MSE 1006.139009 +---------------------- -------------------------------------------------------- +Time: 5.201s Load: 0.057s, Pack+Encode: 2.626s, Decode+Unpack: 2.518s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1006.1390 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03255030-ILSVRC2012_val_00001045.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03255030-ILSVRC2012_val_00001045.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03271574-ILSVRC2012_val_00000942.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03271574-ILSVRC2012_val_00000942.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 607,448B, BPFP=1.1530 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 585,788B, BPFP=1.1119 +⌛️ [2/4] FRONTEND: Frontend time: 2.628s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.509s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10164264 36.19317906 + layer.39.0 660.63544704 1885.40694849 + ------------------------------------------------------------------------------------- + TOTAL 330.36854484 960.80006378 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1193236 +BPFP 1.1324 bits/point +EBPFP 1.1324 equivalent bits/point +MSE 960.800064 +---------------------- -------------------------------------------------------- +Time: 5.189s Load: 0.052s, Pack+Encode: 2.628s, Decode+Unpack: 2.509s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 960.8001 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03271574-ILSVRC2012_val_00000942.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03271574-ILSVRC2012_val_00000942.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272010-ILSVRC2012_val_00000374.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272010-ILSVRC2012_val_00000374.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.089s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 604,580B, BPFP=1.1475 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 710,564B, BPFP=1.3487 +⌛️ [2/4] FRONTEND: Frontend time: 2.658s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.519s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10420663 12.29096058 + layer.39.0 9.63653369 2532.35908649 + ------------------------------------------------------------------------------------- + TOTAL 4.87037016 1272.32502354 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1315144 +BPFP 1.2481 bits/point +EBPFP 1.2481 equivalent bits/point +MSE 1272.325024 +---------------------- -------------------------------------------------------- +Time: 5.266s Load: 0.089s, Pack+Encode: 2.658s, Decode+Unpack: 2.519s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1272.3250 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272010-ILSVRC2012_val_00000374.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03272010-ILSVRC2012_val_00000374.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272562-ILSVRC2012_val_00001699.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272562-ILSVRC2012_val_00001699.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 702,200B, BPFP=1.3328 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 532,236B, BPFP=1.0102 +⌛️ [2/4] FRONTEND: Frontend time: 2.633s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.518s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11399285 12.66663440 + layer.39.0 12.79457642 1460.12086978 + ------------------------------------------------------------------------------------- + TOTAL 6.45428464 736.39375209 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1234436 +BPFP 1.1715 bits/point +EBPFP 1.1715 equivalent bits/point +MSE 736.393752 +---------------------- -------------------------------------------------------- +Time: 5.203s Load: 0.052s, Pack+Encode: 2.633s, Decode+Unpack: 2.518s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 736.3938 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272562-ILSVRC2012_val_00001699.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03272562-ILSVRC2012_val_00001699.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03290653-ILSVRC2012_val_00000199.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03290653-ILSVRC2012_val_00000199.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 581,028B, BPFP=1.1028 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 654,264B, BPFP=1.2418 +⌛️ [2/4] FRONTEND: Frontend time: 2.646s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.521s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09581849 0.80744339 + layer.39.0 9.30266794 1803.86686103 + ------------------------------------------------------------------------------------- + TOTAL 4.69924322 902.33715221 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1235292 +BPFP 1.1723 bits/point +EBPFP 1.1723 equivalent bits/point +MSE 902.337152 +---------------------- -------------------------------------------------------- +Time: 5.228s Load: 0.061s, Pack+Encode: 2.646s, Decode+Unpack: 2.521s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 902.3372 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03290653-ILSVRC2012_val_00000199.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03290653-ILSVRC2012_val_00000199.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03291819-ILSVRC2012_val_00000419.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03291819-ILSVRC2012_val_00000419.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.090s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 558,728B, BPFP=1.0605 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 514,312B, BPFP=0.9762 +⌛️ [2/4] FRONTEND: Frontend time: 2.651s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.523s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10621172 1.21827821 + layer.39.0 31.36357166 847.04938047 + ------------------------------------------------------------------------------------- + TOTAL 15.73489169 424.13382934 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1073040 +BPFP 1.0184 bits/point +EBPFP 1.0184 equivalent bits/point +MSE 424.133829 +---------------------- -------------------------------------------------------- +Time: 5.264s Load: 0.090s, Pack+Encode: 2.651s, Decode+Unpack: 2.523s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 424.1338 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03291819-ILSVRC2012_val_00000419.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03291819-ILSVRC2012_val_00000419.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03314780-ILSVRC2012_val_00000624.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03314780-ILSVRC2012_val_00000624.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 631,900B, BPFP=1.1994 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 740,364B, BPFP=1.4053 +⌛️ [2/4] FRONTEND: Frontend time: 2.645s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.529s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10172509 0.80143799 + layer.39.0 35.60390853 2779.91618076 + ------------------------------------------------------------------------------------- + TOTAL 17.85281681 1390.35880937 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1372264 +BPFP 1.3023 bits/point +EBPFP 1.3023 equivalent bits/point +MSE 1390.358809 +---------------------- -------------------------------------------------------- +Time: 5.224s Load: 0.050s, Pack+Encode: 2.645s, Decode+Unpack: 2.529s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1390.3588 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03314780-ILSVRC2012_val_00000624.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03314780-ILSVRC2012_val_00000624.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03325584-ILSVRC2012_val_00001256.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03325584-ILSVRC2012_val_00001256.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 709,980B, BPFP=1.3476 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 732,820B, BPFP=1.3910 +⌛️ [2/4] FRONTEND: Frontend time: 2.686s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.540s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11348933 0.84137099 + layer.39.0 26.85401292 2763.17687075 + ------------------------------------------------------------------------------------- + TOTAL 13.48375113 1382.00912087 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1442800 +BPFP 1.3693 bits/point +EBPFP 1.3693 equivalent bits/point +MSE 1382.009121 +---------------------- -------------------------------------------------------- +Time: 5.297s Load: 0.072s, Pack+Encode: 2.686s, Decode+Unpack: 2.540s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1382.0091 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03325584-ILSVRC2012_val_00001256.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03325584-ILSVRC2012_val_00001256.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03337140-ILSVRC2012_val_00000132.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03337140-ILSVRC2012_val_00000132.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 567,120B, BPFP=1.0764 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 579,240B, BPFP=1.0994 +⌛️ [2/4] FRONTEND: Frontend time: 2.712s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.526s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09852950 8.59123409 + layer.39.0 10.39905343 1529.99611273 + ------------------------------------------------------------------------------------- + TOTAL 5.24879146 769.29367341 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1146360 +BPFP 1.0879 bits/point +EBPFP 1.0879 equivalent bits/point +MSE 769.293673 +---------------------- -------------------------------------------------------- +Time: 5.308s Load: 0.070s, Pack+Encode: 2.712s, Decode+Unpack: 2.526s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 769.2937 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03337140-ILSVRC2012_val_00000132.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03337140-ILSVRC2012_val_00000132.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03344393-ILSVRC2012_val_00000288.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03344393-ILSVRC2012_val_00000288.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.078s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 601,164B, BPFP=1.1411 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 578,836B, BPFP=1.0987 +⌛️ [2/4] FRONTEND: Frontend time: 2.661s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.519s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09830858 8.61496295 + layer.39.0 109.00505649 1436.21416424 + ------------------------------------------------------------------------------------- + TOTAL 54.55168253 722.41456359 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1180000 +BPFP 1.1199 bits/point +EBPFP 1.1199 equivalent bits/point +MSE 722.414564 +---------------------- -------------------------------------------------------- +Time: 5.257s Load: 0.078s, Pack+Encode: 2.661s, Decode+Unpack: 2.519s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 722.4146 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03344393-ILSVRC2012_val_00000288.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03344393-ILSVRC2012_val_00000288.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03345487-ILSVRC2012_val_00000764.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03345487-ILSVRC2012_val_00000764.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 643,324B, BPFP=1.2211 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 646,844B, BPFP=1.2278 +⌛️ [2/4] FRONTEND: Frontend time: 2.646s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.523s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10639974 12.75927117 + layer.39.0 14.55993569 2204.95043732 + ------------------------------------------------------------------------------------- + TOTAL 7.33316771 1108.85485425 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1290168 +BPFP 1.2244 bits/point +EBPFP 1.2244 equivalent bits/point +MSE 1108.854854 +---------------------- -------------------------------------------------------- +Time: 5.239s Load: 0.071s, Pack+Encode: 2.646s, Decode+Unpack: 2.523s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1108.8549 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03345487-ILSVRC2012_val_00000764.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03345487-ILSVRC2012_val_00000764.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03347037-ILSVRC2012_val_00000743.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03347037-ILSVRC2012_val_00000743.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 752,100B, BPFP=1.4275 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 684,212B, BPFP=1.2987 +⌛️ [2/4] FRONTEND: Frontend time: 2.627s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.540s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14351733 12.66804847 + layer.39.0 355.98426871 2101.11370262 + ------------------------------------------------------------------------------------- + TOTAL 178.06389302 1056.89087555 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1436312 +BPFP 1.3631 bits/point +EBPFP 1.3631 equivalent bits/point +MSE 1056.890876 +---------------------- -------------------------------------------------------- +Time: 5.216s Load: 0.050s, Pack+Encode: 2.627s, Decode+Unpack: 2.540s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1056.8909 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03347037-ILSVRC2012_val_00000743.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03347037-ILSVRC2012_val_00000743.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03355925-ILSVRC2012_val_00000445.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03355925-ILSVRC2012_val_00000445.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 546,164B, BPFP=1.0367 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 520,972B, BPFP=0.9888 +⌛️ [2/4] FRONTEND: Frontend time: 2.630s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.549s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09979894 8.64187584 + layer.39.0 9.06502540 1496.67298348 + ------------------------------------------------------------------------------------- + TOTAL 4.58241217 752.65742966 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1067136 +BPFP 1.0128 bits/point +EBPFP 1.0128 equivalent bits/point +MSE 752.657430 +---------------------- -------------------------------------------------------- +Time: 5.230s Load: 0.050s, Pack+Encode: 2.630s, Decode+Unpack: 2.549s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 752.6574 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03355925-ILSVRC2012_val_00000445.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03355925-ILSVRC2012_val_00000445.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03376595-ILSVRC2012_val_00001616.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03376595-ILSVRC2012_val_00001616.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.062s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 690,204B, BPFP=1.3101 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 730,148B, BPFP=1.3859 +⌛️ [2/4] FRONTEND: Frontend time: 2.628s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.525s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09988844 12.45882209 + layer.39.0 1408.20760447 2704.29470360 + ------------------------------------------------------------------------------------- + TOTAL 704.15374646 1358.37676284 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1420352 +BPFP 1.3480 bits/point +EBPFP 1.3480 equivalent bits/point +MSE 1358.376763 +---------------------- -------------------------------------------------------- +Time: 5.216s Load: 0.062s, Pack+Encode: 2.628s, Decode+Unpack: 2.525s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1358.3768 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03376595-ILSVRC2012_val_00001616.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03376595-ILSVRC2012_val_00001616.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03379051-ILSVRC2012_val_00002562.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03379051-ILSVRC2012_val_00002562.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 676,332B, BPFP=1.2837 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 693,824B, BPFP=1.3169 +⌛️ [2/4] FRONTEND: Frontend time: 2.645s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.528s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10889592 1.20414130 + layer.39.0 102.95462828 2299.05636540 + ------------------------------------------------------------------------------------- + TOTAL 51.53176210 1150.13025335 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1370156 +BPFP 1.3003 bits/point +EBPFP 1.3003 equivalent bits/point +MSE 1150.130253 +---------------------- -------------------------------------------------------- +Time: 5.226s Load: 0.052s, Pack+Encode: 2.645s, Decode+Unpack: 2.528s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1150.1303 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03379051-ILSVRC2012_val_00002562.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03379051-ILSVRC2012_val_00002562.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388043-ILSVRC2012_val_00001018.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388043-ILSVRC2012_val_00001018.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.055s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 640,256B, BPFP=1.2153 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 588,684B, BPFP=1.1174 +⌛️ [2/4] FRONTEND: Frontend time: 2.668s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.523s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09747427 12.59291105 + layer.39.0 21.12933142 1848.90026725 + ------------------------------------------------------------------------------------- + TOTAL 10.61340285 930.74658915 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1228940 +BPFP 1.1663 bits/point +EBPFP 1.1663 equivalent bits/point +MSE 930.746589 +---------------------- -------------------------------------------------------- +Time: 5.246s Load: 0.055s, Pack+Encode: 2.668s, Decode+Unpack: 2.523s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 930.7466 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388043-ILSVRC2012_val_00001018.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03388043-ILSVRC2012_val_00001018.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388183-ILSVRC2012_val_00002799.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388183-ILSVRC2012_val_00002799.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 689,964B, BPFP=1.3096 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 708,032B, BPFP=1.3439 +⌛️ [2/4] FRONTEND: Frontend time: 2.648s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.535s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10066175 12.83521718 + layer.39.0 786.68810739 3655.44509232 + ------------------------------------------------------------------------------------- + TOTAL 393.39438457 1834.14015475 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1397996 +BPFP 1.3268 bits/point +EBPFP 1.3268 equivalent bits/point +MSE 1834.140155 +---------------------- -------------------------------------------------------- +Time: 5.251s Load: 0.069s, Pack+Encode: 2.648s, Decode+Unpack: 2.535s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1834.1402 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388183-ILSVRC2012_val_00002799.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03388183-ILSVRC2012_val_00002799.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388549-ILSVRC2012_val_00002945.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388549-ILSVRC2012_val_00002945.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 634,960B, BPFP=1.2052 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 609,256B, BPFP=1.1564 +⌛️ [2/4] FRONTEND: Frontend time: 2.630s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.512s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09849939 0.78134674 + layer.39.0 10.79426799 1770.96635083 + ------------------------------------------------------------------------------------- + TOTAL 5.44638369 885.87384878 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1244216 +BPFP 1.1808 bits/point +EBPFP 1.1808 equivalent bits/point +MSE 885.873849 +---------------------- -------------------------------------------------------- +Time: 5.202s Load: 0.061s, Pack+Encode: 2.630s, Decode+Unpack: 2.512s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 885.8738 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388549-ILSVRC2012_val_00002945.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03388549-ILSVRC2012_val_00002945.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03393912-ILSVRC2012_val_00000047.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03393912-ILSVRC2012_val_00000047.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 621,824B, BPFP=1.1803 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 567,740B, BPFP=1.0776 +⌛️ [2/4] FRONTEND: Frontend time: 2.639s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.520s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09729456 12.98874343 + layer.39.0 38.26720800 1800.45638970 + ------------------------------------------------------------------------------------- + TOTAL 19.18225128 906.72256657 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1189564 +BPFP 1.1289 bits/point +EBPFP 1.1289 equivalent bits/point +MSE 906.722567 +---------------------- -------------------------------------------------------- +Time: 5.210s Load: 0.051s, Pack+Encode: 2.639s, Decode+Unpack: 2.520s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 906.7226 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03393912-ILSVRC2012_val_00000047.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03393912-ILSVRC2012_val_00000047.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03394916-ILSVRC2012_val_00000957.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03394916-ILSVRC2012_val_00000957.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 608,472B, BPFP=1.1549 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 652,116B, BPFP=1.2378 +⌛️ [2/4] FRONTEND: Frontend time: 2.641s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.540s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10421823 12.61451500 + layer.39.0 9.72561820 2189.82871720 + ------------------------------------------------------------------------------------- + TOTAL 4.91491822 1101.22161610 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1260588 +BPFP 1.1963 bits/point +EBPFP 1.1963 equivalent bits/point +MSE 1101.221616 +---------------------- -------------------------------------------------------- +Time: 5.231s Load: 0.050s, Pack+Encode: 2.641s, Decode+Unpack: 2.540s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1101.2216 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03394916-ILSVRC2012_val_00000957.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03394916-ILSVRC2012_val_00000957.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03404251-ILSVRC2012_val_00000641.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03404251-ILSVRC2012_val_00000641.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 623,188B, BPFP=1.1829 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 722,228B, BPFP=1.3708 +⌛️ [2/4] FRONTEND: Frontend time: 2.648s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.525s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10764784 12.63466123 + layer.39.0 585.45553936 2768.26943635 + ------------------------------------------------------------------------------------- + TOTAL 292.78159360 1390.45204879 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1345416 +BPFP 1.2769 bits/point +EBPFP 1.2769 equivalent bits/point +MSE 1390.452049 +---------------------- -------------------------------------------------------- +Time: 5.225s Load: 0.052s, Pack+Encode: 2.648s, Decode+Unpack: 2.525s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1390.4520 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03404251-ILSVRC2012_val_00000641.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03404251-ILSVRC2012_val_00000641.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03417042-ILSVRC2012_val_00001144.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03417042-ILSVRC2012_val_00001144.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 628,144B, BPFP=1.1923 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 608,056B, BPFP=1.1541 +⌛️ [2/4] FRONTEND: Frontend time: 2.659s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 2.530s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10091509 0.79070802 + layer.39.0 202.93364310 2610.63799806 + ------------------------------------------------------------------------------------- + TOTAL 101.51727910 1305.71435304 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture elic-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1236200 +BPFP 1.1732 bits/point +EBPFP 1.1732 equivalent bits/point +MSE 1305.714353 +---------------------- -------------------------------------------------------- +Time: 5.259s Load: 0.070s, Pack+Encode: 2.659s, Decode+Unpack: 2.530s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1305.7144 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03417042-ILSVRC2012_val_00001144.zst + to output-fixed/dinov3-total/lambda0.02/elic-featurecoding-8bit-layerwise/cls_in1kval/n03417042-ILSVRC2012_val_00001144.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 1.2078 bits/point +Avg EBPFP 1.2078 equivalent bits/point +Avg MSE 1034.578033 +Avg Time 5.267s +------------------------ ---------------------------- diff --git a/lambda0.02/hyperprior-featurecoding-8bit-individual/cls_in1ka/dtufc_hyperprior-featurecoding_dinov3-total_individual.log b/lambda0.02/hyperprior-featurecoding-8bit-individual/cls_in1ka/dtufc_hyperprior-featurecoding_dinov3-total_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..93e391a03bd09f9e667592e5d1da0001d6a970c1 --- /dev/null +++ b/lambda0.02/hyperprior-featurecoding-8bit-individual/cls_in1ka/dtufc_hyperprior-featurecoding_dinov3-total_individual.log @@ -0,0 +1,9344 @@ +Experiment: dtufc_hyperprior-featurecoding_dinov3-total_individual +Log file: output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/dtufc_hyperprior-featurecoding_dinov3-total_individual.log +DTUFCCodecConfig: + arch: hyperprior-featurecoding + handler: dinov3-total + checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.02_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.02_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 598 +Loaded hyperprior-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.9' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.19' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.29' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.39' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json +Loaded per-key mappings: model=dinov3-total + Keys: ['layer.9', 'layer.19', 'layer.29', 'layer.39'] +---------------- ----------------------------------------------------------------------------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +Checkpoint codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.02_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-DINOv3/imagenet1k-a +Output output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka +---------------- ----------------------------------------------------------------------------------------------------------------------------- +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01498041-0.006639_koala _ American bullfrog_0.6658246.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01498041-0.006639_koala _ American bullfrog_0.6658246.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.086s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 737,248B, BPFP=1.3994 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 648,192B, BPFP=1.2303 +⌛️ [2/4] FRONTEND: Frontend time: 0.777s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.074s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09594801 62.87544035 + layer.39.0 58.94484178 1853.78036929 + ------------------------------------------------------------------------------------- + TOTAL 29.52039490 958.32790482 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1385440 +BPFP 1.3148 bits/point +EBPFP 1.3148 equivalent bits/point +MSE 958.327905 +---------------------- -------------------------------------------------------- +Time: 1.938s Load: 0.086s, Pack+Encode: 0.777s, Decode+Unpack: 1.074s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 958.3279 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01498041-0.006639_koala _ American bullfrog_0.6658246.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01498041-0.006639_koala _ American bullfrog_0.6658246.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01531178-0.000765_fox squirrel _ ant_0.9486805.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01531178-0.000765_fox squirrel _ ant_0.9486805.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 729,404B, BPFP=1.3845 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 680,296B, BPFP=1.2913 +⌛️ [2/4] FRONTEND: Frontend time: 0.637s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.100s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09773727 37.59633139 + layer.39.0 17.17825445 1304.34135083 + ------------------------------------------------------------------------------------- + TOTAL 8.63799586 670.96884111 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1409700 +BPFP 1.3379 bits/point +EBPFP 1.3379 equivalent bits/point +MSE 670.968841 +---------------------- -------------------------------------------------------- +Time: 1.808s Load: 0.071s, Pack+Encode: 0.637s, Decode+Unpack: 1.100s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 670.9688 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01531178-0.000765_fox squirrel _ ant_0.9486805.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01531178-0.000765_fox squirrel _ ant_0.9486805.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01534433-0.004573_stingray _ stingray_0.97124094.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01534433-0.004573_stingray _ stingray_0.97124094.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 631,608B, BPFP=1.1988 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 510,336B, BPFP=0.9687 +⌛️ [2/4] FRONTEND: Frontend time: 0.556s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.050s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09515371 12.67173075 + layer.39.0 6.87362484 926.03486395 + ------------------------------------------------------------------------------------- + TOTAL 3.48438928 469.35329735 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1141944 +BPFP 1.0838 bits/point +EBPFP 1.0838 equivalent bits/point +MSE 469.353297 +---------------------- -------------------------------------------------------- +Time: 1.657s Load: 0.051s, Pack+Encode: 0.556s, Decode+Unpack: 1.050s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 469.3533 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01534433-0.004573_stingray _ stingray_0.97124094.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01534433-0.004573_stingray _ stingray_0.97124094.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01558993-0.000522_bow _ bow_0.9033333.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01558993-0.000522_bow _ bow_0.9033333.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 735,744B, BPFP=1.3965 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 530,092B, BPFP=1.0062 +⌛️ [2/4] FRONTEND: Frontend time: 0.531s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.049s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09874929 308.78498542 + layer.39.0 7.31778236 895.81936346 + ------------------------------------------------------------------------------------- + TOTAL 3.70826583 602.30217444 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1265836 +BPFP 1.2013 bits/point +EBPFP 1.2013 equivalent bits/point +MSE 602.302174 +---------------------- -------------------------------------------------------- +Time: 1.631s Load: 0.051s, Pack+Encode: 0.531s, Decode+Unpack: 1.049s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 602.3022 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01558993-0.000522_bow _ bow_0.9033333.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01558993-0.000522_bow _ bow_0.9033333.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01580077-0.004583_African bush elephant _ African bush elephant_0.59939015.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01580077-0.004583_African bush elephant _ African bush elephant_0.59939015.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 716,904B, BPFP=1.3607 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 633,900B, BPFP=1.2032 +⌛️ [2/4] FRONTEND: Frontend time: 0.540s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.056s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10720986 234.90259050 + layer.39.0 24.46209533 1057.51737123 + ------------------------------------------------------------------------------------- + TOTAL 12.28465260 646.20998087 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1350804 +BPFP 1.2820 bits/point +EBPFP 1.2820 equivalent bits/point +MSE 646.209981 +---------------------- -------------------------------------------------------- +Time: 1.647s Load: 0.051s, Pack+Encode: 0.540s, Decode+Unpack: 1.056s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 646.2100 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01580077-0.004583_African bush elephant _ African bush elephant_0.59939015.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01580077-0.004583_African bush elephant _ African bush elephant_0.59939015.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01614925-0.049724_white-headed capuchin _ white-headed capuchin_0.9309422.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01614925-0.049724_white-headed capuchin _ white-headed capuchin_0.9309422.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 761,160B, BPFP=1.4447 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 558,660B, BPFP=1.0604 +⌛️ [2/4] FRONTEND: Frontend time: 0.551s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.028s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09739119 298.77459913 + layer.39.0 8.81423010 906.95918367 + ------------------------------------------------------------------------------------- + TOTAL 4.45581065 602.86689140 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1319820 +BPFP 1.2526 bits/point +EBPFP 1.2526 equivalent bits/point +MSE 602.866891 +---------------------- -------------------------------------------------------- +Time: 1.628s Load: 0.050s, Pack+Encode: 0.551s, Decode+Unpack: 1.028s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 602.8669 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01614925-0.049724_white-headed capuchin _ white-headed capuchin_0.9309422.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01614925-0.049724_white-headed capuchin _ white-headed capuchin_0.9309422.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01631663-0.004606_American bullfrog _ American bullfrog_0.8789855.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01631663-0.004606_American bullfrog _ American bullfrog_0.8789855.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 695,740B, BPFP=1.3206 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 632,256B, BPFP=1.2001 +⌛️ [2/4] FRONTEND: Frontend time: 0.545s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.039s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09716670 38.14584852 + layer.39.0 20.45897868 1079.67844995 + ------------------------------------------------------------------------------------- + TOTAL 10.27807269 558.91214923 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1327996 +BPFP 1.2603 bits/point +EBPFP 1.2603 equivalent bits/point +MSE 558.912149 +---------------------- -------------------------------------------------------- +Time: 1.643s Load: 0.060s, Pack+Encode: 0.545s, Decode+Unpack: 1.039s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 558.9121 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01631663-0.004606_American bullfrog _ American bullfrog_0.8789855.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01631663-0.004606_American bullfrog _ American bullfrog_0.8789855.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01669191-0.029754_sandal _ sandal_0.38198605.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01669191-0.029754_sandal _ sandal_0.38198605.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 818,092B, BPFP=1.5528 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 550,876B, BPFP=1.0456 +⌛️ [2/4] FRONTEND: Frontend time: 0.563s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.097s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10877632 916.01949708 + layer.39.0 13.16500205 955.83017493 + ------------------------------------------------------------------------------------- + TOTAL 6.63688918 935.92483601 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1368968 +BPFP 1.2992 bits/point +EBPFP 1.2992 equivalent bits/point +MSE 935.924836 +---------------------- -------------------------------------------------------- +Time: 1.710s Load: 0.050s, Pack+Encode: 0.563s, Decode+Unpack: 1.097s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 935.9248 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01669191-0.029754_sandal _ sandal_0.38198605.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01669191-0.029754_sandal _ sandal_0.38198605.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770081-0.000571_syringe _ syringe_0.7369336.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770081-0.000571_syringe _ syringe_0.7369336.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 652,660B, BPFP=1.2388 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 636,676B, BPFP=1.2085 +⌛️ [2/4] FRONTEND: Frontend time: 0.527s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.029s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09508557 12.45005106 + layer.39.0 60.03878538 1163.57495141 + ------------------------------------------------------------------------------------- + TOTAL 30.06693547 588.01250123 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1289336 +BPFP 1.2236 bits/point +EBPFP 1.2236 equivalent bits/point +MSE 588.012501 +---------------------- -------------------------------------------------------- +Time: 1.615s Load: 0.059s, Pack+Encode: 0.527s, Decode+Unpack: 1.029s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 588.0125 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770081-0.000571_syringe _ syringe_0.7369336.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01770081-0.000571_syringe _ syringe_0.7369336.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770393-0.000908_harvestman _ harvestman_0.8782497.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770393-0.000908_harvestman _ harvestman_0.8782497.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.053s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 720,976B, BPFP=1.3685 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 683,608B, BPFP=1.2975 +⌛️ [2/4] FRONTEND: Frontend time: 0.519s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.048s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11350316 173.62421040 + layer.39.0 19.73148992 1269.34086492 + ------------------------------------------------------------------------------------- + TOTAL 9.92249654 721.48253766 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1404584 +BPFP 1.3330 bits/point +EBPFP 1.3330 equivalent bits/point +MSE 721.482538 +---------------------- -------------------------------------------------------- +Time: 1.620s Load: 0.053s, Pack+Encode: 0.519s, Decode+Unpack: 1.048s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 721.4825 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01770393-0.000908_harvestman _ harvestman_0.8782497.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01770393-0.000908_harvestman _ harvestman_0.8782497.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01784675-0.027853_syringe _ syringe_0.9584382.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01784675-0.027853_syringe _ syringe_0.9584382.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 804,928B, BPFP=1.5278 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 558,144B, BPFP=1.0594 +⌛️ [2/4] FRONTEND: Frontend time: 0.616s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.069s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11002613 498.93683188 + layer.39.0 26.08665877 1733.36977648 + ------------------------------------------------------------------------------------- + TOTAL 13.09834245 1116.15330418 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1363072 +BPFP 1.2936 bits/point +EBPFP 1.2936 equivalent bits/point +MSE 1116.153304 +---------------------- -------------------------------------------------------- +Time: 1.755s Load: 0.070s, Pack+Encode: 0.616s, Decode+Unpack: 1.069s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1116.1533 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01784675-0.027853_syringe _ syringe_0.9584382.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01784675-0.027853_syringe _ syringe_0.9584382.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01819313-0.053742_koala _ koala_0.98647016.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01819313-0.053742_koala _ koala_0.98647016.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 795,932B, BPFP=1.5107 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 665,188B, BPFP=1.2626 +⌛️ [2/4] FRONTEND: Frontend time: 0.565s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.068s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14565475 519.41660593 + layer.39.0 25.01023445 1503.28717201 + ------------------------------------------------------------------------------------- + TOTAL 12.57794460 1011.35188897 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1461120 +BPFP 1.3867 bits/point +EBPFP 1.3867 equivalent bits/point +MSE 1011.351889 +---------------------- -------------------------------------------------------- +Time: 1.683s Load: 0.051s, Pack+Encode: 0.565s, Decode+Unpack: 1.068s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1011.3519 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01819313-0.053742_koala _ koala_0.98647016.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01819313-0.053742_koala _ koala_0.98647016.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01820546-0.012522_toucan _ toucan_0.63882655.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01820546-0.012522_toucan _ toucan_0.63882655.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 720,108B, BPFP=1.3668 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 662,452B, BPFP=1.2574 +⌛️ [2/4] FRONTEND: Frontend time: 0.526s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.054s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09696376 100.20376276 + layer.39.0 16.65489097 1190.36175899 + ------------------------------------------------------------------------------------- + TOTAL 8.37592737 645.28276087 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1382560 +BPFP 1.3121 bits/point +EBPFP 1.3121 equivalent bits/point +MSE 645.282761 +---------------------- -------------------------------------------------------- +Time: 1.633s Load: 0.052s, Pack+Encode: 0.526s, Decode+Unpack: 1.054s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 645.2828 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01820546-0.012522_toucan _ toucan_0.63882655.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01820546-0.012522_toucan _ toucan_0.63882655.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01833805-0.013248_toucan _ lorikeet_0.77773976.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01833805-0.013248_toucan _ lorikeet_0.77773976.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 702,544B, BPFP=1.3335 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 685,632B, BPFP=1.3014 +⌛️ [2/4] FRONTEND: Frontend time: 0.589s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.101s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09866240 14.73869124 + layer.39.0 7.67772963 1062.84159378 + ------------------------------------------------------------------------------------- + TOTAL 3.88819601 538.79014251 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1388176 +BPFP 1.3174 bits/point +EBPFP 1.3174 equivalent bits/point +MSE 538.790143 +---------------------- -------------------------------------------------------- +Time: 1.740s Load: 0.050s, Pack+Encode: 0.589s, Decode+Unpack: 1.101s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 538.7901 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01833805-0.013248_toucan _ lorikeet_0.77773976.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01833805-0.013248_toucan _ lorikeet_0.77773976.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01843383-0.013605_white-headed capuchin _ white-headed capuchin_0.9984768.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01843383-0.013605_white-headed capuchin _ white-headed capuchin_0.9984768.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 772,024B, BPFP=1.4654 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 661,836B, BPFP=1.2562 +⌛️ [2/4] FRONTEND: Frontend time: 0.608s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.102s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11910487 335.94852405 + layer.39.0 9.20068692 1284.01178328 + ------------------------------------------------------------------------------------- + TOTAL 4.65989589 809.98015367 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1433860 +BPFP 1.3608 bits/point +EBPFP 1.3608 equivalent bits/point +MSE 809.980154 +---------------------- -------------------------------------------------------- +Time: 1.762s Load: 0.052s, Pack+Encode: 0.608s, Decode+Unpack: 1.102s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 809.9802 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01843383-0.013605_white-headed capuchin _ white-headed capuchin_0.9984768.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01843383-0.013605_white-headed capuchin _ white-headed capuchin_0.9984768.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01924916-0.000644_jay _ jay_0.82223135.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01924916-0.000644_jay _ jay_0.82223135.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 781,040B, BPFP=1.4825 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 569,648B, BPFP=1.0812 +⌛️ [2/4] FRONTEND: Frontend time: 0.589s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.072s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11488669 431.63869655 + layer.39.0 141.08750911 1006.12293489 + ------------------------------------------------------------------------------------- + TOTAL 70.60119790 718.88081572 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1350688 +BPFP 1.2819 bits/point +EBPFP 1.2819 equivalent bits/point +MSE 718.880816 +---------------------- -------------------------------------------------------- +Time: 1.720s Load: 0.060s, Pack+Encode: 0.589s, Decode+Unpack: 1.072s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 718.8808 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01924916-0.000644_jay _ jay_0.82223135.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01924916-0.000644_jay _ jay_0.82223135.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01944390-0.002567_American robin _ American robin_0.5629079.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01944390-0.002567_American robin _ American robin_0.5629079.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.053s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 733,840B, BPFP=1.3929 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 668,632B, BPFP=1.2691 +⌛️ [2/4] FRONTEND: Frontend time: 0.602s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.060s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10732387 162.78179665 + layer.39.0 16.74672581 1194.72898445 + ------------------------------------------------------------------------------------- + TOTAL 8.42702484 678.75539055 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1402472 +BPFP 1.3310 bits/point +EBPFP 1.3310 equivalent bits/point +MSE 678.755391 +---------------------- -------------------------------------------------------- +Time: 1.715s Load: 0.053s, Pack+Encode: 0.602s, Decode+Unpack: 1.060s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 678.7554 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01944390-0.002567_American robin _ American robin_0.5629079.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01944390-0.002567_American robin _ American robin_0.5629079.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01985128-0.001579_centipede _ centipede_0.85936093.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01985128-0.001579_centipede _ centipede_0.85936093.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 726,888B, BPFP=1.3797 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 566,704B, BPFP=1.0756 +⌛️ [2/4] FRONTEND: Frontend time: 0.507s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.027s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09645609 74.98069272 + layer.39.0 23.47999613 1287.41933916 + ------------------------------------------------------------------------------------- + TOTAL 11.78822611 681.20001594 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1293592 +BPFP 1.2277 bits/point +EBPFP 1.2277 equivalent bits/point +MSE 681.200016 +---------------------- -------------------------------------------------------- +Time: 1.584s Load: 0.050s, Pack+Encode: 0.507s, Decode+Unpack: 1.027s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 681.2000 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n01985128-0.001579_centipede _ centipede_0.85936093.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n01985128-0.001579_centipede _ centipede_0.85936093.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02037110-0.003503_snowmobile _ snowmobile_0.5200631.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02037110-0.003503_snowmobile _ snowmobile_0.5200631.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.049s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 553,892B, BPFP=1.0513 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 501,496B, BPFP=0.9519 +⌛️ [2/4] FRONTEND: Frontend time: 0.532s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.062s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09471867 12.63313802 + layer.39.0 17.04498261 1219.83187561 + ------------------------------------------------------------------------------------- + TOTAL 8.56985064 616.23250681 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1055388 +BPFP 1.0016 bits/point +EBPFP 1.0016 equivalent bits/point +MSE 616.232507 +---------------------- -------------------------------------------------------- +Time: 1.644s Load: 0.049s, Pack+Encode: 0.532s, Decode+Unpack: 1.062s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 616.2325 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02037110-0.003503_snowmobile _ snowmobile_0.5200631.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02037110-0.003503_snowmobile _ snowmobile_0.5200631.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02123394-0.015363_marmot _ marmot_0.82052565.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02123394-0.015363_marmot _ marmot_0.82052565.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 677,888B, BPFP=1.2867 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 640,680B, BPFP=1.2161 +⌛️ [2/4] FRONTEND: Frontend time: 0.525s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.038s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10209646 63.70989735 + layer.39.0 11.38238543 1230.36601069 + ------------------------------------------------------------------------------------- + TOTAL 5.74224095 647.03795402 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1318568 +BPFP 1.2514 bits/point +EBPFP 1.2514 equivalent bits/point +MSE 647.037954 +---------------------- -------------------------------------------------------- +Time: 1.624s Load: 0.060s, Pack+Encode: 0.525s, Decode+Unpack: 1.038s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 647.0380 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02123394-0.015363_marmot _ marmot_0.82052565.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02123394-0.015363_marmot _ marmot_0.82052565.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02165456-0.000157_corn _ corn_0.9868978.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02165456-0.000157_corn _ corn_0.9868978.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 743,324B, BPFP=1.4109 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 680,960B, BPFP=1.2925 +⌛️ [2/4] FRONTEND: Frontend time: 0.567s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.067s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10346756 318.97436832 + layer.39.0 776.17699223 2012.86127308 + ------------------------------------------------------------------------------------- + TOTAL 388.14022989 1165.91782070 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1424284 +BPFP 1.3517 bits/point +EBPFP 1.3517 equivalent bits/point +MSE 1165.917821 +---------------------- -------------------------------------------------------- +Time: 1.686s Load: 0.052s, Pack+Encode: 0.567s, Decode+Unpack: 1.067s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1165.9178 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02165456-0.000157_corn _ corn_0.9868978.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02165456-0.000157_corn _ corn_0.9868978.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02219486-0.000060_cliff _ cliff_0.99684334.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02219486-0.000060_cliff _ cliff_0.99684334.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 697,164B, BPFP=1.3233 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 511,308B, BPFP=0.9705 +⌛️ [2/4] FRONTEND: Frontend time: 0.597s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.097s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09584527 37.72222728 + layer.39.0 31.94620460 1077.22594752 + ------------------------------------------------------------------------------------- + TOTAL 16.02102494 557.47408740 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1208472 +BPFP 1.1469 bits/point +EBPFP 1.1469 equivalent bits/point +MSE 557.474087 +---------------------- -------------------------------------------------------- +Time: 1.745s Load: 0.050s, Pack+Encode: 0.597s, Decode+Unpack: 1.097s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 557.4741 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02219486-0.000060_cliff _ cliff_0.99684334.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02219486-0.000060_cliff _ cliff_0.99684334.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02226429-0.000085_stick insect _ stick insect_0.99900275.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02226429-0.000085_stick insect _ stick insect_0.99900275.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 711,524B, BPFP=1.3505 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 676,476B, BPFP=1.2840 +⌛️ [2/4] FRONTEND: Frontend time: 0.597s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.055s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09547379 75.04515914 + layer.39.0 19.16722850 1608.38058795 + ------------------------------------------------------------------------------------- + TOTAL 9.63135114 841.71287354 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1388000 +BPFP 1.3173 bits/point +EBPFP 1.3173 equivalent bits/point +MSE 841.712874 +---------------------- -------------------------------------------------------- +Time: 1.702s Load: 0.050s, Pack+Encode: 0.597s, Decode+Unpack: 1.055s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 841.7129 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02226429-0.000085_stick insect _ stick insect_0.99900275.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02226429-0.000085_stick insect _ stick insect_0.99900275.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02231487-0.000080_manhole cover _ manhole cover_0.7554716.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02231487-0.000080_manhole cover _ manhole cover_0.7554716.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 690,528B, BPFP=1.3107 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 679,504B, BPFP=1.2898 +⌛️ [2/4] FRONTEND: Frontend time: 0.570s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.057s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09512618 13.42968750 + layer.39.0 210.79875790 1561.75923226 + ------------------------------------------------------------------------------------- + TOTAL 105.44694204 787.59445988 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1370032 +BPFP 1.3002 bits/point +EBPFP 1.3002 equivalent bits/point +MSE 787.594460 +---------------------- -------------------------------------------------------- +Time: 1.697s Load: 0.070s, Pack+Encode: 0.570s, Decode+Unpack: 1.057s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 787.5945 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02231487-0.000080_manhole cover _ manhole cover_0.7554716.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02231487-0.000080_manhole cover _ manhole cover_0.7554716.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02233338-0.002901_manhole cover _ manhole cover_0.69458175.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02233338-0.002901_manhole cover _ manhole cover_0.69458175.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 650,852B, BPFP=1.2354 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 617,600B, BPFP=1.1723 +⌛️ [2/4] FRONTEND: Frontend time: 0.600s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.100s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09539769 12.95133606 + layer.39.0 58.97704841 1109.36175899 + ------------------------------------------------------------------------------------- + TOTAL 29.53622305 561.15654752 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1268452 +BPFP 1.2038 bits/point +EBPFP 1.2038 equivalent bits/point +MSE 561.156548 +---------------------- -------------------------------------------------------- +Time: 1.771s Load: 0.071s, Pack+Encode: 0.600s, Decode+Unpack: 1.100s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 561.1565 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02233338-0.002901_manhole cover _ manhole cover_0.69458175.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02233338-0.002901_manhole cover _ manhole cover_0.69458175.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02236044-0.000522_sundial _ sundial_0.96381366.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02236044-0.000522_sundial _ sundial_0.96381366.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 743,028B, BPFP=1.4103 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 706,284B, BPFP=1.3406 +⌛️ [2/4] FRONTEND: Frontend time: 0.601s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.106s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09795647 149.24202806 + layer.39.0 53.12385356 1361.06049563 + ------------------------------------------------------------------------------------- + TOTAL 26.61090502 755.15126184 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1449312 +BPFP 1.3755 bits/point +EBPFP 1.3755 equivalent bits/point +MSE 755.151262 +---------------------- -------------------------------------------------------- +Time: 1.767s Load: 0.060s, Pack+Encode: 0.601s, Decode+Unpack: 1.106s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 755.1513 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02236044-0.000522_sundial _ sundial_0.96381366.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02236044-0.000522_sundial _ sundial_0.96381366.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02259212-0.000032_chain _ chain_0.6590295.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02259212-0.000032_chain _ chain_0.6590295.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 692,520B, BPFP=1.3145 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 690,760B, BPFP=1.3111 +⌛️ [2/4] FRONTEND: Frontend time: 0.625s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.058s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09523673 24.97148703 + layer.39.0 80.66082058 1647.73420797 + ------------------------------------------------------------------------------------- + TOTAL 40.37802865 836.35284750 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1383280 +BPFP 1.3128 bits/point +EBPFP 1.3128 equivalent bits/point +MSE 836.352848 +---------------------- -------------------------------------------------------- +Time: 1.733s Load: 0.051s, Pack+Encode: 0.625s, Decode+Unpack: 1.058s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 836.3528 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02259212-0.000032_chain _ chain_0.6590295.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02259212-0.000032_chain _ chain_0.6590295.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02279972-0.000576_apron _ apron_0.7661352.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02279972-0.000576_apron _ apron_0.7661352.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 684,924B, BPFP=1.3000 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 610,476B, BPFP=1.1587 +⌛️ [2/4] FRONTEND: Frontend time: 0.621s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.062s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12772729 336.24107143 + layer.39.0 1038.59135083 2242.09863946 + ------------------------------------------------------------------------------------- + TOTAL 519.35953906 1289.16985544 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1295400 +BPFP 1.2294 bits/point +EBPFP 1.2294 equivalent bits/point +MSE 1289.169855 +---------------------- -------------------------------------------------------- +Time: 1.733s Load: 0.051s, Pack+Encode: 0.621s, Decode+Unpack: 1.062s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1289.1699 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02279972-0.000576_apron _ apron_0.7661352.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02279972-0.000576_apron _ apron_0.7661352.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02280649-0.001599_custard apple _ leafhopper_0.9128452.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02280649-0.001599_custard apple _ leafhopper_0.9128452.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 679,800B, BPFP=1.2903 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 667,784B, BPFP=1.2675 +⌛️ [2/4] FRONTEND: Frontend time: 0.520s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.022s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09488542 12.64212353 + layer.39.0 1031.59973275 2551.70651118 + ------------------------------------------------------------------------------------- + TOTAL 515.84730909 1282.17431736 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1347584 +BPFP 1.2789 bits/point +EBPFP 1.2789 equivalent bits/point +MSE 1282.174317 +---------------------- -------------------------------------------------------- +Time: 1.592s Load: 0.050s, Pack+Encode: 0.520s, Decode+Unpack: 1.022s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1282.1743 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02280649-0.001599_custard apple _ leafhopper_0.9128452.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02280649-0.001599_custard apple _ leafhopper_0.9128452.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02317335-0.033681_flatworm _ flatworm_0.6070924.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02317335-0.033681_flatworm _ flatworm_0.6070924.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 676,204B, BPFP=1.2835 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 555,024B, BPFP=1.0535 +⌛️ [2/4] FRONTEND: Frontend time: 0.574s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.055s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09575805 13.05368911 + layer.39.0 62.35741238 1376.84560253 + ------------------------------------------------------------------------------------- + TOTAL 31.22658522 694.94964582 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1231228 +BPFP 1.1685 bits/point +EBPFP 1.1685 equivalent bits/point +MSE 694.949646 +---------------------- -------------------------------------------------------- +Time: 1.700s Load: 0.071s, Pack+Encode: 0.574s, Decode+Unpack: 1.055s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 694.9496 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02317335-0.033681_flatworm _ flatworm_0.6070924.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02317335-0.033681_flatworm _ flatworm_0.6070924.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02325366-0.000350_flatworm _ flatworm_0.87634724.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02325366-0.000350_flatworm _ flatworm_0.87634724.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 626,056B, BPFP=1.1883 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 521,324B, BPFP=0.9895 +⌛️ [2/4] FRONTEND: Frontend time: 0.597s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.074s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09712043 12.79935010 + layer.39.0 30.59439155 1198.04616132 + ------------------------------------------------------------------------------------- + TOTAL 15.34575599 605.42275571 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1147380 +BPFP 1.0889 bits/point +EBPFP 1.0889 equivalent bits/point +MSE 605.422756 +---------------------- -------------------------------------------------------- +Time: 1.740s Load: 0.070s, Pack+Encode: 0.597s, Decode+Unpack: 1.074s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 605.4228 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02325366-0.000350_flatworm _ flatworm_0.87634724.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02325366-0.000350_flatworm _ flatworm_0.87634724.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02346627-0.011107_fountain _ skunk_0.28641737.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02346627-0.011107_fountain _ skunk_0.28641737.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.091s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 585,300B, BPFP=1.1109 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 453,080B, BPFP=0.8600 +⌛️ [2/4] FRONTEND: Frontend time: 0.606s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.026s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09705289 36.50486288 + layer.39.0 9.52721088 1173.03061224 + ------------------------------------------------------------------------------------- + TOTAL 4.81213189 604.76773756 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1038380 +BPFP 0.9855 bits/point +EBPFP 0.9855 equivalent bits/point +MSE 604.767738 +---------------------- -------------------------------------------------------- +Time: 1.722s Load: 0.091s, Pack+Encode: 0.606s, Decode+Unpack: 1.026s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 604.7677 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02346627-0.011107_fountain _ skunk_0.28641737.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02346627-0.011107_fountain _ skunk_0.28641737.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02445715-0.036432_shovel _ tarantula_0.26785344.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02445715-0.036432_shovel _ tarantula_0.26785344.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 742,604B, BPFP=1.4095 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 496,748B, BPFP=0.9429 +⌛️ [2/4] FRONTEND: Frontend time: 0.556s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.067s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09708806 222.55659317 + layer.39.0 8.00606437 944.62147716 + ------------------------------------------------------------------------------------- + TOTAL 4.05157622 583.58903517 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1239352 +BPFP 1.1762 bits/point +EBPFP 1.1762 equivalent bits/point +MSE 583.589035 +---------------------- -------------------------------------------------------- +Time: 1.673s Load: 0.051s, Pack+Encode: 0.556s, Decode+Unpack: 1.067s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 583.5890 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02445715-0.036432_shovel _ tarantula_0.26785344.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02445715-0.036432_shovel _ tarantula_0.26785344.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02454379-0.082010_koala _ koala_0.7052893.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02454379-0.082010_koala _ koala_0.7052893.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 823,184B, BPFP=1.5625 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 584,976B, BPFP=1.1103 +⌛️ [2/4] FRONTEND: Frontend time: 0.533s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.037s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14585212 744.00467687 + layer.39.0 44.19989826 1077.69533528 + ------------------------------------------------------------------------------------- + TOTAL 22.17287519 910.85000607 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1408160 +BPFP 1.3364 bits/point +EBPFP 1.3364 equivalent bits/point +MSE 910.850006 +---------------------- -------------------------------------------------------- +Time: 1.640s Load: 0.070s, Pack+Encode: 0.533s, Decode+Unpack: 1.037s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 910.8500 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02454379-0.082010_koala _ koala_0.7052893.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02454379-0.082010_koala _ koala_0.7052893.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02782093-0.007542_American alligator _ American alligator_0.49872985.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02782093-0.007542_American alligator _ American alligator_0.49872985.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.068s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 627,068B, BPFP=1.1902 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 594,504B, BPFP=1.1284 +⌛️ [2/4] FRONTEND: Frontend time: 0.515s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.045s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09848133 13.05365400 + layer.39.0 9.18780844 1074.98505831 + ------------------------------------------------------------------------------------- + TOTAL 4.64314488 544.01935615 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1221572 +BPFP 1.1593 bits/point +EBPFP 1.1593 equivalent bits/point +MSE 544.019356 +---------------------- -------------------------------------------------------- +Time: 1.629s Load: 0.068s, Pack+Encode: 0.515s, Decode+Unpack: 1.045s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 544.0194 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02782093-0.007542_American alligator _ American alligator_0.49872985.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02782093-0.007542_American alligator _ American alligator_0.49872985.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02787622-0.004599_marimba _ accordion_0.25991488.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02787622-0.004599_marimba _ accordion_0.25991488.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 740,744B, BPFP=1.4060 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 720,776B, BPFP=1.3681 +⌛️ [2/4] FRONTEND: Frontend time: 0.560s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.054s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12856446 309.70550899 + layer.39.0 1004.59450923 2489.22934888 + ------------------------------------------------------------------------------------- + TOTAL 502.36153685 1399.46742894 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1461520 +BPFP 1.3870 bits/point +EBPFP 1.3870 equivalent bits/point +MSE 1399.467429 +---------------------- -------------------------------------------------------- +Time: 1.665s Load: 0.051s, Pack+Encode: 0.560s, Decode+Unpack: 1.054s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1399.4674 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02787622-0.004599_marimba _ accordion_0.25991488.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02787622-0.004599_marimba _ accordion_0.25991488.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02793495-0.000130_flatworm _ stingray_0.5528234.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02793495-0.000130_flatworm _ stingray_0.5528234.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 643,940B, BPFP=1.2223 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 462,388B, BPFP=0.8776 +⌛️ [2/4] FRONTEND: Frontend time: 0.526s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.023s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09706621 13.55146551 + layer.39.0 8.05872662 1014.98475462 + ------------------------------------------------------------------------------------- + TOTAL 4.07789641 514.26811006 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1106328 +BPFP 1.0499 bits/point +EBPFP 1.0499 equivalent bits/point +MSE 514.268110 +---------------------- -------------------------------------------------------- +Time: 1.598s Load: 0.050s, Pack+Encode: 0.526s, Decode+Unpack: 1.023s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 514.2681 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02793495-0.000130_flatworm _ stingray_0.5528234.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02793495-0.000130_flatworm _ stingray_0.5528234.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02797295-0.002775_hair dryer _ box turtle_0.2407761.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02797295-0.002775_hair dryer _ box turtle_0.2407761.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 750,884B, BPFP=1.4252 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 752,884B, BPFP=1.4290 +⌛️ [2/4] FRONTEND: Frontend time: 0.527s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.050s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11161610 235.66719813 + layer.39.0 373.09438776 2138.45189504 + ------------------------------------------------------------------------------------- + TOTAL 186.60300193 1187.05954659 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1503768 +BPFP 1.4271 bits/point +EBPFP 1.4271 equivalent bits/point +MSE 1187.059547 +---------------------- -------------------------------------------------------- +Time: 1.627s Load: 0.050s, Pack+Encode: 0.527s, Decode+Unpack: 1.050s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1187.0595 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02797295-0.002775_hair dryer _ box turtle_0.2407761.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02797295-0.002775_hair dryer _ box turtle_0.2407761.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02814860-0.006340_fountain _ fountain_0.7891514.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02814860-0.006340_fountain _ fountain_0.7891514.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.079s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 530,860B, BPFP=1.0076 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 557,264B, BPFP=1.0577 +⌛️ [2/4] FRONTEND: Frontend time: 0.603s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.085s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.04615183 12.81082020 + layer.39.0 7.48662090 1132.29786200 + ------------------------------------------------------------------------------------- + TOTAL 7.76638637 572.55434110 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1088124 +BPFP 1.0327 bits/point +EBPFP 1.0327 equivalent bits/point +MSE 572.554341 +---------------------- -------------------------------------------------------- +Time: 1.767s Load: 0.079s, Pack+Encode: 0.603s, Decode+Unpack: 1.085s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 572.5543 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02814860-0.006340_fountain _ fountain_0.7891514.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02814860-0.006340_fountain _ fountain_0.7891514.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02879718-0.003578_maraca _ maraca_0.6809677.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02879718-0.003578_maraca _ maraca_0.6809677.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 695,260B, BPFP=1.3197 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 761,836B, BPFP=1.4460 +⌛️ [2/4] FRONTEND: Frontend time: 0.587s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.047s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10989876 148.84274781 + layer.39.0 33.03751367 1606.30721574 + ------------------------------------------------------------------------------------- + TOTAL 16.57370621 877.57498178 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1457096 +BPFP 1.3828 bits/point +EBPFP 1.3828 equivalent bits/point +MSE 877.574982 +---------------------- -------------------------------------------------------- +Time: 1.685s Load: 0.050s, Pack+Encode: 0.587s, Decode+Unpack: 1.047s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 877.5750 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02879718-0.003578_maraca _ maraca_0.6809677.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02879718-0.003578_maraca _ maraca_0.6809677.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02883205-0.000262_syringe _ syringe_0.7098205.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02883205-0.000262_syringe _ syringe_0.7098205.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 635,904B, BPFP=1.2070 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 618,404B, BPFP=1.1738 +⌛️ [2/4] FRONTEND: Frontend time: 0.579s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.050s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09610580 12.86207882 + layer.39.0 8.14318931 1115.87718659 + ------------------------------------------------------------------------------------- + TOTAL 4.11964755 564.36963270 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1254308 +BPFP 1.1904 bits/point +EBPFP 1.1904 equivalent bits/point +MSE 564.369633 +---------------------- -------------------------------------------------------- +Time: 1.681s Load: 0.052s, Pack+Encode: 0.579s, Decode+Unpack: 1.050s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 564.3696 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02883205-0.000262_syringe _ syringe_0.7098205.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02883205-0.000262_syringe _ syringe_0.7098205.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02906734-0.000163_stethoscope _ stethoscope_0.9290994.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02906734-0.000163_stethoscope _ stethoscope_0.9290994.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 691,536B, BPFP=1.3126 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 673,700B, BPFP=1.2787 +⌛️ [2/4] FRONTEND: Frontend time: 0.556s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.056s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12024398 246.61951834 + layer.39.0 47.23105336 1522.79689018 + ------------------------------------------------------------------------------------- + TOTAL 23.67564867 884.70820426 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1365236 +BPFP 1.2957 bits/point +EBPFP 1.2957 equivalent bits/point +MSE 884.708204 +---------------------- -------------------------------------------------------- +Time: 1.661s Load: 0.050s, Pack+Encode: 0.556s, Decode+Unpack: 1.056s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 884.7082 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02906734-0.000163_stethoscope _ stethoscope_0.9290994.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02906734-0.000163_stethoscope _ stethoscope_0.9290994.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02948072-0.002303_digital clock _ digital clock_0.928374.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02948072-0.002303_digital clock _ digital clock_0.928374.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 668,608B, BPFP=1.2691 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 675,792B, BPFP=1.2827 +⌛️ [2/4] FRONTEND: Frontend time: 0.562s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.069s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09670976 13.16616083 + layer.39.0 81.62974520 1678.62585034 + ------------------------------------------------------------------------------------- + TOTAL 40.86322748 845.89600558 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1344400 +BPFP 1.2759 bits/point +EBPFP 1.2759 equivalent bits/point +MSE 845.896006 +---------------------- -------------------------------------------------------- +Time: 1.682s Load: 0.052s, Pack+Encode: 0.562s, Decode+Unpack: 1.069s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 845.8960 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02948072-0.002303_digital clock _ digital clock_0.928374.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02948072-0.002303_digital clock _ digital clock_0.928374.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02999410-0.000148_chest _ chest_0.9948565.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02999410-0.000148_chest _ chest_0.9948565.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 664,220B, BPFP=1.2607 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 568,656B, BPFP=1.0794 +⌛️ [2/4] FRONTEND: Frontend time: 0.574s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.057s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10256943 64.43219297 + layer.39.0 13.72598738 1029.50206511 + ------------------------------------------------------------------------------------- + TOTAL 6.91427841 546.96712904 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1232876 +BPFP 1.1700 bits/point +EBPFP 1.1700 equivalent bits/point +MSE 546.967129 +---------------------- -------------------------------------------------------- +Time: 1.681s Load: 0.051s, Pack+Encode: 0.574s, Decode+Unpack: 1.057s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 546.9671 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n02999410-0.000148_chest _ chest_0.9948565.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n02999410-0.000148_chest _ chest_0.9948565.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03026506-0.001828_basketball _ basketball_0.6904969.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03026506-0.001828_basketball _ basketball_0.6904969.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 652,912B, BPFP=1.2393 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 663,804B, BPFP=1.2600 +⌛️ [2/4] FRONTEND: Frontend time: 0.578s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.094s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09484169 24.53908718 + layer.39.0 87.31533194 1260.32288630 + ------------------------------------------------------------------------------------- + TOTAL 43.70508681 642.43098674 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1316716 +BPFP 1.2496 bits/point +EBPFP 1.2496 equivalent bits/point +MSE 642.430987 +---------------------- -------------------------------------------------------- +Time: 1.725s Load: 0.052s, Pack+Encode: 0.578s, Decode+Unpack: 1.094s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 642.4310 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03026506-0.001828_basketball _ basketball_0.6904969.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03026506-0.001828_basketball _ basketball_0.6904969.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03124043-0.001154_bow tie _ bow tie_0.9622156.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03124043-0.001154_bow tie _ bow tie_0.9622156.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.068s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 653,668B, BPFP=1.2407 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 640,788B, BPFP=1.2163 +⌛️ [2/4] FRONTEND: Frontend time: 0.547s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.044s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09893820 26.64213587 + layer.39.0 13.24554141 1382.45942663 + ------------------------------------------------------------------------------------- + TOTAL 6.67223981 704.55078125 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1294456 +BPFP 1.2285 bits/point +EBPFP 1.2285 equivalent bits/point +MSE 704.550781 +---------------------- -------------------------------------------------------- +Time: 1.659s Load: 0.068s, Pack+Encode: 0.547s, Decode+Unpack: 1.044s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 704.5508 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03124043-0.001154_bow tie _ bow tie_0.9622156.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03124043-0.001154_bow tie _ bow tie_0.9622156.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03196217-0.015958_soap dispenser _ envelope_0.80274254.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03196217-0.015958_soap dispenser _ envelope_0.80274254.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 520,432B, BPFP=0.9878 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 578,824B, BPFP=1.0987 +⌛️ [2/4] FRONTEND: Frontend time: 0.602s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.044s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10340443 98.98201379 + layer.39.0 8.70910111 1337.48894558 + ------------------------------------------------------------------------------------- + TOTAL 4.40625277 718.23547968 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1099256 +BPFP 1.0432 bits/point +EBPFP 1.0432 equivalent bits/point +MSE 718.235480 +---------------------- -------------------------------------------------------- +Time: 1.697s Load: 0.051s, Pack+Encode: 0.602s, Decode+Unpack: 1.044s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 718.2355 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03196217-0.015958_soap dispenser _ envelope_0.80274254.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03196217-0.015958_soap dispenser _ envelope_0.80274254.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03223299-0.001528_hot dog _ hot dog_0.9147216.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03223299-0.001528_hot dog _ hot dog_0.9147216.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 693,472B, BPFP=1.3163 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 619,428B, BPFP=1.1757 +⌛️ [2/4] FRONTEND: Frontend time: 0.521s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.031s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10130972 126.74347060 + layer.39.0 352.09596696 1451.16982507 + ------------------------------------------------------------------------------------- + TOTAL 176.09863834 788.95664784 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1312900 +BPFP 1.2460 bits/point +EBPFP 1.2460 equivalent bits/point +MSE 788.956648 +---------------------- -------------------------------------------------------- +Time: 1.601s Load: 0.050s, Pack+Encode: 0.521s, Decode+Unpack: 1.031s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 788.9566 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03223299-0.001528_hot dog _ hot dog_0.9147216.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03223299-0.001528_hot dog _ hot dog_0.9147216.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03255030-0.005469_bubble _ bubble_0.9381716.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03255030-0.005469_bubble _ bubble_0.9381716.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 686,488B, BPFP=1.3030 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 750,476B, BPFP=1.4245 +⌛️ [2/4] FRONTEND: Frontend time: 0.529s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 16.277s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09675161 37.28394148 + layer.39.0 42.23478499 1453.94727891 + ------------------------------------------------------------------------------------- + TOTAL 21.16576830 745.61561019 + (elements=8,429,568) +---------------------- --------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- --------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- --------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1436964 +BPFP 1.3637 bits/point +EBPFP 1.3637 equivalent bits/point +MSE 745.615610 +---------------------- --------------------------------------------------------- +Time: 16.866s Load: 0.060s, Pack+Encode: 0.529s, Decode+Unpack: 16.277s +---------------------- --------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 745.6156 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03255030-0.005469_bubble _ bubble_0.9381716.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03255030-0.005469_bubble _ bubble_0.9381716.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03325584-0.000773_candle _ candle_0.810919.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03325584-0.000773_candle _ candle_0.810919.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 735,140B, BPFP=1.3954 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 729,568B, BPFP=1.3848 +⌛️ [2/4] FRONTEND: Frontend time: 0.598s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.062s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10394677 176.33799502 + layer.39.0 140.58187561 2088.41107872 + ------------------------------------------------------------------------------------- + TOTAL 70.34291119 1132.37453687 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1464708 +BPFP 1.3901 bits/point +EBPFP 1.3901 equivalent bits/point +MSE 1132.374537 +---------------------- -------------------------------------------------------- +Time: 1.730s Load: 0.070s, Pack+Encode: 0.598s, Decode+Unpack: 1.062s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1132.3745 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03325584-0.000773_candle _ candle_0.810919.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03325584-0.000773_candle _ candle_0.810919.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03355925-0.004997_spider web _ spider web_0.9142101.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03355925-0.004997_spider web _ spider web_0.9142101.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 566,104B, BPFP=1.0745 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 456,160B, BPFP=0.8658 +⌛️ [2/4] FRONTEND: Frontend time: 0.586s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.042s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09873271 12.88637994 + layer.39.0 6.60211199 1135.64200680 + ------------------------------------------------------------------------------------- + TOTAL 3.35042235 574.26419337 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1022264 +BPFP 0.9702 bits/point +EBPFP 0.9702 equivalent bits/point +MSE 574.264193 +---------------------- -------------------------------------------------------- +Time: 1.698s Load: 0.070s, Pack+Encode: 0.586s, Decode+Unpack: 1.042s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 574.2642 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03355925-0.004997_spider web _ spider web_0.9142101.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03355925-0.004997_spider web _ spider web_0.9142101.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03384352-0.002273_viaduct _ viaduct_0.6161024.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03384352-0.002273_viaduct _ viaduct_0.6161024.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 687,308B, BPFP=1.3046 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 617,560B, BPFP=1.1722 +⌛️ [2/4] FRONTEND: Frontend time: 0.585s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.092s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09647940 86.92971028 + layer.39.0 175.50411504 1550.48372206 + ------------------------------------------------------------------------------------- + TOTAL 87.80029722 818.70671617 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1304868 +BPFP 1.2384 bits/point +EBPFP 1.2384 equivalent bits/point +MSE 818.706716 +---------------------- -------------------------------------------------------- +Time: 1.727s Load: 0.050s, Pack+Encode: 0.585s, Decode+Unpack: 1.092s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 818.7067 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03384352-0.002273_viaduct _ viaduct_0.6161024.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03384352-0.002273_viaduct _ viaduct_0.6161024.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03388043-0.005154_candle _ candle_0.9636924.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03388043-0.005154_candle _ candle_0.9636924.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 670,428B, BPFP=1.2725 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 638,824B, BPFP=1.2125 +⌛️ [2/4] FRONTEND: Frontend time: 0.600s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.056s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09640297 24.81891172 + layer.39.0 7.87377147 1099.45031584 + ------------------------------------------------------------------------------------- + TOTAL 3.98508722 562.13461378 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1309252 +BPFP 1.2425 bits/point +EBPFP 1.2425 equivalent bits/point +MSE 562.134614 +---------------------- -------------------------------------------------------- +Time: 1.706s Load: 0.050s, Pack+Encode: 0.600s, Decode+Unpack: 1.056s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 562.1346 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03388043-0.005154_candle _ candle_0.9636924.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03388043-0.005154_candle _ candle_0.9636924.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03417042-0.001187_tank _ tank_0.70379025.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03417042-0.001187_tank _ tank_0.70379025.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 650,532B, BPFP=1.2348 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 615,316B, BPFP=1.1679 +⌛️ [2/4] FRONTEND: Frontend time: 0.588s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.043s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09848782 39.27067389 + layer.39.0 16.63742104 1435.97886297 + ------------------------------------------------------------------------------------- + TOTAL 8.36795443 737.62476843 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1265848 +BPFP 1.2013 bits/point +EBPFP 1.2013 equivalent bits/point +MSE 737.624768 +---------------------- -------------------------------------------------------- +Time: 1.683s Load: 0.051s, Pack+Encode: 0.588s, Decode+Unpack: 1.043s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 737.6248 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03417042-0.001187_tank _ tank_0.70379025.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03417042-0.001187_tank _ tank_0.70379025.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03444034-0.002100_maraca _ maraca_0.502369.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03444034-0.002100_maraca _ maraca_0.502369.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 745,364B, BPFP=1.4148 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 801,156B, BPFP=1.5207 +⌛️ [2/4] FRONTEND: Frontend time: 0.622s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.091s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11197850 272.53741497 + layer.39.0 347.54634354 1962.45894072 + ------------------------------------------------------------------------------------- + TOTAL 173.82916102 1117.49817784 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1546520 +BPFP 1.4677 bits/point +EBPFP 1.4677 equivalent bits/point +MSE 1117.498178 +---------------------- -------------------------------------------------------- +Time: 1.763s Load: 0.051s, Pack+Encode: 0.622s, Decode+Unpack: 1.091s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1117.4982 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03444034-0.002100_maraca _ maraca_0.502369.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03444034-0.002100_maraca _ maraca_0.502369.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03445924-0.003505_baseball player _ baseball player_0.6723684.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03445924-0.003505_baseball player _ baseball player_0.6723684.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 654,308B, BPFP=1.2419 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 669,728B, BPFP=1.2712 +⌛️ [2/4] FRONTEND: Frontend time: 0.578s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.094s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09665277 73.92794886 + layer.39.0 26.28463618 1441.04227405 + ------------------------------------------------------------------------------------- + TOTAL 13.19064447 757.48511146 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1324036 +BPFP 1.2566 bits/point +EBPFP 1.2566 equivalent bits/point +MSE 757.485111 +---------------------- -------------------------------------------------------- +Time: 1.722s Load: 0.051s, Pack+Encode: 0.578s, Decode+Unpack: 1.094s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 757.4851 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03445924-0.003505_baseball player _ baseball player_0.6723684.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03445924-0.003505_baseball player _ baseball player_0.6723684.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03452741-0.002771_chain _ chain_0.9575044.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03452741-0.002771_chain _ chain_0.9575044.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 732,460B, BPFP=1.3903 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 738,668B, BPFP=1.4021 +⌛️ [2/4] FRONTEND: Frontend time: 0.524s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.083s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12351380 208.38034500 + layer.39.0 42.82565370 1542.09293003 + ------------------------------------------------------------------------------------- + TOTAL 21.47458375 875.23663751 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1471128 +BPFP 1.3962 bits/point +EBPFP 1.3962 equivalent bits/point +MSE 875.236638 +---------------------- -------------------------------------------------------- +Time: 1.657s Load: 0.050s, Pack+Encode: 0.524s, Decode+Unpack: 1.083s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 875.2366 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03452741-0.002771_chain _ chain_0.9575044.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03452741-0.002771_chain _ chain_0.9575044.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03483316-0.004974_lighter _ lighter_0.27796906.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03483316-0.004974_lighter _ lighter_0.27796906.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 737,332B, BPFP=1.3995 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 671,564B, BPFP=1.2747 +⌛️ [2/4] FRONTEND: Frontend time: 0.516s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.042s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12993333 532.21671526 + layer.39.0 87.07173986 1487.16642371 + ------------------------------------------------------------------------------------- + TOTAL 43.60083660 1009.69156948 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1408896 +BPFP 1.3371 bits/point +EBPFP 1.3371 equivalent bits/point +MSE 1009.691569 +---------------------- -------------------------------------------------------- +Time: 1.608s Load: 0.050s, Pack+Encode: 0.516s, Decode+Unpack: 1.042s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1009.6916 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03483316-0.004974_lighter _ lighter_0.27796906.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03483316-0.004974_lighter _ lighter_0.27796906.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03584829-0.104805_golf cart _ golf cart_0.73568964.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03584829-0.104805_golf cart _ golf cart_0.73568964.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 683,348B, BPFP=1.2970 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 655,372B, BPFP=1.2439 +⌛️ [2/4] FRONTEND: Frontend time: 0.594s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.059s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09917131 64.43202594 + layer.39.0 24.34873246 1469.15962099 + ------------------------------------------------------------------------------------- + TOTAL 12.22395189 766.79582346 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1338720 +BPFP 1.2705 bits/point +EBPFP 1.2705 equivalent bits/point +MSE 766.795823 +---------------------- -------------------------------------------------------- +Time: 1.705s Load: 0.051s, Pack+Encode: 0.594s, Decode+Unpack: 1.059s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 766.7958 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03584829-0.104805_golf cart _ golf cart_0.73568964.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03584829-0.104805_golf cart _ golf cart_0.73568964.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03590841-0.001115_rocking chair _ rocking chair_0.2192492.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03590841-0.001115_rocking chair _ rocking chair_0.2192492.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 670,364B, BPFP=1.2724 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 579,704B, BPFP=1.1003 +⌛️ [2/4] FRONTEND: Frontend time: 0.590s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.054s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11329899 161.82996234 + layer.39.0 19.97532495 1100.77308066 + ------------------------------------------------------------------------------------- + TOTAL 10.04431197 631.30152150 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1250068 +BPFP 1.1864 bits/point +EBPFP 1.1864 equivalent bits/point +MSE 631.301522 +---------------------- -------------------------------------------------------- +Time: 1.695s Load: 0.051s, Pack+Encode: 0.590s, Decode+Unpack: 1.054s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 631.3015 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03590841-0.001115_rocking chair _ rocking chair_0.2192492.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03590841-0.001115_rocking chair _ rocking chair_0.2192492.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03617480-0.003238_basketball _ basketball_0.67568874.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03617480-0.003238_basketball _ basketball_0.67568874.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 776,624B, BPFP=1.4741 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 771,356B, BPFP=1.4641 +⌛️ [2/4] FRONTEND: Frontend time: 0.523s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.041s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12967051 358.92544339 + layer.39.0 57.10576865 2017.38241011 + ------------------------------------------------------------------------------------- + TOTAL 28.61771958 1188.15392675 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1547980 +BPFP 1.4691 bits/point +EBPFP 1.4691 equivalent bits/point +MSE 1188.153927 +---------------------- -------------------------------------------------------- +Time: 1.614s Load: 0.050s, Pack+Encode: 0.523s, Decode+Unpack: 1.041s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1188.1539 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03617480-0.003238_basketball _ basketball_0.67568874.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03617480-0.003238_basketball _ basketball_0.67568874.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03666591-0.004622_torch _ torch_0.99906796.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03666591-0.004622_torch _ torch_0.99906796.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 592,228B, BPFP=1.1241 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 566,700B, BPFP=1.0756 +⌛️ [2/4] FRONTEND: Frontend time: 0.580s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.105s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.05477861 13.14704051 + layer.39.0 7.78975672 1087.13824101 + ------------------------------------------------------------------------------------- + TOTAL 7.92226767 550.14264076 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1158928 +BPFP 1.0999 bits/point +EBPFP 1.0999 equivalent bits/point +MSE 550.142641 +---------------------- -------------------------------------------------------- +Time: 1.755s Load: 0.070s, Pack+Encode: 0.580s, Decode+Unpack: 1.105s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 550.1426 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03666591-0.004622_torch _ torch_0.99906796.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03666591-0.004622_torch _ torch_0.99906796.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03670208-0.003899_hair dryer _ grand piano_0.7359066.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03670208-0.003899_hair dryer _ grand piano_0.7359066.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 672,432B, BPFP=1.2763 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 731,040B, BPFP=1.3876 +⌛️ [2/4] FRONTEND: Frontend time: 0.591s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.093s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11232473 162.30064079 + layer.39.0 36.60432231 1603.41375121 + ------------------------------------------------------------------------------------- + TOTAL 18.35832352 882.85719600 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1403472 +BPFP 1.3320 bits/point +EBPFP 1.3320 equivalent bits/point +MSE 882.857196 +---------------------- -------------------------------------------------------- +Time: 1.752s Load: 0.069s, Pack+Encode: 0.591s, Decode+Unpack: 1.093s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 882.8572 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03670208-0.003899_hair dryer _ grand piano_0.7359066.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03670208-0.003899_hair dryer _ grand piano_0.7359066.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03717622-0.001175_sundial _ sundial_0.9998197.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03717622-0.001175_sundial _ sundial_0.9998197.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 751,756B, BPFP=1.4269 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 717,652B, BPFP=1.3622 +⌛️ [2/4] FRONTEND: Frontend time: 0.576s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.046s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13381931 200.99230138 + layer.39.0 773.52204810 2390.53814383 + ------------------------------------------------------------------------------------- + TOTAL 386.82793371 1295.76522261 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1469408 +BPFP 1.3945 bits/point +EBPFP 1.3945 equivalent bits/point +MSE 1295.765223 +---------------------- -------------------------------------------------------- +Time: 1.672s Load: 0.050s, Pack+Encode: 0.576s, Decode+Unpack: 1.046s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1295.7652 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03717622-0.001175_sundial _ sundial_0.9998197.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03717622-0.001175_sundial _ sundial_0.9998197.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03720891-0.001854_African bush elephant _ African bush elephant_0.722115.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03720891-0.001854_African bush elephant _ African bush elephant_0.722115.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 706,984B, BPFP=1.3419 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 779,968B, BPFP=1.4804 +⌛️ [2/4] FRONTEND: Frontend time: 0.540s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.035s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09642763 39.14795918 + layer.39.0 155.23232507 1818.63544704 + ------------------------------------------------------------------------------------- + TOTAL 77.66437635 928.89170311 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1486952 +BPFP 1.4112 bits/point +EBPFP 1.4112 equivalent bits/point +MSE 928.891703 +---------------------- -------------------------------------------------------- +Time: 1.626s Load: 0.051s, Pack+Encode: 0.540s, Decode+Unpack: 1.035s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 928.8917 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03720891-0.001854_African bush elephant _ African bush elephant_0.722115.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03720891-0.001854_African bush elephant _ African bush elephant_0.722115.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03721384-0.003327_chain _ chain_0.5599652.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03721384-0.003327_chain _ chain_0.5599652.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 595,348B, BPFP=1.1300 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 574,484B, BPFP=1.0904 +⌛️ [2/4] FRONTEND: Frontend time: 0.540s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.035s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09561452 50.49378189 + layer.39.0 742.66502672 2161.68926142 + ------------------------------------------------------------------------------------- + TOTAL 371.38032062 1106.09152165 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1169832 +BPFP 1.1102 bits/point +EBPFP 1.1102 equivalent bits/point +MSE 1106.091522 +---------------------- -------------------------------------------------------- +Time: 1.636s Load: 0.060s, Pack+Encode: 0.540s, Decode+Unpack: 1.035s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1106.0915 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03721384-0.003327_chain _ chain_0.5599652.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03721384-0.003327_chain _ chain_0.5599652.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03724870-0.001366_Christmas stocking _ Christmas stocking_0.5709616.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03724870-0.001366_Christmas stocking _ Christmas stocking_0.5709616.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 704,844B, BPFP=1.3379 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 629,408B, BPFP=1.1947 +⌛️ [2/4] FRONTEND: Frontend time: 0.561s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.094s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10329660 124.95323888 + layer.39.0 513.92243683 1958.46076288 + ------------------------------------------------------------------------------------- + TOTAL 257.01286671 1041.70700088 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1334252 +BPFP 1.2663 bits/point +EBPFP 1.2663 equivalent bits/point +MSE 1041.707001 +---------------------- -------------------------------------------------------- +Time: 1.725s Load: 0.069s, Pack+Encode: 0.561s, Decode+Unpack: 1.094s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1041.7070 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03724870-0.001366_Christmas stocking _ Christmas stocking_0.5709616.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03724870-0.001366_Christmas stocking _ Christmas stocking_0.5709616.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03775071-0.000145_Christmas stocking _ Christmas stocking_0.9986047.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03775071-0.000145_Christmas stocking _ Christmas stocking_0.9986047.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 696,004B, BPFP=1.3211 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 707,536B, BPFP=1.3430 +⌛️ [2/4] FRONTEND: Frontend time: 0.543s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.080s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09700392 25.54004001 + layer.39.0 284.92189018 1720.49331876 + ------------------------------------------------------------------------------------- + TOTAL 142.50944705 873.01667938 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1403540 +BPFP 1.3320 bits/point +EBPFP 1.3320 equivalent bits/point +MSE 873.016679 +---------------------- -------------------------------------------------------- +Time: 1.674s Load: 0.051s, Pack+Encode: 0.543s, Decode+Unpack: 1.080s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 873.0167 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03775071-0.000145_Christmas stocking _ Christmas stocking_0.9986047.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03775071-0.000145_Christmas stocking _ Christmas stocking_0.9986047.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03788195-0.005975_steam locomotive _ steam locomotive_0.42419377.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03788195-0.005975_steam locomotive _ steam locomotive_0.42419377.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.049s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 758,720B, BPFP=1.4401 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 668,608B, BPFP=1.2691 +⌛️ [2/4] FRONTEND: Frontend time: 0.629s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.102s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10790903 259.52338435 + layer.39.0 10.34781284 1311.12184159 + ------------------------------------------------------------------------------------- + TOTAL 5.22786094 785.32261297 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1427328 +BPFP 1.3546 bits/point +EBPFP 1.3546 equivalent bits/point +MSE 785.322613 +---------------------- -------------------------------------------------------- +Time: 1.780s Load: 0.049s, Pack+Encode: 0.629s, Decode+Unpack: 1.102s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 785.3226 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03788195-0.005975_steam locomotive _ steam locomotive_0.42419377.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03788195-0.005975_steam locomotive _ steam locomotive_0.42419377.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03837869-0.002023_lighthouse _ lighthouse_0.5898798.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03837869-0.002023_lighthouse _ lighthouse_0.5898798.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 663,536B, BPFP=1.2594 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 577,776B, BPFP=1.0967 +⌛️ [2/4] FRONTEND: Frontend time: 0.576s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.076s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12703056 222.24436650 + layer.39.0 141.21340500 1444.25510204 + ------------------------------------------------------------------------------------- + TOTAL 70.67021778 833.24973427 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1241312 +BPFP 1.1781 bits/point +EBPFP 1.1781 equivalent bits/point +MSE 833.249734 +---------------------- -------------------------------------------------------- +Time: 1.702s Load: 0.050s, Pack+Encode: 0.576s, Decode+Unpack: 1.076s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 833.2497 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03837869-0.002023_lighthouse _ lighthouse_0.5898798.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03837869-0.002023_lighthouse _ lighthouse_0.5898798.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03840681-0.002188_ladybug _ ladybug_0.9972365.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03840681-0.002188_ladybug _ ladybug_0.9972365.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 614,236B, BPFP=1.1659 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 493,340B, BPFP=0.9364 +⌛️ [2/4] FRONTEND: Frontend time: 0.556s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.043s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09487485 12.61144106 + layer.39.0 29.40353574 1207.53255588 + ------------------------------------------------------------------------------------- + TOTAL 14.74920530 610.07199847 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1107576 +BPFP 1.0511 bits/point +EBPFP 1.0511 equivalent bits/point +MSE 610.071998 +---------------------- -------------------------------------------------------- +Time: 1.649s Load: 0.051s, Pack+Encode: 0.556s, Decode+Unpack: 1.043s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 610.0720 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03840681-0.002188_ladybug _ ladybug_0.9972365.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03840681-0.002188_ladybug _ ladybug_0.9972365.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03888257-0.004083_rugby ball _ snail_0.41588444.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03888257-0.004083_rugby ball _ snail_0.41588444.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 651,884B, BPFP=1.2373 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 511,644B, BPFP=0.9711 +⌛️ [2/4] FRONTEND: Frontend time: 0.598s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.077s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10005040 26.07303625 + layer.39.0 7.47115060 967.62609329 + ------------------------------------------------------------------------------------- + TOTAL 3.78560050 496.84956477 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1163528 +BPFP 1.1042 bits/point +EBPFP 1.1042 equivalent bits/point +MSE 496.849565 +---------------------- -------------------------------------------------------- +Time: 1.726s Load: 0.050s, Pack+Encode: 0.598s, Decode+Unpack: 1.077s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 496.8496 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03888257-0.004083_rugby ball _ snail_0.41588444.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03888257-0.004083_rugby ball _ snail_0.41588444.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03891332-0.003727_syringe _ syringe_0.93799996.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03891332-0.003727_syringe _ syringe_0.93799996.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 662,400B, BPFP=1.2573 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 665,184B, BPFP=1.2626 +⌛️ [2/4] FRONTEND: Frontend time: 0.602s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.045s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09617506 25.34741709 + layer.39.0 18.45312310 1587.69278426 + ------------------------------------------------------------------------------------- + TOTAL 9.27464908 806.52010067 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1327584 +BPFP 1.2599 bits/point +EBPFP 1.2599 equivalent bits/point +MSE 806.520101 +---------------------- -------------------------------------------------------- +Time: 1.719s Load: 0.072s, Pack+Encode: 0.602s, Decode+Unpack: 1.045s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 806.5201 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03891332-0.003727_syringe _ syringe_0.93799996.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03891332-0.003727_syringe _ syringe_0.93799996.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03982430-0.005102_couch _ couch_0.9976859.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03982430-0.005102_couch _ couch_0.9976859.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 552,364B, BPFP=1.0484 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 570,740B, BPFP=1.0833 +⌛️ [2/4] FRONTEND: Frontend time: 0.587s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.090s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09691652 36.90674350 + layer.39.0 169.89398081 1421.97874150 + ------------------------------------------------------------------------------------- + TOTAL 84.99544866 729.44274250 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1123104 +BPFP 1.0659 bits/point +EBPFP 1.0659 equivalent bits/point +MSE 729.442742 +---------------------- -------------------------------------------------------- +Time: 1.736s Load: 0.059s, Pack+Encode: 0.587s, Decode+Unpack: 1.090s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 729.4427 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n03982430-0.005102_couch _ couch_0.9976859.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n03982430-0.005102_couch _ couch_0.9976859.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04033901-0.007476_envelope _ envelope_0.9990971.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04033901-0.007476_envelope _ envelope_0.9990971.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 604,664B, BPFP=1.1477 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 578,708B, BPFP=1.0984 +⌛️ [2/4] FRONTEND: Frontend time: 0.597s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.085s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10364226 38.85757182 + layer.39.0 7.34252906 1144.97315355 + ------------------------------------------------------------------------------------- + TOTAL 3.72308566 591.91536269 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1183372 +BPFP 1.1231 bits/point +EBPFP 1.1231 equivalent bits/point +MSE 591.915363 +---------------------- -------------------------------------------------------- +Time: 1.731s Load: 0.050s, Pack+Encode: 0.597s, Decode+Unpack: 1.085s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 591.9154 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04033901-0.007476_envelope _ envelope_0.9990971.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04033901-0.007476_envelope _ envelope_0.9990971.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04039381-0.000054_hair dryer _ hair dryer_0.5667548.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04039381-0.000054_hair dryer _ hair dryer_0.5667548.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 636,876B, BPFP=1.2088 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 721,996B, BPFP=1.3704 +⌛️ [2/4] FRONTEND: Frontend time: 0.562s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.052s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09588603 12.70719733 + layer.39.0 26.21653304 1341.75996113 + ------------------------------------------------------------------------------------- + TOTAL 13.15620954 677.23357923 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1358872 +BPFP 1.2896 bits/point +EBPFP 1.2896 equivalent bits/point +MSE 677.233579 +---------------------- -------------------------------------------------------- +Time: 1.674s Load: 0.060s, Pack+Encode: 0.562s, Decode+Unpack: 1.052s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 677.2336 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04039381-0.000054_hair dryer _ hair dryer_0.5667548.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04039381-0.000054_hair dryer _ hair dryer_0.5667548.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04118538-0.005804_cowboy boot _ cowboy boot_0.21208441.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04118538-0.005804_cowboy boot _ cowboy boot_0.21208441.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.061s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 693,020B, BPFP=1.3154 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 647,012B, BPFP=1.2281 +⌛️ [2/4] FRONTEND: Frontend time: 0.599s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.125s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09664223 74.77633777 + layer.39.0 8.64007266 1169.84402332 + ------------------------------------------------------------------------------------- + TOTAL 4.36835744 622.31018055 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1340032 +BPFP 1.2717 bits/point +EBPFP 1.2717 equivalent bits/point +MSE 622.310181 +---------------------- -------------------------------------------------------- +Time: 1.784s Load: 0.061s, Pack+Encode: 0.599s, Decode+Unpack: 1.125s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 622.3102 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04118538-0.005804_cowboy boot _ cowboy boot_0.21208441.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04118538-0.005804_cowboy boot _ cowboy boot_0.21208441.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04146614-0.008793_marimba _ marimba_0.54555196.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04146614-0.008793_marimba _ marimba_0.54555196.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 678,188B, BPFP=1.2873 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 661,228B, BPFP=1.2551 +⌛️ [2/4] FRONTEND: Frontend time: 0.603s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.090s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09774729 98.56543823 + layer.39.0 155.07908163 2119.54713314 + ------------------------------------------------------------------------------------- + TOTAL 77.58841446 1109.05628568 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1339416 +BPFP 1.2712 bits/point +EBPFP 1.2712 equivalent bits/point +MSE 1109.056286 +---------------------- -------------------------------------------------------- +Time: 1.763s Load: 0.070s, Pack+Encode: 0.603s, Decode+Unpack: 1.090s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1109.0563 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04146614-0.008793_marimba _ marimba_0.54555196.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04146614-0.008793_marimba _ marimba_0.54555196.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04179913-0.006024_guacamole _ custard apple_0.5550306.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04179913-0.006024_guacamole _ custard apple_0.5550306.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 721,044B, BPFP=1.3686 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 701,388B, BPFP=1.3313 +⌛️ [2/4] FRONTEND: Frontend time: 0.540s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.037s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11409367 285.43989917 + layer.39.0 68.43204871 1364.97157434 + ------------------------------------------------------------------------------------- + TOTAL 34.27307119 825.20573676 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1422432 +BPFP 1.3499 bits/point +EBPFP 1.3499 equivalent bits/point +MSE 825.205737 +---------------------- -------------------------------------------------------- +Time: 1.629s Load: 0.051s, Pack+Encode: 0.540s, Decode+Unpack: 1.037s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 825.2057 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04179913-0.006024_guacamole _ custard apple_0.5550306.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04179913-0.006024_guacamole _ custard apple_0.5550306.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04208210-0.007213_doormat _ doormat_0.81736016.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04208210-0.007213_doormat _ doormat_0.81736016.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 744,596B, BPFP=1.4133 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 643,472B, BPFP=1.2214 +⌛️ [2/4] FRONTEND: Frontend time: 0.524s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.043s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10601767 238.05647170 + layer.39.0 349.44518343 1617.27684645 + ------------------------------------------------------------------------------------- + TOTAL 174.77560055 927.66665907 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1388068 +BPFP 1.3173 bits/point +EBPFP 1.3173 equivalent bits/point +MSE 927.666659 +---------------------- -------------------------------------------------------- +Time: 1.618s Load: 0.051s, Pack+Encode: 0.524s, Decode+Unpack: 1.043s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 927.6667 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04208210-0.007213_doormat _ doormat_0.81736016.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04208210-0.007213_doormat _ doormat_0.81736016.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04270147-0.000435_rotary dial telephone _ rotary dial telephone_0.9268941.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04270147-0.000435_rotary dial telephone _ rotary dial telephone_0.9268941.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.057s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 631,144B, BPFP=1.1980 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 722,404B, BPFP=1.3712 +⌛️ [2/4] FRONTEND: Frontend time: 0.532s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.032s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09464848 12.49804782 + layer.39.0 229.78908528 1745.79518950 + ------------------------------------------------------------------------------------- + TOTAL 114.94186688 879.14661866 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1353548 +BPFP 1.2846 bits/point +EBPFP 1.2846 equivalent bits/point +MSE 879.146619 +---------------------- -------------------------------------------------------- +Time: 1.621s Load: 0.057s, Pack+Encode: 0.532s, Decode+Unpack: 1.032s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 879.1466 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04270147-0.000435_rotary dial telephone _ rotary dial telephone_0.9268941.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04270147-0.000435_rotary dial telephone _ rotary dial telephone_0.9268941.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04317175-0.006894_bow tie _ bow tie_0.95877784.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04317175-0.006894_bow tie _ bow tie_0.95877784.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 662,424B, BPFP=1.2573 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 686,992B, BPFP=1.3040 +⌛️ [2/4] FRONTEND: Frontend time: 0.530s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.027s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09706025 49.96889046 + layer.39.0 10.87108806 1295.34693878 + ------------------------------------------------------------------------------------- + TOTAL 5.48407415 672.65791462 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1349416 +BPFP 1.2807 bits/point +EBPFP 1.2807 equivalent bits/point +MSE 672.657915 +---------------------- -------------------------------------------------------- +Time: 1.627s Load: 0.071s, Pack+Encode: 0.530s, Decode+Unpack: 1.027s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 672.6579 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04317175-0.006894_bow tie _ bow tie_0.95877784.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04317175-0.006894_bow tie _ bow tie_0.95877784.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04347754-0.001405_viaduct _ viaduct_0.8445672.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04347754-0.001405_viaduct _ viaduct_0.8445672.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 553,660B, BPFP=1.0509 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 635,460B, BPFP=1.2062 +⌛️ [2/4] FRONTEND: Frontend time: 0.547s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.086s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09586499 12.70984990 + layer.39.0 267.55718537 1627.80126336 + ------------------------------------------------------------------------------------- + TOTAL 133.82652518 820.25555663 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1189120 +BPFP 1.1285 bits/point +EBPFP 1.1285 equivalent bits/point +MSE 820.255557 +---------------------- -------------------------------------------------------- +Time: 1.703s Load: 0.070s, Pack+Encode: 0.547s, Decode+Unpack: 1.086s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 820.2556 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04347754-0.001405_viaduct _ viaduct_0.8445672.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04347754-0.001405_viaduct _ viaduct_0.8445672.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04355338-0.000791_envelope _ envelope_0.9969598.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04355338-0.000791_envelope _ envelope_0.9969598.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 665,540B, BPFP=1.2632 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 654,788B, BPFP=1.2428 +⌛️ [2/4] FRONTEND: Frontend time: 0.606s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.060s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10273007 75.21754282 + layer.39.0 331.89978134 1742.92468416 + ------------------------------------------------------------------------------------- + TOTAL 166.00125571 909.07111349 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1320328 +BPFP 1.2530 bits/point +EBPFP 1.2530 equivalent bits/point +MSE 909.071113 +---------------------- -------------------------------------------------------- +Time: 1.716s Load: 0.051s, Pack+Encode: 0.606s, Decode+Unpack: 1.060s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 909.0711 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04355338-0.000791_envelope _ envelope_0.9969598.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04355338-0.000791_envelope _ envelope_0.9969598.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04366367-0.002021_parachute _ parachute_0.9226023.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04366367-0.002021_parachute _ parachute_0.9226023.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 591,220B, BPFP=1.1222 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 514,652B, BPFP=0.9769 +⌛️ [2/4] FRONTEND: Frontend time: 0.529s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.011s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09577132 49.88361349 + layer.39.0 47.60657343 1240.14625850 + ------------------------------------------------------------------------------------- + TOTAL 23.85117238 645.01493600 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1105872 +BPFP 1.0495 bits/point +EBPFP 1.0495 equivalent bits/point +MSE 645.014936 +---------------------- -------------------------------------------------------- +Time: 1.590s Load: 0.050s, Pack+Encode: 0.529s, Decode+Unpack: 1.011s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 645.0149 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04366367-0.002021_parachute _ parachute_0.9226023.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04366367-0.002021_parachute _ parachute_0.9226023.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04376876-0.004615_lighter _ lighter_0.69176143.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04376876-0.004615_lighter _ lighter_0.69176143.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 651,988B, BPFP=1.2375 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 687,512B, BPFP=1.3050 +⌛️ [2/4] FRONTEND: Frontend time: 0.554s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.030s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09912059 38.16846225 + layer.39.0 173.01079628 1709.96926628 + ------------------------------------------------------------------------------------- + TOTAL 86.55495844 874.06886426 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1339500 +BPFP 1.2712 bits/point +EBPFP 1.2712 equivalent bits/point +MSE 874.068864 +---------------------- -------------------------------------------------------- +Time: 1.644s Load: 0.060s, Pack+Encode: 0.554s, Decode+Unpack: 1.030s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 874.0689 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04376876-0.004615_lighter _ lighter_0.69176143.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04376876-0.004615_lighter _ lighter_0.69176143.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04442312-0.001690_washing machine _ washing machine_0.8494714.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04442312-0.001690_washing machine _ washing machine_0.8494714.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 569,724B, BPFP=1.0814 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 601,060B, BPFP=1.1409 +⌛️ [2/4] FRONTEND: Frontend time: 0.529s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.054s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.08302300 12.77034268 + layer.39.0 28.24609944 1157.44740039 + ------------------------------------------------------------------------------------- + TOTAL 18.16456122 585.10887153 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1170784 +BPFP 1.1111 bits/point +EBPFP 1.1111 equivalent bits/point +MSE 585.108872 +---------------------- -------------------------------------------------------- +Time: 1.653s Load: 0.070s, Pack+Encode: 0.529s, Decode+Unpack: 1.054s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 585.1089 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04442312-0.001690_washing machine _ washing machine_0.8494714.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04442312-0.001690_washing machine _ washing machine_0.8494714.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04456115-0.002573_hair dryer _ envelope_0.34823084.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04456115-0.002573_hair dryer _ envelope_0.34823084.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 609,016B, BPFP=1.1560 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 659,972B, BPFP=1.2527 +⌛️ [2/4] FRONTEND: Frontend time: 0.648s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.087s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09444211 12.88102925 + layer.39.0 8.80792942 1151.73493683 + ------------------------------------------------------------------------------------- + TOTAL 4.45118577 582.30798304 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1268988 +BPFP 1.2043 bits/point +EBPFP 1.2043 equivalent bits/point +MSE 582.307983 +---------------------- -------------------------------------------------------- +Time: 1.804s Load: 0.069s, Pack+Encode: 0.648s, Decode+Unpack: 1.087s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 582.3080 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04456115-0.002573_hair dryer _ envelope_0.34823084.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04456115-0.002573_hair dryer _ envelope_0.34823084.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04509417-0.000350_sundial _ sundial_0.99936897.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04509417-0.000350_sundial _ sundial_0.99936897.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 661,840B, BPFP=1.2562 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 606,932B, BPFP=1.1520 +⌛️ [2/4] FRONTEND: Frontend time: 0.593s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.086s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10319057 75.70123451 + layer.39.0 8.14296913 1033.34815355 + ------------------------------------------------------------------------------------- + TOTAL 4.12307985 554.52469403 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1268772 +BPFP 1.2041 bits/point +EBPFP 1.2041 equivalent bits/point +MSE 554.524694 +---------------------- -------------------------------------------------------- +Time: 1.749s Load: 0.070s, Pack+Encode: 0.593s, Decode+Unpack: 1.086s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 554.5247 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04509417-0.000350_sundial _ sundial_0.99936897.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04509417-0.000350_sundial _ sundial_0.99936897.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04532670-0.000670_spider web _ spider web_0.70711935.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04532670-0.000670_spider web _ spider web_0.70711935.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 732,900B, BPFP=1.3911 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 720,128B, BPFP=1.3669 +⌛️ [2/4] FRONTEND: Frontend time: 0.530s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.065s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09618602 64.16452563 + layer.39.0 175.41615039 1554.06717687 + ------------------------------------------------------------------------------------- + TOTAL 87.75616821 809.11585125 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1453028 +BPFP 1.3790 bits/point +EBPFP 1.3790 equivalent bits/point +MSE 809.115851 +---------------------- -------------------------------------------------------- +Time: 1.646s Load: 0.051s, Pack+Encode: 0.530s, Decode+Unpack: 1.065s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 809.1159 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04532670-0.000670_spider web _ spider web_0.70711935.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04532670-0.000670_spider web _ spider web_0.70711935.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04540053-0.000734_balance beam _ balance beam_0.99765223.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04540053-0.000734_balance beam _ balance beam_0.99765223.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 612,020B, BPFP=1.1617 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 580,872B, BPFP=1.1025 +⌛️ [2/4] FRONTEND: Frontend time: 0.589s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.092s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09941827 48.98554801 + layer.39.0 8.11341412 1195.93938290 + ------------------------------------------------------------------------------------- + TOTAL 4.10641619 622.46246545 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1192892 +BPFP 1.1321 bits/point +EBPFP 1.1321 equivalent bits/point +MSE 622.462465 +---------------------- -------------------------------------------------------- +Time: 1.751s Load: 0.071s, Pack+Encode: 0.589s, Decode+Unpack: 1.092s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 622.4625 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04540053-0.000734_balance beam _ balance beam_0.99765223.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04540053-0.000734_balance beam _ balance beam_0.99765223.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04606251-0.003483_cliff _ cliff_0.9972174.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04606251-0.003483_cliff _ cliff_0.9972174.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 666,972B, BPFP=1.2660 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 558,228B, BPFP=1.0596 +⌛️ [2/4] FRONTEND: Frontend time: 0.542s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.018s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09940710 148.86462889 + layer.39.0 906.86880466 1882.63326045 + ------------------------------------------------------------------------------------- + TOTAL 453.48410588 1015.74894467 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1225200 +BPFP 1.1628 bits/point +EBPFP 1.1628 equivalent bits/point +MSE 1015.748945 +---------------------- -------------------------------------------------------- +Time: 1.620s Load: 0.059s, Pack+Encode: 0.542s, Decode+Unpack: 1.018s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1015.7489 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n04606251-0.003483_cliff _ cliff_0.9972174.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n04606251-0.003483_cliff _ cliff_0.9972174.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07583066-0.003935_maraca _ maraca_0.5370013.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07583066-0.003935_maraca _ maraca_0.5370013.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 610,004B, BPFP=1.1578 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 574,064B, BPFP=1.0896 +⌛️ [2/4] FRONTEND: Frontend time: 0.504s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.023s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12045678 258.57288630 + layer.39.0 38.29438092 1494.31972789 + ------------------------------------------------------------------------------------- + TOTAL 19.20741885 876.44630709 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1184068 +BPFP 1.1237 bits/point +EBPFP 1.1237 equivalent bits/point +MSE 876.446307 +---------------------- -------------------------------------------------------- +Time: 1.579s Load: 0.051s, Pack+Encode: 0.504s, Decode+Unpack: 1.023s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 876.4463 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07583066-0.003935_maraca _ maraca_0.5370013.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n07583066-0.003935_maraca _ maraca_0.5370013.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07695742-0.003226_ant _ ant_0.64413536.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07695742-0.003226_ant _ ant_0.64413536.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 798,616B, BPFP=1.5158 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 662,688B, BPFP=1.2578 +⌛️ [2/4] FRONTEND: Frontend time: 0.614s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.100s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.16263347 462.23104956 + layer.39.0 172.10254191 1604.27332362 + ------------------------------------------------------------------------------------- + TOTAL 86.13258769 1033.25218659 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1461304 +BPFP 1.3868 bits/point +EBPFP 1.3868 equivalent bits/point +MSE 1033.252187 +---------------------- -------------------------------------------------------- +Time: 1.766s Load: 0.052s, Pack+Encode: 0.614s, Decode+Unpack: 1.100s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1033.2522 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07695742-0.003226_ant _ ant_0.64413536.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n07695742-0.003226_ant _ ant_0.64413536.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07718472-0.001330_spatula _ spatula_0.5388231.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07718472-0.001330_spatula _ spatula_0.5388231.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.059s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 649,092B, BPFP=1.2320 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 667,652B, BPFP=1.2673 +⌛️ [2/4] FRONTEND: Frontend time: 0.609s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.069s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09672572 50.36734694 + layer.39.0 34.52145211 1460.33709913 + ------------------------------------------------------------------------------------- + TOTAL 17.30908891 755.35222303 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1316744 +BPFP 1.2496 bits/point +EBPFP 1.2496 equivalent bits/point +MSE 755.352223 +---------------------- -------------------------------------------------------- +Time: 1.738s Load: 0.059s, Pack+Encode: 0.609s, Decode+Unpack: 1.069s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 755.3522 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07718472-0.001330_spatula _ spatula_0.5388231.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n07718472-0.001330_spatula _ spatula_0.5388231.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07831146-0.002710_guacamole _ guacamole_0.99510396.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07831146-0.002710_guacamole _ guacamole_0.99510396.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 699,228B, BPFP=1.3272 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 701,376B, BPFP=1.3313 +⌛️ [2/4] FRONTEND: Frontend time: 0.540s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.026s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09717902 87.33179968 + layer.39.0 26.55584533 1591.31899903 + ------------------------------------------------------------------------------------- + TOTAL 13.32651218 839.32539936 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1400604 +BPFP 1.3292 bits/point +EBPFP 1.3292 equivalent bits/point +MSE 839.325399 +---------------------- -------------------------------------------------------- +Time: 1.616s Load: 0.050s, Pack+Encode: 0.540s, Decode+Unpack: 1.026s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 839.3254 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n07831146-0.002710_guacamole _ guacamole_0.99510396.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n07831146-0.002710_guacamole _ guacamole_0.99510396.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n09229709-0.002628_stethoscope _ stethoscope_0.9726458.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n09229709-0.002628_stethoscope _ stethoscope_0.9726458.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 653,380B, BPFP=1.2402 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 634,488B, BPFP=1.2043 +⌛️ [2/4] FRONTEND: Frontend time: 0.525s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.022s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10247729 27.13382608 + layer.39.0 58.71458181 1340.96671526 + ------------------------------------------------------------------------------------- + TOTAL 29.40852955 684.05027067 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1287868 +BPFP 1.2222 bits/point +EBPFP 1.2222 equivalent bits/point +MSE 684.050271 +---------------------- -------------------------------------------------------- +Time: 1.617s Load: 0.070s, Pack+Encode: 0.525s, Decode+Unpack: 1.022s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 684.0503 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n09229709-0.002628_stethoscope _ stethoscope_0.9726458.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n09229709-0.002628_stethoscope _ stethoscope_0.9726458.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12057211-0.000404_nail _ newt_0.31321314.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12057211-0.000404_nail _ newt_0.31321314.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 794,824B, BPFP=1.5086 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 576,632B, BPFP=1.0945 +⌛️ [2/4] FRONTEND: Frontend time: 0.616s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.077s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11577855 706.34548105 + layer.39.0 8.72387956 1018.46944849 + ------------------------------------------------------------------------------------- + TOTAL 4.41982905 862.40746477 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1371456 +BPFP 1.3016 bits/point +EBPFP 1.3016 equivalent bits/point +MSE 862.407465 +---------------------- -------------------------------------------------------- +Time: 1.765s Load: 0.072s, Pack+Encode: 0.616s, Decode+Unpack: 1.077s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 862.4075 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12057211-0.000404_nail _ newt_0.31321314.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n12057211-0.000404_nail _ newt_0.31321314.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12144580-0.002806_banana _ banana_0.999156.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12144580-0.002806_banana _ banana_0.999156.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 701,380B, BPFP=1.3313 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 696,376B, BPFP=1.3218 +⌛️ [2/4] FRONTEND: Frontend time: 0.604s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.065s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09629347 100.12447613 + layer.39.0 105.38953930 2135.20165209 + ------------------------------------------------------------------------------------- + TOTAL 52.74291638 1117.66306411 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1397756 +BPFP 1.3265 bits/point +EBPFP 1.3265 equivalent bits/point +MSE 1117.663064 +---------------------- -------------------------------------------------------- +Time: 1.719s Load: 0.050s, Pack+Encode: 0.604s, Decode+Unpack: 1.065s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1117.6631 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12144580-0.002806_banana _ banana_0.999156.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n12144580-0.002806_banana _ banana_0.999156.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12267677-0.004617_pomegranate _ pomegranate_0.9159373.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12267677-0.004617_pomegranate _ pomegranate_0.9159373.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 700,740B, BPFP=1.3301 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 660,596B, BPFP=1.2539 +⌛️ [2/4] FRONTEND: Frontend time: 0.541s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.041s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10323383 39.22243228 + layer.39.0 78.12042942 1746.06717687 + ------------------------------------------------------------------------------------- + TOTAL 39.11183162 892.64480457 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1361336 +BPFP 1.2920 bits/point +EBPFP 1.2920 equivalent bits/point +MSE 892.644805 +---------------------- -------------------------------------------------------- +Time: 1.632s Load: 0.050s, Pack+Encode: 0.541s, Decode+Unpack: 1.041s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 892.6448 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-a/n12267677-0.004617_pomegranate _ pomegranate_0.9159373.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1ka/n12267677-0.004617_pomegranate _ pomegranate_0.9159373.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 1.2483 bits/point +Avg EBPFP 1.2483 equivalent bits/point +Avg MSE 791.261932 +Avg Time 1.841s +------------------------ ---------------------------- diff --git a/lambda0.02/hyperprior-featurecoding-8bit-individual/cls_in1kr/dtufc_hyperprior-featurecoding_dinov3-total_individual.log b/lambda0.02/hyperprior-featurecoding-8bit-individual/cls_in1kr/dtufc_hyperprior-featurecoding_dinov3-total_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..2e06a7a521cacf7ce9865632c2b324fcff78975c --- /dev/null +++ b/lambda0.02/hyperprior-featurecoding-8bit-individual/cls_in1kr/dtufc_hyperprior-featurecoding_dinov3-total_individual.log @@ -0,0 +1,9344 @@ +Experiment: dtufc_hyperprior-featurecoding_dinov3-total_individual +Log file: output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/dtufc_hyperprior-featurecoding_dinov3-total_individual.log +DTUFCCodecConfig: + arch: hyperprior-featurecoding + handler: dinov3-total + checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.02_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.02_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 598 +Loaded hyperprior-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.9' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.19' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.29' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.39' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json +Loaded per-key mappings: model=dinov3-total + Keys: ['layer.9', 'layer.19', 'layer.29', 'layer.39'] +---------------- ----------------------------------------------------------------------------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +Checkpoint codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.02_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-DINOv3/imagenet1k-r +Output output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr +---------------- ----------------------------------------------------------------------------------------------------------------------------- +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01443537-graffiti_3.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01443537-graffiti_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.088s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 657,044B, BPFP=1.2471 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 606,336B, BPFP=1.1509 +⌛️ [2/4] FRONTEND: Frontend time: 0.776s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.069s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09690064 25.86039617 + layer.39.0 23.14008974 1576.46890185 + ------------------------------------------------------------------------------------- + TOTAL 11.61849519 801.16464901 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1263380 +BPFP 1.1990 bits/point +EBPFP 1.1990 equivalent bits/point +MSE 801.164649 +---------------------- -------------------------------------------------------- +Time: 1.933s Load: 0.088s, Pack+Encode: 0.776s, Decode+Unpack: 1.069s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 801.1646 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01443537-graffiti_3.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01443537-graffiti_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01494475-misc_31.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01494475-misc_31.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 664,400B, BPFP=1.2611 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 710,056B, BPFP=1.3477 +⌛️ [2/4] FRONTEND: Frontend time: 0.600s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.109s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09558801 13.06150256 + layer.39.0 281.54433916 2154.08017493 + ------------------------------------------------------------------------------------- + TOTAL 140.81996359 1083.57083874 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1374456 +BPFP 1.3044 bits/point +EBPFP 1.3044 equivalent bits/point +MSE 1083.570839 +---------------------- -------------------------------------------------------- +Time: 1.781s Load: 0.071s, Pack+Encode: 0.600s, Decode+Unpack: 1.109s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1083.5708 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01494475-misc_31.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01494475-misc_31.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01531178-cartoon_22.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01531178-cartoon_22.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.057s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 689,524B, BPFP=1.3088 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 659,056B, BPFP=1.2509 +⌛️ [2/4] FRONTEND: Frontend time: 0.588s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.051s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10319715 25.93941137 + layer.39.0 12.97479918 1008.35981535 + ------------------------------------------------------------------------------------- + TOTAL 6.53899817 517.14961336 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1348580 +BPFP 1.2799 bits/point +EBPFP 1.2799 equivalent bits/point +MSE 517.149613 +---------------------- -------------------------------------------------------- +Time: 1.696s Load: 0.057s, Pack+Encode: 0.588s, Decode+Unpack: 1.051s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 517.1496 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01531178-cartoon_22.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01531178-cartoon_22.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01534433-painting_9.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01534433-painting_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 736,056B, BPFP=1.3971 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 575,384B, BPFP=1.0921 +⌛️ [2/4] FRONTEND: Frontend time: 0.593s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.053s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10660143 307.21143100 + layer.39.0 8.42910859 1006.34456997 + ------------------------------------------------------------------------------------- + TOTAL 4.26785501 656.77800049 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1311440 +BPFP 1.2446 bits/point +EBPFP 1.2446 equivalent bits/point +MSE 656.778000 +---------------------- -------------------------------------------------------- +Time: 1.697s Load: 0.052s, Pack+Encode: 0.593s, Decode+Unpack: 1.053s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 656.7780 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01534433-painting_9.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01534433-painting_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01632777-toy_21.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01632777-toy_21.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 640,124B, BPFP=1.2150 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 682,268B, BPFP=1.2950 +⌛️ [2/4] FRONTEND: Frontend time: 0.580s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.046s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09516629 25.46253758 + layer.39.0 31.73491595 1698.93428086 + ------------------------------------------------------------------------------------- + TOTAL 15.91504112 862.19840922 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1322392 +BPFP 1.2550 bits/point +EBPFP 1.2550 equivalent bits/point +MSE 862.198409 +---------------------- -------------------------------------------------------- +Time: 1.679s Load: 0.052s, Pack+Encode: 0.580s, Decode+Unpack: 1.046s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 862.1984 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01632777-toy_21.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01632777-toy_21.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01748264-misc_18.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01748264-misc_18.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 677,108B, BPFP=1.2852 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 595,248B, BPFP=1.1298 +⌛️ [2/4] FRONTEND: Frontend time: 0.572s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.063s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.16139180 517.14637998 + layer.39.0 362.83485180 1999.98858115 + ------------------------------------------------------------------------------------- + TOTAL 181.49812180 1258.56748056 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1272356 +BPFP 1.2075 bits/point +EBPFP 1.2075 equivalent bits/point +MSE 1258.567481 +---------------------- -------------------------------------------------------- +Time: 1.686s Load: 0.051s, Pack+Encode: 0.572s, Decode+Unpack: 1.063s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1258.5675 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01748264-misc_18.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01748264-misc_18.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01784675-painting_2.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01784675-painting_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.049s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 767,184B, BPFP=1.4562 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 615,776B, BPFP=1.1688 +⌛️ [2/4] FRONTEND: Frontend time: 0.579s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.053s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13866578 264.51934524 + layer.39.0 232.10166120 1846.48712342 + ------------------------------------------------------------------------------------- + TOTAL 116.12016349 1055.50323433 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1382960 +BPFP 1.3125 bits/point +EBPFP 1.3125 equivalent bits/point +MSE 1055.503234 +---------------------- -------------------------------------------------------- +Time: 1.682s Load: 0.049s, Pack+Encode: 0.579s, Decode+Unpack: 1.053s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1055.5032 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01784675-painting_2.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01784675-painting_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01820546-painting_29.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01820546-painting_29.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 741,396B, BPFP=1.4072 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 626,496B, BPFP=1.1891 +⌛️ [2/4] FRONTEND: Frontend time: 0.519s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.031s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10398871 286.97555272 + layer.39.0 202.99580904 2208.43343052 + ------------------------------------------------------------------------------------- + TOTAL 101.54989888 1247.70449162 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1367892 +BPFP 1.2982 bits/point +EBPFP 1.2982 equivalent bits/point +MSE 1247.704492 +---------------------- -------------------------------------------------------- +Time: 1.602s Load: 0.052s, Pack+Encode: 0.519s, Decode+Unpack: 1.031s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1247.7045 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01820546-painting_29.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01820546-painting_29.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01833805-painting_23.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01833805-painting_23.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 688,624B, BPFP=1.3071 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 602,744B, BPFP=1.1441 +⌛️ [2/4] FRONTEND: Frontend time: 0.576s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.106s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09675035 61.26847971 + layer.39.0 56.43029868 1338.73773081 + ------------------------------------------------------------------------------------- + TOTAL 28.26352451 700.00310526 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1291368 +BPFP 1.2256 bits/point +EBPFP 1.2256 equivalent bits/point +MSE 700.003105 +---------------------- -------------------------------------------------------- +Time: 1.733s Load: 0.051s, Pack+Encode: 0.576s, Decode+Unpack: 1.106s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 700.0031 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01833805-painting_23.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01833805-painting_23.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01860187-painting_2.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01860187-painting_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 705,988B, BPFP=1.3400 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 601,740B, BPFP=1.1422 +⌛️ [2/4] FRONTEND: Frontend time: 0.606s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.058s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09532418 12.56375653 + layer.39.0 11.39113179 1200.43950437 + ------------------------------------------------------------------------------------- + TOTAL 5.74322799 606.50163045 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1307728 +BPFP 1.2411 bits/point +EBPFP 1.2411 equivalent bits/point +MSE 606.501630 +---------------------- -------------------------------------------------------- +Time: 1.734s Load: 0.071s, Pack+Encode: 0.606s, Decode+Unpack: 1.058s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 606.5016 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01860187-painting_2.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01860187-painting_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01944390-deviantart_6.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01944390-deviantart_6.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 586,180B, BPFP=1.1126 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 546,228B, BPFP=1.0368 +⌛️ [2/4] FRONTEND: Frontend time: 0.566s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.051s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10713051 124.34717414 + layer.39.0 82.30322218 1830.84268707 + ------------------------------------------------------------------------------------- + TOTAL 41.20517635 977.59493061 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1132408 +BPFP 1.0747 bits/point +EBPFP 1.0747 equivalent bits/point +MSE 977.594931 +---------------------- -------------------------------------------------------- +Time: 1.668s Load: 0.052s, Pack+Encode: 0.566s, Decode+Unpack: 1.051s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 977.5949 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01944390-deviantart_6.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01944390-deviantart_6.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01983481-misc_6.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01983481-misc_6.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 660,232B, BPFP=1.2532 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 609,400B, BPFP=1.1567 +⌛️ [2/4] FRONTEND: Frontend time: 0.604s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.085s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10315659 164.37704993 + layer.39.0 236.29731535 1868.23019922 + ------------------------------------------------------------------------------------- + TOTAL 118.20023597 1016.30362457 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1269632 +BPFP 1.2049 bits/point +EBPFP 1.2049 equivalent bits/point +MSE 1016.303625 +---------------------- -------------------------------------------------------- +Time: 1.760s Load: 0.070s, Pack+Encode: 0.604s, Decode+Unpack: 1.085s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1016.3036 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n01983481-misc_6.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n01983481-misc_6.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02051845-cartoon_2.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02051845-cartoon_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 673,876B, BPFP=1.2791 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 642,316B, BPFP=1.2192 +⌛️ [2/4] FRONTEND: Frontend time: 0.599s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.088s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11657756 111.65107204 + layer.39.0 123.57765428 1390.16448008 + ------------------------------------------------------------------------------------- + TOTAL 61.84711592 750.90777606 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1316192 +BPFP 1.2491 bits/point +EBPFP 1.2491 equivalent bits/point +MSE 750.907776 +---------------------- -------------------------------------------------------- +Time: 1.757s Load: 0.069s, Pack+Encode: 0.599s, Decode+Unpack: 1.088s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 750.9078 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02051845-cartoon_2.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02051845-cartoon_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02056570-art_2.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02056570-art_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 617,404B, BPFP=1.1719 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 597,236B, BPFP=1.1336 +⌛️ [2/4] FRONTEND: Frontend time: 0.525s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.022s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09569211 24.94804384 + layer.39.0 33.39981930 1799.64176385 + ------------------------------------------------------------------------------------- + TOTAL 16.74775571 912.29490384 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1214640 +BPFP 1.1527 bits/point +EBPFP 1.1527 equivalent bits/point +MSE 912.294904 +---------------------- -------------------------------------------------------- +Time: 1.597s Load: 0.050s, Pack+Encode: 0.525s, Decode+Unpack: 1.022s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 912.2949 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02056570-art_2.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02056570-art_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02085620-misc_90.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02085620-misc_90.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 696,076B, BPFP=1.3212 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 728,360B, BPFP=1.3825 +⌛️ [2/4] FRONTEND: Frontend time: 0.541s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.030s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09843166 62.26197309 + layer.39.0 72.76188958 1987.48372206 + ------------------------------------------------------------------------------------- + TOTAL 36.43016062 1024.87284758 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1424436 +BPFP 1.3518 bits/point +EBPFP 1.3518 equivalent bits/point +MSE 1024.872848 +---------------------- -------------------------------------------------------- +Time: 1.641s Load: 0.069s, Pack+Encode: 0.541s, Decode+Unpack: 1.030s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1024.8728 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02085620-misc_90.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02085620-misc_90.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088094-misc_39.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088094-misc_39.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 693,384B, BPFP=1.3161 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 608,192B, BPFP=1.1544 +⌛️ [2/4] FRONTEND: Frontend time: 0.561s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.045s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09820385 49.95758549 + layer.39.0 12.32374423 1260.59244412 + ------------------------------------------------------------------------------------- + TOTAL 6.21097404 655.27501481 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1301576 +BPFP 1.2352 bits/point +EBPFP 1.2352 equivalent bits/point +MSE 655.275015 +---------------------- -------------------------------------------------------- +Time: 1.676s Load: 0.070s, Pack+Encode: 0.561s, Decode+Unpack: 1.045s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 655.2750 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088094-misc_39.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02088094-misc_39.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088466-sketch_11.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088466-sketch_11.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 643,644B, BPFP=1.2217 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 648,928B, BPFP=1.2317 +⌛️ [2/4] FRONTEND: Frontend time: 0.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.042s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09459993 12.54154614 + layer.39.0 16.33682960 1576.27295918 + ------------------------------------------------------------------------------------- + TOTAL 8.21571477 794.40725266 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1292572 +BPFP 1.2267 bits/point +EBPFP 1.2267 equivalent bits/point +MSE 794.407253 +---------------------- -------------------------------------------------------- +Time: 1.693s Load: 0.069s, Pack+Encode: 0.581s, Decode+Unpack: 1.042s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 794.4073 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02088466-sketch_11.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02088466-sketch_11.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02094433-misc_20.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02094433-misc_20.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 692,548B, BPFP=1.3145 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 630,448B, BPFP=1.1966 +⌛️ [2/4] FRONTEND: Frontend time: 0.574s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.055s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09538842 37.73494442 + layer.39.0 94.83275632 2180.51967930 + ------------------------------------------------------------------------------------- + TOTAL 47.46407237 1109.12731186 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1322996 +BPFP 1.2556 bits/point +EBPFP 1.2556 equivalent bits/point +MSE 1109.127312 +---------------------- -------------------------------------------------------- +Time: 1.680s Load: 0.051s, Pack+Encode: 0.574s, Decode+Unpack: 1.055s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1109.1273 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02094433-misc_20.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02094433-misc_20.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02097298-misc_9.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02097298-misc_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 764,616B, BPFP=1.4513 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 666,160B, BPFP=1.2644 +⌛️ [2/4] FRONTEND: Frontend time: 0.610s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.092s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11199322 485.39224976 + layer.39.0 26.16675018 1585.57896016 + ------------------------------------------------------------------------------------- + TOTAL 13.13937170 1035.48560496 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1430776 +BPFP 1.3579 bits/point +EBPFP 1.3579 equivalent bits/point +MSE 1035.485605 +---------------------- -------------------------------------------------------- +Time: 1.773s Load: 0.071s, Pack+Encode: 0.610s, Decode+Unpack: 1.092s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1035.4856 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02097298-misc_9.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02097298-misc_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02106662-misc_55.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02106662-misc_55.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 697,312B, BPFP=1.3236 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 660,112B, BPFP=1.2529 +⌛️ [2/4] FRONTEND: Frontend time: 0.604s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.063s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09642073 75.95042213 + layer.39.0 14.86428154 1376.08187561 + ------------------------------------------------------------------------------------- + TOTAL 7.48035113 726.01614887 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1357424 +BPFP 1.2883 bits/point +EBPFP 1.2883 equivalent bits/point +MSE 726.016149 +---------------------- -------------------------------------------------------- +Time: 1.737s Load: 0.070s, Pack+Encode: 0.604s, Decode+Unpack: 1.063s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 726.0161 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02106662-misc_55.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02106662-misc_55.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02109525-sketch_23.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02109525-sketch_23.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 645,252B, BPFP=1.2247 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 669,940B, BPFP=1.2716 +⌛️ [2/4] FRONTEND: Frontend time: 0.584s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.059s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09568003 12.45566178 + layer.39.0 14.01675815 1456.63131681 + ------------------------------------------------------------------------------------- + TOTAL 7.05621909 734.54348930 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1315192 +BPFP 1.2482 bits/point +EBPFP 1.2482 equivalent bits/point +MSE 734.543489 +---------------------- -------------------------------------------------------- +Time: 1.715s Load: 0.071s, Pack+Encode: 0.584s, Decode+Unpack: 1.059s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 734.5435 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02109525-sketch_23.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02109525-sketch_23.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110185-painting_33.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110185-painting_33.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 677,636B, BPFP=1.2862 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 707,008B, BPFP=1.3420 +⌛️ [2/4] FRONTEND: Frontend time: 0.538s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.030s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09599521 24.39412506 + layer.39.0 22.05506522 1780.07568027 + ------------------------------------------------------------------------------------- + TOTAL 11.07553021 902.23490267 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1384644 +BPFP 1.3141 bits/point +EBPFP 1.3141 equivalent bits/point +MSE 902.234903 +---------------------- -------------------------------------------------------- +Time: 1.638s Load: 0.070s, Pack+Encode: 0.538s, Decode+Unpack: 1.030s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 902.2349 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110185-painting_33.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02110185-painting_33.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110341-misc_162.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110341-misc_162.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 627,144B, BPFP=1.1904 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 620,668B, BPFP=1.1781 +⌛️ [2/4] FRONTEND: Frontend time: 0.528s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.057s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11124049 87.56204446 + layer.39.0 14.33747210 1124.95225948 + ------------------------------------------------------------------------------------- + TOTAL 7.22435629 606.25715197 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1247812 +BPFP 1.1842 bits/point +EBPFP 1.1842 equivalent bits/point +MSE 606.257152 +---------------------- -------------------------------------------------------- +Time: 1.637s Load: 0.051s, Pack+Encode: 0.528s, Decode+Unpack: 1.057s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 606.2572 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02110341-misc_162.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02110341-misc_162.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02165456-tattoo_37.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02165456-tattoo_37.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 737,124B, BPFP=1.3991 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 671,940B, BPFP=1.2754 +⌛️ [2/4] FRONTEND: Frontend time: 0.591s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.068s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09780899 61.95066509 + layer.39.0 88.96013271 1919.80636540 + ------------------------------------------------------------------------------------- + TOTAL 44.52897085 990.87851525 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1409064 +BPFP 1.3373 bits/point +EBPFP 1.3373 equivalent bits/point +MSE 990.878515 +---------------------- -------------------------------------------------------- +Time: 1.728s Load: 0.070s, Pack+Encode: 0.591s, Decode+Unpack: 1.068s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 990.8785 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02165456-tattoo_37.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02165456-tattoo_37.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02219486-misc_3.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02219486-misc_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 647,632B, BPFP=1.2293 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 559,304B, BPFP=1.0616 +⌛️ [2/4] FRONTEND: Frontend time: 0.564s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.076s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10021695 13.45590094 + layer.39.0 75.73793580 943.72461127 + ------------------------------------------------------------------------------------- + TOTAL 37.91907638 478.59025611 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1206936 +BPFP 1.1454 bits/point +EBPFP 1.1454 equivalent bits/point +MSE 478.590256 +---------------------- -------------------------------------------------------- +Time: 1.691s Load: 0.051s, Pack+Encode: 0.564s, Decode+Unpack: 1.076s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 478.5903 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02219486-misc_3.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02219486-misc_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02226429-tattoo_0.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02226429-tattoo_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 675,844B, BPFP=1.2828 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 634,896B, BPFP=1.2051 +⌛️ [2/4] FRONTEND: Frontend time: 0.526s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.035s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09506506 12.80367108 + layer.39.0 201.13660107 1547.98590865 + ------------------------------------------------------------------------------------- + TOTAL 100.61583306 780.39478986 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1310740 +BPFP 1.2439 bits/point +EBPFP 1.2439 equivalent bits/point +MSE 780.394790 +---------------------- -------------------------------------------------------- +Time: 1.611s Load: 0.050s, Pack+Encode: 0.526s, Decode+Unpack: 1.035s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 780.3948 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02226429-tattoo_0.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02226429-tattoo_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02233338-tattoo_5.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02233338-tattoo_5.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 676,384B, BPFP=1.2838 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 711,944B, BPFP=1.3513 +⌛️ [2/4] FRONTEND: Frontend time: 0.613s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.069s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09502332 61.42155992 + layer.39.0 172.43500972 1885.44217687 + ------------------------------------------------------------------------------------- + TOTAL 86.26501652 973.43186839 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1388328 +BPFP 1.3176 bits/point +EBPFP 1.3176 equivalent bits/point +MSE 973.431868 +---------------------- -------------------------------------------------------- +Time: 1.751s Load: 0.069s, Pack+Encode: 0.613s, Decode+Unpack: 1.069s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 973.4319 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02233338-tattoo_5.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02233338-tattoo_5.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02279972-painting_2.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02279972-painting_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.076s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 782,976B, BPFP=1.4862 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 706,104B, BPFP=1.3402 +⌛️ [2/4] FRONTEND: Frontend time: 0.599s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.050s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11337867 211.71351130 + layer.39.0 361.17623299 1804.56049563 + ------------------------------------------------------------------------------------- + TOTAL 180.64480583 1008.13700346 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1489080 +BPFP 1.4132 bits/point +EBPFP 1.4132 equivalent bits/point +MSE 1008.137003 +---------------------- -------------------------------------------------------- +Time: 1.725s Load: 0.076s, Pack+Encode: 0.599s, Decode+Unpack: 1.050s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1008.1370 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02279972-painting_2.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02279972-painting_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02317335-sculpture_0.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02317335-sculpture_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 682,004B, BPFP=1.2945 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 738,888B, BPFP=1.4025 +⌛️ [2/4] FRONTEND: Frontend time: 0.582s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.070s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09546056 13.34200661 + layer.39.0 1163.18707483 2059.96234208 + ------------------------------------------------------------------------------------- + TOTAL 581.64126769 1036.65217435 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1420892 +BPFP 1.3485 bits/point +EBPFP 1.3485 equivalent bits/point +MSE 1036.652174 +---------------------- -------------------------------------------------------- +Time: 1.703s Load: 0.050s, Pack+Encode: 0.582s, Decode+Unpack: 1.070s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1036.6522 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02317335-sculpture_0.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02317335-sculpture_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02346627-painting_9.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02346627-painting_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 721,288B, BPFP=1.3691 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 697,260B, BPFP=1.3235 +⌛️ [2/4] FRONTEND: Frontend time: 0.593s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.052s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13205896 302.72789116 + layer.39.0 503.01482021 1847.84256560 + ------------------------------------------------------------------------------------- + TOTAL 251.57343959 1075.28522838 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1418548 +BPFP 1.3463 bits/point +EBPFP 1.3463 equivalent bits/point +MSE 1075.285228 +---------------------- -------------------------------------------------------- +Time: 1.715s Load: 0.070s, Pack+Encode: 0.593s, Decode+Unpack: 1.052s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1075.2852 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02346627-painting_9.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02346627-painting_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02391049-deviantart_0.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02391049-deviantart_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 636,144B, BPFP=1.2075 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 580,600B, BPFP=1.1020 +⌛️ [2/4] FRONTEND: Frontend time: 0.578s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.053s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10116939 124.23918853 + layer.39.0 17.42674737 1443.90524781 + ------------------------------------------------------------------------------------- + TOTAL 8.76395838 784.07221817 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1216744 +BPFP 1.1547 bits/point +EBPFP 1.1547 equivalent bits/point +MSE 784.072218 +---------------------- -------------------------------------------------------- +Time: 1.682s Load: 0.051s, Pack+Encode: 0.578s, Decode+Unpack: 1.053s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 784.0722 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02391049-deviantart_0.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02391049-deviantart_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02395406-sculpture_31.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02395406-sculpture_31.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 778,084B, BPFP=1.4769 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 556,468B, BPFP=1.0562 +⌛️ [2/4] FRONTEND: Frontend time: 0.591s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.075s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11469608 574.40087464 + layer.39.0 30.55020044 1148.14419339 + ------------------------------------------------------------------------------------- + TOTAL 15.33244826 861.27253401 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1334552 +BPFP 1.2665 bits/point +EBPFP 1.2665 equivalent bits/point +MSE 861.272534 +---------------------- -------------------------------------------------------- +Time: 1.738s Load: 0.071s, Pack+Encode: 0.591s, Decode+Unpack: 1.075s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 861.2725 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02395406-sculpture_31.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02395406-sculpture_31.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02445715-art_3.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02445715-art_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 673,564B, BPFP=1.2785 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 657,540B, BPFP=1.2481 +⌛️ [2/4] FRONTEND: Frontend time: 0.603s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.056s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09587883 13.09305720 + layer.39.0 77.63827138 2068.52283771 + ------------------------------------------------------------------------------------- + TOTAL 38.86707511 1040.80794745 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1331104 +BPFP 1.2633 bits/point +EBPFP 1.2633 equivalent bits/point +MSE 1040.807947 +---------------------- -------------------------------------------------------- +Time: 1.729s Load: 0.071s, Pack+Encode: 0.603s, Decode+Unpack: 1.056s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1040.8079 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02445715-art_3.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02445715-art_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02672831-sculpture_2.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02672831-sculpture_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.080s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 743,452B, BPFP=1.4111 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 693,324B, BPFP=1.3160 +⌛️ [2/4] FRONTEND: Frontend time: 0.607s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.097s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11638676 272.76545797 + layer.39.0 42.74346681 2054.52332362 + ------------------------------------------------------------------------------------- + TOTAL 21.42992678 1163.64439079 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1436776 +BPFP 1.3636 bits/point +EBPFP 1.3636 equivalent bits/point +MSE 1163.644391 +---------------------- -------------------------------------------------------- +Time: 1.783s Load: 0.080s, Pack+Encode: 0.607s, Decode+Unpack: 1.097s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1163.6444 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02672831-sculpture_2.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02672831-sculpture_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02701002-videogame_1.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02701002-videogame_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 613,560B, BPFP=1.1646 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 659,840B, BPFP=1.2524 +⌛️ [2/4] FRONTEND: Frontend time: 0.615s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.051s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10320827 137.13722364 + layer.39.0 160.61054422 2011.56353256 + ------------------------------------------------------------------------------------- + TOTAL 80.35687624 1074.35037810 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1273400 +BPFP 1.2085 bits/point +EBPFP 1.2085 equivalent bits/point +MSE 1074.350378 +---------------------- -------------------------------------------------------- +Time: 1.737s Load: 0.071s, Pack+Encode: 0.615s, Decode+Unpack: 1.051s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1074.3504 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02701002-videogame_1.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02701002-videogame_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02749479-misc_35.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02749479-misc_35.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 574,364B, BPFP=1.0902 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 605,924B, BPFP=1.1501 +⌛️ [2/4] FRONTEND: Frontend time: 0.589s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.034s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09764870 49.82657237 + layer.39.0 172.65676628 1777.15002430 + ------------------------------------------------------------------------------------- + TOTAL 86.37720749 913.48829833 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1180288 +BPFP 1.1201 bits/point +EBPFP 1.1201 equivalent bits/point +MSE 913.488298 +---------------------- -------------------------------------------------------- +Time: 1.675s Load: 0.052s, Pack+Encode: 0.589s, Decode+Unpack: 1.034s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 913.4883 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02749479-misc_35.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02749479-misc_35.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02769748-cartoon_11.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02769748-cartoon_11.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 583,288B, BPFP=1.1071 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 666,336B, BPFP=1.2648 +⌛️ [2/4] FRONTEND: Frontend time: 0.584s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.054s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12263774 73.99844357 + layer.39.0 11.02823964 1400.08697765 + ------------------------------------------------------------------------------------- + TOTAL 5.57543869 737.04271061 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1249624 +BPFP 1.1859 bits/point +EBPFP 1.1859 equivalent bits/point +MSE 737.042711 +---------------------- -------------------------------------------------------- +Time: 1.690s Load: 0.052s, Pack+Encode: 0.584s, Decode+Unpack: 1.054s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 737.0427 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02769748-cartoon_11.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02769748-cartoon_11.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02793495-sketch_11.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02793495-sketch_11.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 601,764B, BPFP=1.1422 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 544,744B, BPFP=1.0340 +⌛️ [2/4] FRONTEND: Frontend time: 0.584s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.042s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09793751 39.49542563 + layer.39.0 182.75789602 1648.21720117 + ------------------------------------------------------------------------------------- + TOTAL 91.42791676 843.85631340 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1146508 +BPFP 1.0881 bits/point +EBPFP 1.0881 equivalent bits/point +MSE 843.856313 +---------------------- -------------------------------------------------------- +Time: 1.677s Load: 0.051s, Pack+Encode: 0.584s, Decode+Unpack: 1.042s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 843.8563 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02793495-sketch_11.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02793495-sketch_11.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02797295-misc_13.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02797295-misc_13.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 809,568B, BPFP=1.5366 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 709,028B, BPFP=1.3458 +⌛️ [2/4] FRONTEND: Frontend time: 0.614s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.076s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.17140635 814.51779640 + layer.39.0 172.50999150 2028.25862488 + ------------------------------------------------------------------------------------- + TOTAL 86.34069892 1421.38821064 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1518596 +BPFP 1.4412 bits/point +EBPFP 1.4412 equivalent bits/point +MSE 1421.388211 +---------------------- -------------------------------------------------------- +Time: 1.741s Load: 0.051s, Pack+Encode: 0.614s, Decode+Unpack: 1.076s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1421.3882 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02797295-misc_13.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02797295-misc_13.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02802426-art_1.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02802426-art_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 780,172B, BPFP=1.4808 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 754,652B, BPFP=1.4324 +⌛️ [2/4] FRONTEND: Frontend time: 0.617s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.120s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.16523854 489.87181122 + layer.39.0 477.65184645 1940.96027697 + ------------------------------------------------------------------------------------- + TOTAL 238.90854250 1215.41604410 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1534824 +BPFP 1.4566 bits/point +EBPFP 1.4566 equivalent bits/point +MSE 1215.416044 +---------------------- -------------------------------------------------------- +Time: 1.807s Load: 0.070s, Pack+Encode: 0.617s, Decode+Unpack: 1.120s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1215.4160 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02802426-art_1.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02802426-art_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02814860-sticker_8.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02814860-sticker_8.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 694,928B, BPFP=1.3190 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 653,356B, BPFP=1.2401 +⌛️ [2/4] FRONTEND: Frontend time: 0.615s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.057s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12757226 267.64477041 + layer.39.0 19.27598852 1004.74854227 + ------------------------------------------------------------------------------------- + TOTAL 9.70178039 636.19665634 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1348284 +BPFP 1.2796 bits/point +EBPFP 1.2796 equivalent bits/point +MSE 636.196656 +---------------------- -------------------------------------------------------- +Time: 1.742s Load: 0.070s, Pack+Encode: 0.615s, Decode+Unpack: 1.057s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 636.1967 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02814860-sticker_8.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02814860-sticker_8.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02841315-graphic_4.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02841315-graphic_4.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 725,248B, BPFP=1.3766 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 708,012B, BPFP=1.3439 +⌛️ [2/4] FRONTEND: Frontend time: 0.562s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.031s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11826141 112.26358267 + layer.39.0 55.46440340 1464.00352284 + ------------------------------------------------------------------------------------- + TOTAL 27.79133240 788.13355275 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1433260 +BPFP 1.3602 bits/point +EBPFP 1.3602 equivalent bits/point +MSE 788.133553 +---------------------- -------------------------------------------------------- +Time: 1.645s Load: 0.052s, Pack+Encode: 0.562s, Decode+Unpack: 1.031s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 788.1336 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02841315-graphic_4.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02841315-graphic_4.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02843684-cartoon_9.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02843684-cartoon_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 744,192B, BPFP=1.4125 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 645,864B, BPFP=1.2259 +⌛️ [2/4] FRONTEND: Frontend time: 0.606s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.115s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12386809 234.09273263 + layer.39.0 312.00962707 1225.40743440 + ------------------------------------------------------------------------------------- + TOTAL 156.06674758 729.75008352 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1390056 +BPFP 1.3192 bits/point +EBPFP 1.3192 equivalent bits/point +MSE 729.750084 +---------------------- -------------------------------------------------------- +Time: 1.792s Load: 0.071s, Pack+Encode: 0.606s, Decode+Unpack: 1.115s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 729.7501 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02843684-cartoon_9.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02843684-cartoon_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02883205-sketch_15.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02883205-sketch_15.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 614,404B, BPFP=1.1662 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 611,752B, BPFP=1.1612 +⌛️ [2/4] FRONTEND: Frontend time: 0.614s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.056s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09796664 123.28755922 + layer.39.0 103.64267493 1547.73979592 + ------------------------------------------------------------------------------------- + TOTAL 51.87032078 835.51367757 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1226156 +BPFP 1.1637 bits/point +EBPFP 1.1637 equivalent bits/point +MSE 835.513678 +---------------------- -------------------------------------------------------- +Time: 1.740s Load: 0.070s, Pack+Encode: 0.614s, Decode+Unpack: 1.056s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 835.5137 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02883205-sketch_15.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02883205-sketch_15.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02906734-graffiti_3.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02906734-graffiti_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 820,056B, BPFP=1.5565 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 720,316B, BPFP=1.3672 +⌛️ [2/4] FRONTEND: Frontend time: 0.602s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.052s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.17339475 852.58910350 + layer.39.0 166.12656402 1776.64212828 + ------------------------------------------------------------------------------------- + TOTAL 83.14997939 1314.61561589 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1540372 +BPFP 1.4619 bits/point +EBPFP 1.4619 equivalent bits/point +MSE 1314.615616 +---------------------- -------------------------------------------------------- +Time: 1.706s Load: 0.052s, Pack+Encode: 0.602s, Decode+Unpack: 1.052s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1314.6156 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02906734-graffiti_3.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02906734-graffiti_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02909870-sketch_0.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02909870-sketch_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 590,500B, BPFP=1.1208 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 600,660B, BPFP=1.1401 +⌛️ [2/4] FRONTEND: Frontend time: 0.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.053s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.15317524 413.95669339 + layer.39.0 167.75886783 1370.72181730 + ------------------------------------------------------------------------------------- + TOTAL 83.95602154 892.33925534 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1191160 +BPFP 1.1305 bits/point +EBPFP 1.1305 equivalent bits/point +MSE 892.339255 +---------------------- -------------------------------------------------------- +Time: 1.684s Load: 0.050s, Pack+Encode: 0.581s, Decode+Unpack: 1.053s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 892.3393 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02909870-sketch_0.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02909870-sketch_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02939185-painting_0.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02939185-painting_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 650,936B, BPFP=1.2355 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 650,268B, BPFP=1.2343 +⌛️ [2/4] FRONTEND: Frontend time: 0.615s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.057s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09512242 12.97560966 + layer.39.0 131.28711127 1622.53024781 + ------------------------------------------------------------------------------------- + TOTAL 65.69111684 817.75292874 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1301204 +BPFP 1.2349 bits/point +EBPFP 1.2349 equivalent bits/point +MSE 817.752929 +---------------------- -------------------------------------------------------- +Time: 1.743s Load: 0.071s, Pack+Encode: 0.615s, Decode+Unpack: 1.057s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 817.7529 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02939185-painting_0.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02939185-painting_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02948072-misc_10.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02948072-misc_10.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 640,684B, BPFP=1.2161 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 711,196B, BPFP=1.3499 +⌛️ [2/4] FRONTEND: Frontend time: 0.526s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.036s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09566823 25.38668648 + layer.39.0 102.81622783 1707.17808552 + ------------------------------------------------------------------------------------- + TOTAL 51.45594803 866.28238600 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1351880 +BPFP 1.2830 bits/point +EBPFP 1.2830 equivalent bits/point +MSE 866.282386 +---------------------- -------------------------------------------------------- +Time: 1.613s Load: 0.052s, Pack+Encode: 0.526s, Decode+Unpack: 1.036s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 866.2824 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02948072-misc_10.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02948072-misc_10.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02950826-art_1.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02950826-art_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 678,312B, BPFP=1.2875 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 745,012B, BPFP=1.4141 +⌛️ [2/4] FRONTEND: Frontend time: 0.537s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.064s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09506074 48.86834913 + layer.39.0 1071.96149174 2666.22570457 + ------------------------------------------------------------------------------------- + TOTAL 536.02827624 1357.54702685 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1423324 +BPFP 1.3508 bits/point +EBPFP 1.3508 equivalent bits/point +MSE 1357.547027 +---------------------- -------------------------------------------------------- +Time: 1.652s Load: 0.051s, Pack+Encode: 0.537s, Decode+Unpack: 1.064s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1357.5470 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02950826-art_1.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02950826-art_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02951358-misc_1.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02951358-misc_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 602,988B, BPFP=1.1445 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 494,920B, BPFP=0.9394 +⌛️ [2/4] FRONTEND: Frontend time: 0.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.048s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09568294 14.17183704 + layer.39.0 598.97078474 1877.75741011 + ------------------------------------------------------------------------------------- + TOTAL 299.53323384 945.96462357 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1097908 +BPFP 1.0420 bits/point +EBPFP 1.0420 equivalent bits/point +MSE 945.964624 +---------------------- -------------------------------------------------------- +Time: 1.680s Load: 0.051s, Pack+Encode: 0.581s, Decode+Unpack: 1.048s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 945.9646 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02951358-misc_1.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02951358-misc_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02966193-cartoon_22.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02966193-cartoon_22.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 777,544B, BPFP=1.4758 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 725,324B, BPFP=1.3767 +⌛️ [2/4] FRONTEND: Frontend time: 0.615s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.120s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10376222 388.18607264 + layer.39.0 767.85532070 2594.13119534 + ------------------------------------------------------------------------------------- + TOTAL 383.97954146 1491.15863399 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1502868 +BPFP 1.4263 bits/point +EBPFP 1.4263 equivalent bits/point +MSE 1491.158634 +---------------------- -------------------------------------------------------- +Time: 1.786s Load: 0.051s, Pack+Encode: 0.615s, Decode+Unpack: 1.120s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1491.1586 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02966193-cartoon_22.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02966193-cartoon_22.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02980441-graphic_3.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02980441-graphic_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 597,196B, BPFP=1.1335 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 487,220B, BPFP=0.9248 +⌛️ [2/4] FRONTEND: Frontend time: 0.611s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.039s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09509088 37.14912840 + layer.39.0 13.13791359 1049.74659864 + ------------------------------------------------------------------------------------- + TOTAL 6.61650224 543.44786352 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1084416 +BPFP 1.0292 bits/point +EBPFP 1.0292 equivalent bits/point +MSE 543.447864 +---------------------- -------------------------------------------------------- +Time: 1.721s Load: 0.070s, Pack+Encode: 0.611s, Decode+Unpack: 1.039s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 543.4479 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n02980441-graphic_3.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n02980441-graphic_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03124170-painting_15.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03124170-painting_15.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 743,236B, BPFP=1.4107 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 807,392B, BPFP=1.5325 +⌛️ [2/4] FRONTEND: Frontend time: 0.567s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.032s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10783903 115.48371447 + layer.39.0 326.57091229 2983.93440233 + ------------------------------------------------------------------------------------- + TOTAL 163.33937566 1549.70905840 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1550628 +BPFP 1.4716 bits/point +EBPFP 1.4716 equivalent bits/point +MSE 1549.709058 +---------------------- -------------------------------------------------------- +Time: 1.651s Load: 0.052s, Pack+Encode: 0.567s, Decode+Unpack: 1.032s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1549.7091 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03124170-painting_15.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03124170-painting_15.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03345487-toy_17.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03345487-toy_17.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 637,420B, BPFP=1.2099 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 611,948B, BPFP=1.1615 +⌛️ [2/4] FRONTEND: Frontend time: 0.574s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.059s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10662318 101.39575741 + layer.39.0 198.63900024 2003.03741497 + ------------------------------------------------------------------------------------- + TOTAL 99.37281171 1052.21658619 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1249368 +BPFP 1.1857 bits/point +EBPFP 1.1857 equivalent bits/point +MSE 1052.216586 +---------------------- -------------------------------------------------------- +Time: 1.684s Load: 0.051s, Pack+Encode: 0.574s, Decode+Unpack: 1.059s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1052.2166 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03345487-toy_17.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03345487-toy_17.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03372029-sketch_14.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03372029-sketch_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 728,492B, BPFP=1.3827 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 709,104B, BPFP=1.3459 +⌛️ [2/4] FRONTEND: Frontend time: 0.607s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.107s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12162214 200.08029640 + layer.39.0 228.06095117 1596.18634597 + ------------------------------------------------------------------------------------- + TOTAL 114.09128665 898.13332119 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1437596 +BPFP 1.3643 bits/point +EBPFP 1.3643 equivalent bits/point +MSE 898.133321 +---------------------- -------------------------------------------------------- +Time: 1.785s Load: 0.070s, Pack+Encode: 0.607s, Decode+Unpack: 1.107s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 898.1333 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03372029-sketch_14.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03372029-sketch_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03424325-misc_4.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03424325-misc_4.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 727,016B, BPFP=1.3799 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 713,132B, BPFP=1.3536 +⌛️ [2/4] FRONTEND: Frontend time: 0.642s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.063s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10761499 163.07006195 + layer.39.0 21.03287666 1286.50036443 + ------------------------------------------------------------------------------------- + TOTAL 10.57024582 724.78521319 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1440148 +BPFP 1.3668 bits/point +EBPFP 1.3668 equivalent bits/point +MSE 724.785213 +---------------------- -------------------------------------------------------- +Time: 1.774s Load: 0.070s, Pack+Encode: 0.642s, Decode+Unpack: 1.063s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 724.7852 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03424325-misc_4.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03424325-misc_4.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03467068-sketch_17.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03467068-sketch_17.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 654,716B, BPFP=1.2427 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 685,288B, BPFP=1.3007 +⌛️ [2/4] FRONTEND: Frontend time: 0.603s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.112s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09564773 27.65043808 + layer.39.0 208.14688107 1573.94679300 + ------------------------------------------------------------------------------------- + TOTAL 104.12126440 800.79861554 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1340004 +BPFP 1.2717 bits/point +EBPFP 1.2717 equivalent bits/point +MSE 800.798616 +---------------------- -------------------------------------------------------- +Time: 1.785s Load: 0.070s, Pack+Encode: 0.603s, Decode+Unpack: 1.112s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 800.7986 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03467068-sketch_17.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03467068-sketch_17.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03481172-sketch_9.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03481172-sketch_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 565,032B, BPFP=1.0725 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 627,500B, BPFP=1.1910 +⌛️ [2/4] FRONTEND: Frontend time: 0.650s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.109s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14641065 211.06524842 + layer.39.0 516.28267736 1794.87973761 + ------------------------------------------------------------------------------------- + TOTAL 258.21454400 1002.97249302 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1192532 +BPFP 1.1318 bits/point +EBPFP 1.1318 equivalent bits/point +MSE 1002.972493 +---------------------- -------------------------------------------------------- +Time: 1.829s Load: 0.070s, Pack+Encode: 0.650s, Decode+Unpack: 1.109s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1002.9725 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03481172-sketch_9.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03481172-sketch_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03494278-deviantart_3.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03494278-deviantart_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 510,620B, BPFP=0.9692 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 582,228B, BPFP=1.1051 +⌛️ [2/4] FRONTEND: Frontend time: 0.610s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.051s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09714438 38.38919385 + layer.39.0 11.38600982 1474.51129738 + ------------------------------------------------------------------------------------- + TOTAL 5.74157710 756.45024561 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1092848 +BPFP 1.0372 bits/point +EBPFP 1.0372 equivalent bits/point +MSE 756.450246 +---------------------- -------------------------------------------------------- +Time: 1.732s Load: 0.070s, Pack+Encode: 0.610s, Decode+Unpack: 1.051s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 756.4502 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03494278-deviantart_3.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03494278-deviantart_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03495258-painting_3.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03495258-painting_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.060s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 738,568B, BPFP=1.4019 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 684,708B, BPFP=1.2996 +⌛️ [2/4] FRONTEND: Frontend time: 0.606s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.112s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10398556 165.25244473 + layer.39.0 359.17207240 1752.18002915 + ------------------------------------------------------------------------------------- + TOTAL 179.63802898 958.71623694 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1423276 +BPFP 1.3507 bits/point +EBPFP 1.3507 equivalent bits/point +MSE 958.716237 +---------------------- -------------------------------------------------------- +Time: 1.778s Load: 0.060s, Pack+Encode: 0.606s, Decode+Unpack: 1.112s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 958.7162 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03495258-painting_3.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03495258-painting_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03498962-sketch_8.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03498962-sketch_8.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 686,100B, BPFP=1.3023 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 655,360B, BPFP=1.2439 +⌛️ [2/4] FRONTEND: Frontend time: 0.607s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.111s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.16074808 541.99854227 + layer.39.0 476.99061589 1792.06899903 + ------------------------------------------------------------------------------------- + TOTAL 238.57568198 1167.03377065 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1341460 +BPFP 1.2731 bits/point +EBPFP 1.2731 equivalent bits/point +MSE 1167.033771 +---------------------- -------------------------------------------------------- +Time: 1.787s Load: 0.070s, Pack+Encode: 0.607s, Decode+Unpack: 1.111s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1167.0338 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03498962-sketch_8.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03498962-sketch_8.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03602883-misc_12.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03602883-misc_12.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 528,964B, BPFP=1.0040 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 544,904B, BPFP=1.0343 +⌛️ [2/4] FRONTEND: Frontend time: 0.606s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.048s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.09080038 51.10434129 + layer.39.0 100.93773536 1406.01494169 + ------------------------------------------------------------------------------------- + TOTAL 54.51426787 728.55964149 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1073868 +BPFP 1.0191 bits/point +EBPFP 1.0191 equivalent bits/point +MSE 728.559641 +---------------------- -------------------------------------------------------- +Time: 1.724s Load: 0.070s, Pack+Encode: 0.606s, Decode+Unpack: 1.048s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 728.5596 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03602883-misc_12.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03602883-misc_12.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03630383-toy_1.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03630383-toy_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 603,092B, BPFP=1.1447 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 656,612B, BPFP=1.2463 +⌛️ [2/4] FRONTEND: Frontend time: 0.558s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.127s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09574974 12.57861470 + layer.39.0 14.66923857 1307.56754130 + ------------------------------------------------------------------------------------- + TOTAL 7.38249415 660.07307800 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1259704 +BPFP 1.1955 bits/point +EBPFP 1.1955 equivalent bits/point +MSE 660.073078 +---------------------- -------------------------------------------------------- +Time: 1.737s Load: 0.052s, Pack+Encode: 0.558s, Decode+Unpack: 1.127s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 660.0731 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03630383-toy_1.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03630383-toy_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03649909-toy_22.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03649909-toy_22.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 629,584B, BPFP=1.1950 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 466,192B, BPFP=0.8849 +⌛️ [2/4] FRONTEND: Frontend time: 0.633s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.103s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09878858 13.34673094 + layer.39.0 29.68475348 1203.51931487 + ------------------------------------------------------------------------------------- + TOTAL 14.89177103 608.43302290 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1095776 +BPFP 1.0399 bits/point +EBPFP 1.0399 equivalent bits/point +MSE 608.433023 +---------------------- -------------------------------------------------------- +Time: 1.805s Load: 0.070s, Pack+Encode: 0.633s, Decode+Unpack: 1.103s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 608.4330 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03649909-toy_22.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03649909-toy_22.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03676483-sculpture_3.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03676483-sculpture_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 637,116B, BPFP=1.2093 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 726,416B, BPFP=1.3788 +⌛️ [2/4] FRONTEND: Frontend time: 0.616s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.069s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09491264 12.58792384 + layer.39.0 32.22669916 1714.06948494 + ------------------------------------------------------------------------------------- + TOTAL 16.16080590 863.32870439 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1363532 +BPFP 1.2940 bits/point +EBPFP 1.2940 equivalent bits/point +MSE 863.328704 +---------------------- -------------------------------------------------------- +Time: 1.756s Load: 0.071s, Pack+Encode: 0.616s, Decode+Unpack: 1.069s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 863.3287 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03676483-sculpture_3.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03676483-sculpture_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03710193-sketch_14.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03710193-sketch_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.058s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 600,064B, BPFP=1.1390 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 616,480B, BPFP=1.1701 +⌛️ [2/4] FRONTEND: Frontend time: 0.583s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.041s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.47394152 293.22524903 + layer.39.0 335.99814747 1533.64103499 + ------------------------------------------------------------------------------------- + TOTAL 168.23604450 913.43314201 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1216544 +BPFP 1.1545 bits/point +EBPFP 1.1545 equivalent bits/point +MSE 913.433142 +---------------------- -------------------------------------------------------- +Time: 1.682s Load: 0.058s, Pack+Encode: 0.583s, Decode+Unpack: 1.041s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 913.4331 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03710193-sketch_14.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03710193-sketch_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03773504-graphic_4.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03773504-graphic_4.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 560,200B, BPFP=1.0633 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 512,732B, BPFP=0.9732 +⌛️ [2/4] FRONTEND: Frontend time: 0.597s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.049s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09681199 12.75031888 + layer.39.0 18.83313593 1229.39492225 + ------------------------------------------------------------------------------------- + TOTAL 9.46497396 621.07262057 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1072932 +BPFP 1.0183 bits/point +EBPFP 1.0183 equivalent bits/point +MSE 621.072621 +---------------------- -------------------------------------------------------- +Time: 1.697s Load: 0.051s, Pack+Encode: 0.597s, Decode+Unpack: 1.049s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 621.0726 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03773504-graphic_4.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03773504-graphic_4.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03775071-painting_1.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03775071-painting_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 696,772B, BPFP=1.3225 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 612,632B, BPFP=1.1628 +⌛️ [2/4] FRONTEND: Frontend time: 0.597s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.103s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11048905 135.68191205 + layer.39.0 386.73560496 1836.15464043 + ------------------------------------------------------------------------------------- + TOTAL 193.42304701 985.91827624 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1309404 +BPFP 1.2427 bits/point +EBPFP 1.2427 equivalent bits/point +MSE 985.918276 +---------------------- -------------------------------------------------------- +Time: 1.752s Load: 0.052s, Pack+Encode: 0.597s, Decode+Unpack: 1.103s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 985.9183 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03775071-painting_1.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03775071-painting_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03888257-cartoon_30.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03888257-cartoon_30.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 689,132B, BPFP=1.3080 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 664,268B, BPFP=1.2608 +⌛️ [2/4] FRONTEND: Frontend time: 0.605s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.104s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13203045 222.82732021 + layer.39.0 375.96832483 1707.09232264 + ------------------------------------------------------------------------------------- + TOTAL 188.05017764 964.95982143 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1353400 +BPFP 1.2844 bits/point +EBPFP 1.2844 equivalent bits/point +MSE 964.959821 +---------------------- -------------------------------------------------------- +Time: 1.781s Load: 0.072s, Pack+Encode: 0.605s, Decode+Unpack: 1.104s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 964.9598 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03888257-cartoon_30.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03888257-cartoon_30.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03930630-toy_2.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03930630-toy_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 594,352B, BPFP=1.1281 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 579,944B, BPFP=1.1008 +⌛️ [2/4] FRONTEND: Frontend time: 0.607s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.055s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09699417 24.79743114 + layer.39.0 46.17573949 1598.17820700 + ------------------------------------------------------------------------------------- + TOTAL 23.13636683 811.48781907 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1174296 +BPFP 1.1145 bits/point +EBPFP 1.1145 equivalent bits/point +MSE 811.487819 +---------------------- -------------------------------------------------------- +Time: 1.732s Load: 0.070s, Pack+Encode: 0.607s, Decode+Unpack: 1.055s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 811.4878 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n03930630-toy_2.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n03930630-toy_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04086273-sticker_5.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04086273-sticker_5.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 628,472B, BPFP=1.1929 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 533,664B, BPFP=1.0129 +⌛️ [2/4] FRONTEND: Frontend time: 0.562s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.035s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10161624 61.85273931 + layer.39.0 24.98063198 1141.54591837 + ------------------------------------------------------------------------------------- + TOTAL 12.54112411 601.69932884 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1162136 +BPFP 1.1029 bits/point +EBPFP 1.1029 equivalent bits/point +MSE 601.699329 +---------------------- -------------------------------------------------------- +Time: 1.647s Load: 0.051s, Pack+Encode: 0.562s, Decode+Unpack: 1.035s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 601.6993 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04086273-sticker_5.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04086273-sticker_5.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04118538-sculpture_0.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04118538-sculpture_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 684,976B, BPFP=1.3001 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 685,368B, BPFP=1.3009 +⌛️ [2/4] FRONTEND: Frontend time: 0.587s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.048s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09846411 74.21326834 + layer.39.0 11.87055944 1455.76457726 + ------------------------------------------------------------------------------------- + TOTAL 5.98451177 764.98892280 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1370344 +BPFP 1.3005 bits/point +EBPFP 1.3005 equivalent bits/point +MSE 764.988923 +---------------------- -------------------------------------------------------- +Time: 1.686s Load: 0.052s, Pack+Encode: 0.587s, Decode+Unpack: 1.048s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 764.9889 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04118538-sculpture_0.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04118538-sculpture_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04133789-cartoon_7.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04133789-cartoon_7.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 768,744B, BPFP=1.4591 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 705,692B, BPFP=1.3395 +⌛️ [2/4] FRONTEND: Frontend time: 0.609s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.071s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13739287 385.04819606 + layer.39.0 370.52532799 2069.43440233 + ------------------------------------------------------------------------------------- + TOTAL 185.33136043 1227.24129920 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1474436 +BPFP 1.3993 bits/point +EBPFP 1.3993 equivalent bits/point +MSE 1227.241299 +---------------------- -------------------------------------------------------- +Time: 1.731s Load: 0.051s, Pack+Encode: 0.609s, Decode+Unpack: 1.071s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1227.2413 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04133789-cartoon_7.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04133789-cartoon_7.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04141076-cartoon_14.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04141076-cartoon_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 605,428B, BPFP=1.1492 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 668,964B, BPFP=1.2697 +⌛️ [2/4] FRONTEND: Frontend time: 0.611s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.059s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11960477 163.59218598 + layer.39.0 53.25505649 1435.67091837 + ------------------------------------------------------------------------------------- + TOTAL 26.68733063 799.63155217 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1274392 +BPFP 1.2094 bits/point +EBPFP 1.2094 equivalent bits/point +MSE 799.631552 +---------------------- -------------------------------------------------------- +Time: 1.740s Load: 0.070s, Pack+Encode: 0.611s, Decode+Unpack: 1.059s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 799.6316 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04141076-cartoon_14.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04141076-cartoon_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04146614-misc_4.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04146614-misc_4.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 658,544B, BPFP=1.2500 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 636,668B, BPFP=1.2084 +⌛️ [2/4] FRONTEND: Frontend time: 0.597s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.054s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10047569 63.09296040 + layer.39.0 167.29959305 1885.39370748 + ------------------------------------------------------------------------------------- + TOTAL 83.70003437 974.24333394 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1295212 +BPFP 1.2292 bits/point +EBPFP 1.2292 equivalent bits/point +MSE 974.243334 +---------------------- -------------------------------------------------------- +Time: 1.703s Load: 0.052s, Pack+Encode: 0.597s, Decode+Unpack: 1.054s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 974.2433 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04146614-misc_4.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04146614-misc_4.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04147183-art_9.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04147183-art_9.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 649,604B, BPFP=1.2330 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 563,836B, BPFP=1.0702 +⌛️ [2/4] FRONTEND: Frontend time: 0.588s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.059s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11332939 263.46568270 + layer.39.0 22.95352360 1476.03753644 + ------------------------------------------------------------------------------------- + TOTAL 11.53342649 869.75160957 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1213440 +BPFP 1.1516 bits/point +EBPFP 1.1516 equivalent bits/point +MSE 869.751610 +---------------------- -------------------------------------------------------- +Time: 1.716s Load: 0.070s, Pack+Encode: 0.588s, Decode+Unpack: 1.059s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 869.7516 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04147183-art_9.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04147183-art_9.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04192698-videogame_16.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04192698-videogame_16.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 670,080B, BPFP=1.2719 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 652,560B, BPFP=1.2386 +⌛️ [2/4] FRONTEND: Frontend time: 0.595s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.091s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09706018 49.24677326 + layer.39.0 404.66927843 1774.60240525 + ------------------------------------------------------------------------------------- + TOTAL 202.38316930 911.92458926 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1322640 +BPFP 1.2552 bits/point +EBPFP 1.2552 equivalent bits/point +MSE 911.924589 +---------------------- -------------------------------------------------------- +Time: 1.757s Load: 0.070s, Pack+Encode: 0.595s, Decode+Unpack: 1.091s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 911.9246 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04192698-videogame_16.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04192698-videogame_16.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04254680-deviantart_17.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04254680-deviantart_17.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 591,216B, BPFP=1.1222 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 653,772B, BPFP=1.2409 +⌛️ [2/4] FRONTEND: Frontend time: 0.603s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.090s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10685510 121.89834639 + layer.39.0 151.81593173 1442.95493197 + ------------------------------------------------------------------------------------- + TOTAL 75.96139341 782.42663918 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1244988 +BPFP 1.1815 bits/point +EBPFP 1.1815 equivalent bits/point +MSE 782.426639 +---------------------- -------------------------------------------------------- +Time: 1.762s Load: 0.070s, Pack+Encode: 0.603s, Decode+Unpack: 1.090s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 782.4266 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04254680-deviantart_17.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04254680-deviantart_17.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04266014-painting_13.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04266014-painting_13.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 629,120B, BPFP=1.1941 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 547,840B, BPFP=1.0398 +⌛️ [2/4] FRONTEND: Frontend time: 0.609s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.055s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09568562 39.20273779 + layer.39.0 29.62437363 1218.95432459 + ------------------------------------------------------------------------------------- + TOTAL 14.86002963 629.07853119 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1176960 +BPFP 1.1170 bits/point +EBPFP 1.1170 equivalent bits/point +MSE 629.078531 +---------------------- -------------------------------------------------------- +Time: 1.734s Load: 0.070s, Pack+Encode: 0.609s, Decode+Unpack: 1.055s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 629.0785 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04266014-painting_13.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04266014-painting_13.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04310018-art_3.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04310018-art_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 655,348B, BPFP=1.2439 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 598,312B, BPFP=1.1356 +⌛️ [2/4] FRONTEND: Frontend time: 0.610s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.063s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13375617 434.16684888 + layer.39.0 75.24515610 1641.67334791 + ------------------------------------------------------------------------------------- + TOTAL 37.68945614 1037.92009840 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1253660 +BPFP 1.1898 bits/point +EBPFP 1.1898 equivalent bits/point +MSE 1037.920098 +---------------------- -------------------------------------------------------- +Time: 1.743s Load: 0.070s, Pack+Encode: 0.610s, Decode+Unpack: 1.063s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1037.9201 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04310018-art_3.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04310018-art_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04347754-sculpture_0.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04347754-sculpture_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 765,908B, BPFP=1.4538 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 607,216B, BPFP=1.1525 +⌛️ [2/4] FRONTEND: Frontend time: 0.613s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.106s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14257451 667.83709913 + layer.39.0 394.23636419 1410.98918853 + ------------------------------------------------------------------------------------- + TOTAL 197.18946935 1039.41314383 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1373124 +BPFP 1.3032 bits/point +EBPFP 1.3032 equivalent bits/point +MSE 1039.413144 +---------------------- -------------------------------------------------------- +Time: 1.789s Load: 0.071s, Pack+Encode: 0.613s, Decode+Unpack: 1.106s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1039.4131 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04347754-sculpture_0.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04347754-sculpture_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04409515-deviantart_1.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04409515-deviantart_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 609,308B, BPFP=1.1565 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 577,308B, BPFP=1.0958 +⌛️ [2/4] FRONTEND: Frontend time: 0.602s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.109s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09627266 13.05316144 + layer.39.0 9.33068077 1190.52891156 + ------------------------------------------------------------------------------------- + TOTAL 4.71347671 601.79103650 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1186616 +BPFP 1.1261 bits/point +EBPFP 1.1261 equivalent bits/point +MSE 601.791037 +---------------------- -------------------------------------------------------- +Time: 1.781s Load: 0.070s, Pack+Encode: 0.602s, Decode+Unpack: 1.109s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 601.7910 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04409515-deviantart_1.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04409515-deviantart_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04487394-deviantart_8.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04487394-deviantart_8.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.085s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 688,580B, BPFP=1.3070 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 630,664B, BPFP=1.1971 +⌛️ [2/4] FRONTEND: Frontend time: 0.618s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.043s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09911632 114.32196003 + layer.39.0 99.63155977 1622.61904762 + ------------------------------------------------------------------------------------- + TOTAL 49.86533804 868.47050383 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1319244 +BPFP 1.2520 bits/point +EBPFP 1.2520 equivalent bits/point +MSE 868.470504 +---------------------- -------------------------------------------------------- +Time: 1.746s Load: 0.085s, Pack+Encode: 0.618s, Decode+Unpack: 1.043s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 868.4705 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04487394-deviantart_8.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04487394-deviantart_8.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04522168-painting_32.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04522168-painting_32.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 638,108B, BPFP=1.2112 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 566,372B, BPFP=1.0750 +⌛️ [2/4] FRONTEND: Frontend time: 0.526s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.033s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11740584 184.87285897 + layer.39.0 10.95138066 1262.95310982 + ------------------------------------------------------------------------------------- + TOTAL 5.53439325 723.91298439 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1204480 +BPFP 1.1431 bits/point +EBPFP 1.1431 equivalent bits/point +MSE 723.912984 +---------------------- -------------------------------------------------------- +Time: 1.611s Load: 0.052s, Pack+Encode: 0.526s, Decode+Unpack: 1.033s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 723.9130 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04522168-painting_32.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04522168-painting_32.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04591713-painting_14.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04591713-painting_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 756,712B, BPFP=1.4363 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 704,844B, BPFP=1.3379 +⌛️ [2/4] FRONTEND: Frontend time: 0.542s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.032s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11212821 103.86996629 + layer.39.0 165.22564383 1495.16180758 + ------------------------------------------------------------------------------------- + TOTAL 82.66888602 799.51588694 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1461556 +BPFP 1.3871 bits/point +EBPFP 1.3871 equivalent bits/point +MSE 799.515887 +---------------------- -------------------------------------------------------- +Time: 1.624s Load: 0.051s, Pack+Encode: 0.542s, Decode+Unpack: 1.032s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 799.5159 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n04591713-painting_14.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n04591713-painting_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07693725-sketch_15.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07693725-sketch_15.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 679,832B, BPFP=1.2904 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 622,388B, BPFP=1.1813 +⌛️ [2/4] FRONTEND: Frontend time: 0.579s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.061s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10569874 270.30147595 + layer.39.0 214.96065658 1762.72497570 + ------------------------------------------------------------------------------------- + TOTAL 107.53317766 1016.51322583 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1302220 +BPFP 1.2359 bits/point +EBPFP 1.2359 equivalent bits/point +MSE 1016.513226 +---------------------- -------------------------------------------------------- +Time: 1.691s Load: 0.051s, Pack+Encode: 0.579s, Decode+Unpack: 1.061s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1016.5132 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07693725-sketch_15.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07693725-sketch_15.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07695742-videogame_1.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07695742-videogame_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 742,912B, BPFP=1.4101 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 655,780B, BPFP=1.2447 +⌛️ [2/4] FRONTEND: Frontend time: 0.617s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.067s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12460778 427.33494291 + layer.39.0 438.29433916 1647.61394558 + ------------------------------------------------------------------------------------- + TOTAL 219.20947347 1037.47444424 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1398692 +BPFP 1.3274 bits/point +EBPFP 1.3274 equivalent bits/point +MSE 1037.474444 +---------------------- -------------------------------------------------------- +Time: 1.754s Load: 0.070s, Pack+Encode: 0.617s, Decode+Unpack: 1.067s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1037.4744 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07695742-videogame_1.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07695742-videogame_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697313-deviantart_7.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697313-deviantart_7.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 626,604B, BPFP=1.1893 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 562,672B, BPFP=1.0680 +⌛️ [2/4] FRONTEND: Frontend time: 0.516s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.022s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09520741 24.50476608 + layer.39.0 14.69109212 1956.57725948 + ------------------------------------------------------------------------------------- + TOTAL 7.39314977 990.54101278 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1189276 +BPFP 1.1287 bits/point +EBPFP 1.1287 equivalent bits/point +MSE 990.541013 +---------------------- -------------------------------------------------------- +Time: 1.589s Load: 0.052s, Pack+Encode: 0.516s, Decode+Unpack: 1.022s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 990.5410 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697313-deviantart_7.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07697313-deviantart_7.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697537-deviantart_16.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697537-deviantart_16.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 661,404B, BPFP=1.2554 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 670,384B, BPFP=1.2724 +⌛️ [2/4] FRONTEND: Frontend time: 0.623s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.067s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09755328 37.70037658 + layer.39.0 90.32537658 1477.17261905 + ------------------------------------------------------------------------------------- + TOTAL 45.21146493 757.43649781 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1331788 +BPFP 1.2639 bits/point +EBPFP 1.2639 equivalent bits/point +MSE 757.436498 +---------------------- -------------------------------------------------------- +Time: 1.742s Load: 0.052s, Pack+Encode: 0.623s, Decode+Unpack: 1.067s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 757.4365 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07697537-deviantart_16.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07697537-deviantart_16.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714571-deviantart_0.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714571-deviantart_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 677,732B, BPFP=1.2864 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 691,988B, BPFP=1.3134 +⌛️ [2/4] FRONTEND: Frontend time: 0.514s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.027s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09528512 12.41995509 + layer.39.0 45.81401467 2076.19096210 + ------------------------------------------------------------------------------------- + TOTAL 22.95464989 1044.30545860 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1369720 +BPFP 1.2999 bits/point +EBPFP 1.2999 equivalent bits/point +MSE 1044.305459 +---------------------- -------------------------------------------------------- +Time: 1.594s Load: 0.052s, Pack+Encode: 0.514s, Decode+Unpack: 1.027s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1044.3055 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714571-deviantart_0.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07714571-deviantart_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714990-toy_14.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714990-toy_14.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 679,128B, BPFP=1.2890 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 698,884B, BPFP=1.3265 +⌛️ [2/4] FRONTEND: Frontend time: 0.535s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.026s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09793257 61.79996508 + layer.39.0 322.50334062 2137.67881438 + ------------------------------------------------------------------------------------- + TOTAL 161.30063660 1099.73938973 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1378012 +BPFP 1.3078 bits/point +EBPFP 1.3078 equivalent bits/point +MSE 1099.739390 +---------------------- -------------------------------------------------------- +Time: 1.612s Load: 0.051s, Pack+Encode: 0.535s, Decode+Unpack: 1.026s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1099.7394 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07714990-toy_14.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07714990-toy_14.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07718472-cartoon_1.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07718472-cartoon_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 525,424B, BPFP=0.9973 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 521,784B, BPFP=0.9904 +⌛️ [2/4] FRONTEND: Frontend time: 0.614s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.049s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11235230 220.08469995 + layer.39.0 14.49942963 1334.06073858 + ------------------------------------------------------------------------------------- + TOTAL 7.30589096 777.07271927 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1047208 +BPFP 0.9938 bits/point +EBPFP 0.9938 equivalent bits/point +MSE 777.072719 +---------------------- -------------------------------------------------------- +Time: 1.714s Load: 0.051s, Pack+Encode: 0.614s, Decode+Unpack: 1.049s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 777.0727 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07718472-cartoon_1.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07718472-cartoon_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07742313-deviantart_8.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07742313-deviantart_8.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 548,260B, BPFP=1.0406 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 548,280B, BPFP=1.0407 +⌛️ [2/4] FRONTEND: Frontend time: 0.564s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.094s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09669835 12.79221236 + layer.39.0 8.77690150 1310.02210884 + ------------------------------------------------------------------------------------- + TOTAL 4.43679992 661.40716060 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1096540 +BPFP 1.0407 bits/point +EBPFP 1.0407 equivalent bits/point +MSE 661.407161 +---------------------- -------------------------------------------------------- +Time: 1.710s Load: 0.052s, Pack+Encode: 0.564s, Decode+Unpack: 1.094s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 661.4072 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07742313-deviantart_8.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07742313-deviantart_8.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07749582-sticker_0.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07749582-sticker_0.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 697,876B, BPFP=1.3246 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 706,968B, BPFP=1.3419 +⌛️ [2/4] FRONTEND: Frontend time: 0.610s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.063s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09550123 37.82434402 + layer.39.0 34.64631545 1788.04968416 + ------------------------------------------------------------------------------------- + TOTAL 17.37090834 912.93701409 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1404844 +BPFP 1.3333 bits/point +EBPFP 1.3333 equivalent bits/point +MSE 912.937014 +---------------------- -------------------------------------------------------- +Time: 1.743s Load: 0.070s, Pack+Encode: 0.610s, Decode+Unpack: 1.063s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 912.9370 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07749582-sticker_0.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07749582-sticker_0.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07753275-painting_2.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07753275-painting_2.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 800,692B, BPFP=1.5198 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 677,044B, BPFP=1.2851 +⌛️ [2/4] FRONTEND: Frontend time: 0.620s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.069s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10429548 532.06438290 + layer.39.0 540.43106171 2466.55466472 + ------------------------------------------------------------------------------------- + TOTAL 270.26767859 1499.30952381 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1477736 +BPFP 1.4024 bits/point +EBPFP 1.4024 equivalent bits/point +MSE 1499.309524 +---------------------- -------------------------------------------------------- +Time: 1.759s Load: 0.071s, Pack+Encode: 0.620s, Decode+Unpack: 1.069s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1499.3095 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07753275-painting_2.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07753275-painting_2.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07768694-painting_12.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07768694-painting_12.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 719,528B, BPFP=1.3657 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 661,472B, BPFP=1.2555 +⌛️ [2/4] FRONTEND: Frontend time: 0.597s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.055s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09821300 149.87008017 + layer.39.0 635.68343052 2220.86345967 + ------------------------------------------------------------------------------------- + TOTAL 317.89082176 1185.36676992 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1381000 +BPFP 1.3106 bits/point +EBPFP 1.3106 equivalent bits/point +MSE 1185.366770 +---------------------- -------------------------------------------------------- +Time: 1.704s Load: 0.052s, Pack+Encode: 0.597s, Decode+Unpack: 1.055s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1185.3668 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07768694-painting_12.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07768694-painting_12.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07920052-painting_1.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07920052-painting_1.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 684,736B, BPFP=1.2997 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 665,652B, BPFP=1.2635 +⌛️ [2/4] FRONTEND: Frontend time: 0.617s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.064s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09582097 49.12515185 + layer.39.0 9.59182155 1378.18622449 + ------------------------------------------------------------------------------------- + TOTAL 4.84382126 713.65568817 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1350388 +BPFP 1.2816 bits/point +EBPFP 1.2816 equivalent bits/point +MSE 713.655688 +---------------------- -------------------------------------------------------- +Time: 1.733s Load: 0.052s, Pack+Encode: 0.617s, Decode+Unpack: 1.064s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 713.6557 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n07920052-painting_1.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n07920052-painting_1.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09472597-painting_15.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09472597-painting_15.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 603,216B, BPFP=1.1450 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 535,192B, BPFP=1.0158 +⌛️ [2/4] FRONTEND: Frontend time: 0.576s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.094s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09164813 12.81817716 + layer.39.0 9.11265014 1251.54652575 + ------------------------------------------------------------------------------------- + TOTAL 4.60214913 632.18235146 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1138408 +BPFP 1.0804 bits/point +EBPFP 1.0804 equivalent bits/point +MSE 632.182351 +---------------------- -------------------------------------------------------- +Time: 1.722s Load: 0.052s, Pack+Encode: 0.576s, Decode+Unpack: 1.094s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 632.1824 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09472597-painting_15.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n09472597-painting_15.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09835506-videogame_3.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09835506-videogame_3.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 638,732B, BPFP=1.2124 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 670,788B, BPFP=1.2732 +⌛️ [2/4] FRONTEND: Frontend time: 0.603s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.102s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09585661 37.95519011 + layer.39.0 12.34450164 1451.70116618 + ------------------------------------------------------------------------------------- + TOTAL 6.22017912 744.82817815 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1309520 +BPFP 1.2428 bits/point +EBPFP 1.2428 equivalent bits/point +MSE 744.828178 +---------------------- -------------------------------------------------------- +Time: 1.776s Load: 0.071s, Pack+Encode: 0.603s, Decode+Unpack: 1.102s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 744.8282 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n09835506-videogame_3.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n09835506-videogame_3.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n12267677-misc_105.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n12267677-misc_105.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 574,068B, BPFP=1.0896 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 644,724B, BPFP=1.2237 +⌛️ [2/4] FRONTEND: Frontend time: 0.607s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.046s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10166193 88.56963678 + layer.39.0 219.41089650 1728.62427114 + ------------------------------------------------------------------------------------- + TOTAL 109.75627921 908.59695396 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1218792 +BPFP 1.1567 bits/point +EBPFP 1.1567 equivalent bits/point +MSE 908.596954 +---------------------- -------------------------------------------------------- +Time: 1.724s Load: 0.070s, Pack+Encode: 0.607s, Decode+Unpack: 1.046s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 908.5970 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-r/n12267677-misc_105.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kr/n12267677-misc_105.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 1.2406 bits/point +Avg EBPFP 1.2406 equivalent bits/point +Avg MSE 903.333311 +Avg Time 1.717s +------------------------ ---------------------------- diff --git a/lambda0.02/hyperprior-featurecoding-8bit-individual/cls_in1kval/dtufc_hyperprior-featurecoding_dinov3-total_individual.log b/lambda0.02/hyperprior-featurecoding-8bit-individual/cls_in1kval/dtufc_hyperprior-featurecoding_dinov3-total_individual.log new file mode 100644 index 0000000000000000000000000000000000000000..9c575a1c5c95e6750f247456bc30693cb57939ca --- /dev/null +++ b/lambda0.02/hyperprior-featurecoding-8bit-individual/cls_in1kval/dtufc_hyperprior-featurecoding_dinov3-total_individual.log @@ -0,0 +1,9344 @@ +Experiment: dtufc_hyperprior-featurecoding_dinov3-total_individual +Log file: output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/dtufc_hyperprior-featurecoding_dinov3-total_individual.log +DTUFCCodecConfig: + arch: hyperprior-featurecoding + handler: dinov3-total + checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.02_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar + transform_type: kmeans + transform_mapping:featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json + bit_depth: 8 + device: cuda:0 +Loading checkpoint: codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.02_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar +Checkpoint epoch: 598 +Loaded hyperprior-featurecoding (1-channel) on cuda:0 +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.9' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_9_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.19' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_19_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.29' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_29_0.json +Loaded quantization points from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json: torch.Size([256]) +Loaded per-key quantization points for key 'layer.39' from featurecoding_utils/transform_mapping/kmeans10samples-8bits/dinov3_total/dep_fewshot-8bit_layer_39_0.json +Loaded per-key mappings: model=dinov3-total + Keys: ['layer.9', 'layer.19', 'layer.29', 'layer.39'] +---------------- ----------------------------------------------------------------------------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +Checkpoint codec_weights/hyperprior_hybrid/bmshj2018-hyperprior_lambda0.02_epochs600_lr0.0001_bs180_patch256-256_checkpoint_best.pth.tar +Transform type kmeans +Transform config featurecoding_utils/transform_mapping/kmeans10samples-8bits/mapping.json +Input ../datasets/FeatureCoding-DINOv3/imagenet1k-val +Output output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval +---------------- ----------------------------------------------------------------------------------------------------------------------------- +Files found: 100 +---------------------------------------------------------------------- + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02825657-ILSVRC2012_val_00001103.zst (1/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02825657-ILSVRC2012_val_00001103.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.086s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 610,948B, BPFP=1.1596 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 609,040B, BPFP=1.1560 +⌛️ [2/4] FRONTEND: Frontend time: 0.770s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.077s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10264289 61.54242590 + layer.39.0 9.47367932 1295.43950437 + ------------------------------------------------------------------------------------- + TOTAL 4.78816110 678.49096514 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1219988 +BPFP 1.1578 bits/point +EBPFP 1.1578 equivalent bits/point +MSE 678.490965 +---------------------- -------------------------------------------------------- +Time: 1.932s Load: 0.086s, Pack+Encode: 0.770s, Decode+Unpack: 1.077s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 678.4910 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02825657-ILSVRC2012_val_00001103.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02825657-ILSVRC2012_val_00001103.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02834397-ILSVRC2012_val_00001252.zst (2/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02834397-ILSVRC2012_val_00001252.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 758,388B, BPFP=1.4395 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 724,456B, BPFP=1.3751 +⌛️ [2/4] FRONTEND: Frontend time: 0.601s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.089s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14789204 793.41472303 + layer.39.0 415.43227648 1963.41654519 + ------------------------------------------------------------------------------------- + TOTAL 207.79008426 1378.41563411 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1482844 +BPFP 1.4073 bits/point +EBPFP 1.4073 equivalent bits/point +MSE 1378.415634 +---------------------- -------------------------------------------------------- +Time: 1.759s Load: 0.069s, Pack+Encode: 0.601s, Decode+Unpack: 1.089s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1378.4156 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02834397-ILSVRC2012_val_00001252.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02834397-ILSVRC2012_val_00001252.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02840245-ILSVRC2012_val_00003446.zst (3/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02840245-ILSVRC2012_val_00003446.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.057s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 610,512B, BPFP=1.1588 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 593,368B, BPFP=1.1263 +⌛️ [2/4] FRONTEND: Frontend time: 0.577s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.066s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10761288 123.69979956 + layer.39.0 28.71820525 1118.89188533 + ------------------------------------------------------------------------------------- + TOTAL 14.41290906 621.29584244 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1203880 +BPFP 1.1425 bits/point +EBPFP 1.1425 equivalent bits/point +MSE 621.295842 +---------------------- -------------------------------------------------------- +Time: 1.700s Load: 0.057s, Pack+Encode: 0.577s, Decode+Unpack: 1.066s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 621.2958 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02840245-ILSVRC2012_val_00003446.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02840245-ILSVRC2012_val_00003446.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02843684-ILSVRC2012_val_00000514.zst (4/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02843684-ILSVRC2012_val_00000514.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 672,408B, BPFP=1.2763 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 598,532B, BPFP=1.1361 +⌛️ [2/4] FRONTEND: Frontend time: 0.588s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.070s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11482661 245.24070700 + layer.39.0 84.54469600 1558.16545190 + ------------------------------------------------------------------------------------- + TOTAL 42.32976130 901.70307945 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1270940 +BPFP 1.2062 bits/point +EBPFP 1.2062 equivalent bits/point +MSE 901.703079 +---------------------- -------------------------------------------------------- +Time: 1.727s Load: 0.069s, Pack+Encode: 0.588s, Decode+Unpack: 1.070s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 901.7031 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02843684-ILSVRC2012_val_00000514.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02843684-ILSVRC2012_val_00000514.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02859443-ILSVRC2012_val_00000193.zst (5/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02859443-ILSVRC2012_val_00000193.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 534,748B, BPFP=1.0150 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 436,232B, BPFP=0.8280 +⌛️ [2/4] FRONTEND: Frontend time: 0.554s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.045s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11417333 135.93954993 + layer.39.0 9.67809406 1373.84098639 + ------------------------------------------------------------------------------------- + TOTAL 4.89613370 754.89026816 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 970980 +BPFP 0.9215 bits/point +EBPFP 0.9215 equivalent bits/point +MSE 754.890268 +---------------------- -------------------------------------------------------- +Time: 1.650s Load: 0.050s, Pack+Encode: 0.554s, Decode+Unpack: 1.045s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 754.8903 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02859443-ILSVRC2012_val_00000193.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02859443-ILSVRC2012_val_00000193.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02860847-ILSVRC2012_val_00000601.zst (6/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02860847-ILSVRC2012_val_00000601.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 723,672B, BPFP=1.3736 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 586,228B, BPFP=1.1127 +⌛️ [2/4] FRONTEND: Frontend time: 0.541s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.041s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12653054 335.45189504 + layer.39.0 266.35249636 1585.91338678 + ------------------------------------------------------------------------------------- + TOTAL 133.23951345 960.68264091 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1309900 +BPFP 1.2431 bits/point +EBPFP 1.2431 equivalent bits/point +MSE 960.682641 +---------------------- -------------------------------------------------------- +Time: 1.632s Load: 0.050s, Pack+Encode: 0.541s, Decode+Unpack: 1.041s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 960.6826 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02860847-ILSVRC2012_val_00000601.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02860847-ILSVRC2012_val_00000601.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02865351-ILSVRC2012_val_00000763.zst (7/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02865351-ILSVRC2012_val_00000763.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 575,656B, BPFP=1.0926 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 665,584B, BPFP=1.2633 +⌛️ [2/4] FRONTEND: Frontend time: 0.597s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.071s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09467571 26.76789890 + layer.39.0 15.47581086 1746.86540330 + ------------------------------------------------------------------------------------- + TOTAL 7.78524328 886.81665110 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1241240 +BPFP 1.1780 bits/point +EBPFP 1.1780 equivalent bits/point +MSE 886.816651 +---------------------- -------------------------------------------------------- +Time: 1.738s Load: 0.070s, Pack+Encode: 0.597s, Decode+Unpack: 1.071s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 886.8167 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02865351-ILSVRC2012_val_00000763.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02865351-ILSVRC2012_val_00000763.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02869837-ILSVRC2012_val_00000906.zst (8/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02869837-ILSVRC2012_val_00000906.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 712,464B, BPFP=1.3523 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 733,132B, BPFP=1.3915 +⌛️ [2/4] FRONTEND: Frontend time: 0.607s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.049s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09659988 74.78552448 + layer.39.0 16.39405483 1577.84718173 + ------------------------------------------------------------------------------------- + TOTAL 8.24532736 826.31635310 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1445596 +BPFP 1.3719 bits/point +EBPFP 1.3719 equivalent bits/point +MSE 826.316353 +---------------------- -------------------------------------------------------- +Time: 1.725s Load: 0.070s, Pack+Encode: 0.607s, Decode+Unpack: 1.049s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 826.3164 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02869837-ILSVRC2012_val_00000906.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02869837-ILSVRC2012_val_00000906.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02870880-ILSVRC2012_val_00003274.zst (9/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02870880-ILSVRC2012_val_00003274.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 729,484B, BPFP=1.3846 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 646,128B, BPFP=1.2264 +⌛️ [2/4] FRONTEND: Frontend time: 0.523s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.046s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10254154 248.47111880 + layer.39.0 9.36513093 1122.81243926 + ------------------------------------------------------------------------------------- + TOTAL 4.73383623 685.64177903 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1375612 +BPFP 1.3055 bits/point +EBPFP 1.3055 equivalent bits/point +MSE 685.641779 +---------------------- -------------------------------------------------------- +Time: 1.619s Load: 0.050s, Pack+Encode: 0.523s, Decode+Unpack: 1.046s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 685.6418 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02870880-ILSVRC2012_val_00003274.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02870880-ILSVRC2012_val_00003274.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02871525-ILSVRC2012_val_00000879.zst (10/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02871525-ILSVRC2012_val_00000879.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 762,100B, BPFP=1.4465 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 680,908B, BPFP=1.2924 +⌛️ [2/4] FRONTEND: Frontend time: 0.518s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.052s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.17072899 645.84280855 + layer.39.0 20.29403547 1292.87512148 + ------------------------------------------------------------------------------------- + TOTAL 10.23238223 969.35896501 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1443008 +BPFP 1.3695 bits/point +EBPFP 1.3695 equivalent bits/point +MSE 969.358965 +---------------------- -------------------------------------------------------- +Time: 1.621s Load: 0.051s, Pack+Encode: 0.518s, Decode+Unpack: 1.052s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 969.3590 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02871525-ILSVRC2012_val_00000879.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02871525-ILSVRC2012_val_00000879.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02877765-ILSVRC2012_val_00000634.zst (11/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02877765-ILSVRC2012_val_00000634.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.073s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 673,972B, BPFP=1.2793 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 656,644B, BPFP=1.2464 +⌛️ [2/4] FRONTEND: Frontend time: 0.608s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.078s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10908128 100.29165907 + layer.39.0 364.97770894 2317.88581147 + ------------------------------------------------------------------------------------- + TOTAL 182.54339511 1209.08873527 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1330616 +BPFP 1.2628 bits/point +EBPFP 1.2628 equivalent bits/point +MSE 1209.088735 +---------------------- -------------------------------------------------------- +Time: 1.760s Load: 0.073s, Pack+Encode: 0.608s, Decode+Unpack: 1.078s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1209.0887 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02877765-ILSVRC2012_val_00000634.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02877765-ILSVRC2012_val_00000634.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02879718-ILSVRC2012_val_00001354.zst (12/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02879718-ILSVRC2012_val_00001354.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 739,008B, BPFP=1.4027 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 666,848B, BPFP=1.2657 +⌛️ [2/4] FRONTEND: Frontend time: 0.592s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.100s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10948122 247.39303936 + layer.39.0 55.92460444 1541.59645287 + ------------------------------------------------------------------------------------- + TOTAL 28.01704283 894.49474611 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1405856 +BPFP 1.3342 bits/point +EBPFP 1.3342 equivalent bits/point +MSE 894.494746 +---------------------- -------------------------------------------------------- +Time: 1.762s Load: 0.070s, Pack+Encode: 0.592s, Decode+Unpack: 1.100s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 894.4947 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02879718-ILSVRC2012_val_00001354.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02879718-ILSVRC2012_val_00001354.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02883205-ILSVRC2012_val_00000126.zst (13/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02883205-ILSVRC2012_val_00000126.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 569,876B, BPFP=1.0817 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 619,964B, BPFP=1.1767 +⌛️ [2/4] FRONTEND: Frontend time: 0.589s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.089s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.06711708 49.80241740 + layer.39.0 7.82069686 1175.58637026 + ------------------------------------------------------------------------------------- + TOTAL 7.94390697 612.69439383 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1189840 +BPFP 1.1292 bits/point +EBPFP 1.1292 equivalent bits/point +MSE 612.694394 +---------------------- -------------------------------------------------------- +Time: 1.748s Load: 0.070s, Pack+Encode: 0.589s, Decode+Unpack: 1.089s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 612.6944 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02883205-ILSVRC2012_val_00000126.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02883205-ILSVRC2012_val_00000126.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892201-ILSVRC2012_val_00001145.zst (14/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892201-ILSVRC2012_val_00001145.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 751,836B, BPFP=1.4270 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 657,552B, BPFP=1.2481 +⌛️ [2/4] FRONTEND: Frontend time: 0.559s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.046s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11297333 359.12272230 + layer.39.0 15.09638643 1271.56766278 + ------------------------------------------------------------------------------------- + TOTAL 7.60467988 815.34519254 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1409388 +BPFP 1.3376 bits/point +EBPFP 1.3376 equivalent bits/point +MSE 815.345193 +---------------------- -------------------------------------------------------- +Time: 1.655s Load: 0.050s, Pack+Encode: 0.559s, Decode+Unpack: 1.046s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 815.3452 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892201-ILSVRC2012_val_00001145.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02892201-ILSVRC2012_val_00001145.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892767-ILSVRC2012_val_00000808.zst (15/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892767-ILSVRC2012_val_00000808.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 674,408B, BPFP=1.2801 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 754,800B, BPFP=1.4327 +⌛️ [2/4] FRONTEND: Frontend time: 0.563s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.048s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09598007 37.44364599 + layer.39.0 31.15013059 1371.83090379 + ------------------------------------------------------------------------------------- + TOTAL 15.62305533 704.63727489 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1429208 +BPFP 1.3564 bits/point +EBPFP 1.3564 equivalent bits/point +MSE 704.637275 +---------------------- -------------------------------------------------------- +Time: 1.661s Load: 0.050s, Pack+Encode: 0.563s, Decode+Unpack: 1.048s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 704.6373 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02892767-ILSVRC2012_val_00000808.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02892767-ILSVRC2012_val_00000808.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02895154-ILSVRC2012_val_00000080.zst (16/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02895154-ILSVRC2012_val_00000080.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 689,020B, BPFP=1.3078 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 728,688B, BPFP=1.3831 +⌛️ [2/4] FRONTEND: Frontend time: 0.594s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.098s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09530723 111.77779549 + layer.39.0 971.40427600 2793.58309038 + ------------------------------------------------------------------------------------- + TOTAL 485.74979162 1452.68044294 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1417708 +BPFP 1.3455 bits/point +EBPFP 1.3455 equivalent bits/point +MSE 1452.680443 +---------------------- -------------------------------------------------------- +Time: 1.762s Load: 0.070s, Pack+Encode: 0.594s, Decode+Unpack: 1.098s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1452.6804 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02895154-ILSVRC2012_val_00000080.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02895154-ILSVRC2012_val_00000080.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02906734-ILSVRC2012_val_00002937.zst (17/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02906734-ILSVRC2012_val_00002937.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 701,704B, BPFP=1.3319 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 577,132B, BPFP=1.0954 +⌛️ [2/4] FRONTEND: Frontend time: 0.593s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.068s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09767962 39.97040513 + layer.39.0 32.09536716 1350.44861516 + ------------------------------------------------------------------------------------- + TOTAL 16.09652339 695.20951014 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1278836 +BPFP 1.2137 bits/point +EBPFP 1.2137 equivalent bits/point +MSE 695.209510 +---------------------- -------------------------------------------------------- +Time: 1.730s Load: 0.070s, Pack+Encode: 0.593s, Decode+Unpack: 1.068s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 695.2095 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02906734-ILSVRC2012_val_00002937.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02906734-ILSVRC2012_val_00002937.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02910353-ILSVRC2012_val_00000558.zst (18/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02910353-ILSVRC2012_val_00000558.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 630,860B, BPFP=1.1974 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 620,812B, BPFP=1.1784 +⌛️ [2/4] FRONTEND: Frontend time: 0.578s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.093s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11017090 114.17722759 + layer.39.0 483.40066205 1801.78644315 + ------------------------------------------------------------------------------------- + TOTAL 241.75541648 957.98183537 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1251672 +BPFP 1.1879 bits/point +EBPFP 1.1879 equivalent bits/point +MSE 957.981835 +---------------------- -------------------------------------------------------- +Time: 1.742s Load: 0.070s, Pack+Encode: 0.578s, Decode+Unpack: 1.093s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 957.9818 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02910353-ILSVRC2012_val_00000558.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02910353-ILSVRC2012_val_00000558.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02916936-ILSVRC2012_val_00000366.zst (19/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02916936-ILSVRC2012_val_00000366.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 538,536B, BPFP=1.0222 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 625,148B, BPFP=1.1866 +⌛️ [2/4] FRONTEND: Frontend time: 0.584s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.055s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10657579 62.68976251 + layer.39.0 435.18944363 2090.37463557 + ------------------------------------------------------------------------------------- + TOTAL 217.64800971 1076.53219904 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1163684 +BPFP 1.1044 bits/point +EBPFP 1.1044 equivalent bits/point +MSE 1076.532199 +---------------------- -------------------------------------------------------- +Time: 1.710s Load: 0.071s, Pack+Encode: 0.584s, Decode+Unpack: 1.055s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1076.5322 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02916936-ILSVRC2012_val_00000366.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02916936-ILSVRC2012_val_00000366.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02917067-ILSVRC2012_val_00000562.zst (20/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02917067-ILSVRC2012_val_00000562.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 732,536B, BPFP=1.3904 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 653,708B, BPFP=1.2408 +⌛️ [2/4] FRONTEND: Frontend time: 0.529s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.033s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10760244 198.98965926 + layer.39.0 37.55795979 1644.32555879 + ------------------------------------------------------------------------------------- + TOTAL 18.83278111 921.65760903 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1386244 +BPFP 1.3156 bits/point +EBPFP 1.3156 equivalent bits/point +MSE 921.657609 +---------------------- -------------------------------------------------------- +Time: 1.612s Load: 0.050s, Pack+Encode: 0.529s, Decode+Unpack: 1.033s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 921.6576 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02917067-ILSVRC2012_val_00000562.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02917067-ILSVRC2012_val_00000562.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02930766-ILSVRC2012_val_00000056.zst (21/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02930766-ILSVRC2012_val_00000056.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 713,440B, BPFP=1.3542 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 702,344B, BPFP=1.3331 +⌛️ [2/4] FRONTEND: Frontend time: 0.592s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.082s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10591127 187.97526421 + layer.39.0 18.32421875 1610.68974733 + ------------------------------------------------------------------------------------- + TOTAL 9.21506501 899.33250577 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1415784 +BPFP 1.3436 bits/point +EBPFP 1.3436 equivalent bits/point +MSE 899.332506 +---------------------- -------------------------------------------------------- +Time: 1.743s Load: 0.070s, Pack+Encode: 0.592s, Decode+Unpack: 1.082s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 899.3325 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02930766-ILSVRC2012_val_00000056.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02930766-ILSVRC2012_val_00000056.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02939185-ILSVRC2012_val_00000302.zst (22/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02939185-ILSVRC2012_val_00000302.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 699,256B, BPFP=1.3272 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 636,304B, BPFP=1.2078 +⌛️ [2/4] FRONTEND: Frontend time: 0.530s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.040s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09694758 62.34745885 + layer.39.0 25.52453269 1754.65330418 + ------------------------------------------------------------------------------------- + TOTAL 12.81074014 908.50038151 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1335560 +BPFP 1.2675 bits/point +EBPFP 1.2675 equivalent bits/point +MSE 908.500382 +---------------------- -------------------------------------------------------- +Time: 1.620s Load: 0.050s, Pack+Encode: 0.530s, Decode+Unpack: 1.040s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 908.5004 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02939185-ILSVRC2012_val_00000302.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02939185-ILSVRC2012_val_00000302.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02950826-ILSVRC2012_val_00000392.zst (23/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02950826-ILSVRC2012_val_00000392.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 691,216B, BPFP=1.3120 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 616,740B, BPFP=1.1706 +⌛️ [2/4] FRONTEND: Frontend time: 0.515s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.032s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10873010 237.99588496 + layer.39.0 707.96944849 2193.72497570 + ------------------------------------------------------------------------------------- + TOTAL 354.03908930 1215.86043033 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1307956 +BPFP 1.2413 bits/point +EBPFP 1.2413 equivalent bits/point +MSE 1215.860430 +---------------------- -------------------------------------------------------- +Time: 1.598s Load: 0.051s, Pack+Encode: 0.515s, Decode+Unpack: 1.032s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1215.8604 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02950826-ILSVRC2012_val_00000392.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02950826-ILSVRC2012_val_00000392.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951358-ILSVRC2012_val_00000347.zst (24/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951358-ILSVRC2012_val_00000347.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 673,312B, BPFP=1.2780 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 572,196B, BPFP=1.0861 +⌛️ [2/4] FRONTEND: Frontend time: 0.520s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.040s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12200860 271.52778790 + layer.39.0 237.66299198 1556.94327017 + ------------------------------------------------------------------------------------- + TOTAL 118.89250029 914.23552903 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1245508 +BPFP 1.1820 bits/point +EBPFP 1.1820 equivalent bits/point +MSE 914.235529 +---------------------- -------------------------------------------------------- +Time: 1.610s Load: 0.050s, Pack+Encode: 0.520s, Decode+Unpack: 1.040s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 914.2355 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951358-ILSVRC2012_val_00000347.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02951358-ILSVRC2012_val_00000347.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951585-ILSVRC2012_val_00000101.zst (25/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951585-ILSVRC2012_val_00000101.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 484,100B, BPFP=0.9189 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 583,556B, BPFP=1.1076 +⌛️ [2/4] FRONTEND: Frontend time: 0.510s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.026s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 8.07385432 38.05749666 + layer.39.0 181.90962099 1878.69788630 + ------------------------------------------------------------------------------------- + TOTAL 94.99173765 958.37769148 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1067656 +BPFP 1.0132 bits/point +EBPFP 1.0132 equivalent bits/point +MSE 958.377691 +---------------------- -------------------------------------------------------- +Time: 1.586s Load: 0.051s, Pack+Encode: 0.510s, Decode+Unpack: 1.026s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 958.3777 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02951585-ILSVRC2012_val_00000101.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02951585-ILSVRC2012_val_00000101.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02963159-ILSVRC2012_val_00000061.zst (26/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02963159-ILSVRC2012_val_00000061.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 681,680B, BPFP=1.2939 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 666,880B, BPFP=1.2658 +⌛️ [2/4] FRONTEND: Frontend time: 0.520s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.079s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11232698 231.62293489 + layer.39.0 24.77479842 1479.50000000 + ------------------------------------------------------------------------------------- + TOTAL 12.44356270 855.56146744 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1348560 +BPFP 1.2798 bits/point +EBPFP 1.2798 equivalent bits/point +MSE 855.561467 +---------------------- -------------------------------------------------------- +Time: 1.649s Load: 0.050s, Pack+Encode: 0.520s, Decode+Unpack: 1.079s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 855.5615 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02963159-ILSVRC2012_val_00000061.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02963159-ILSVRC2012_val_00000061.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02965783-ILSVRC2012_val_00000213.zst (27/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02965783-ILSVRC2012_val_00000213.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 644,272B, BPFP=1.2229 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 660,676B, BPFP=1.2540 +⌛️ [2/4] FRONTEND: Frontend time: 0.596s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.080s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09516161 12.90015716 + layer.39.0 223.32294704 1591.28547133 + ------------------------------------------------------------------------------------- + TOTAL 111.70905432 802.09281425 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1304948 +BPFP 1.2384 bits/point +EBPFP 1.2384 equivalent bits/point +MSE 802.092814 +---------------------- -------------------------------------------------------- +Time: 1.746s Load: 0.070s, Pack+Encode: 0.596s, Decode+Unpack: 1.080s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 802.0928 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02965783-ILSVRC2012_val_00000213.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02965783-ILSVRC2012_val_00000213.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966193-ILSVRC2012_val_00000074.zst (28/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966193-ILSVRC2012_val_00000074.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 776,388B, BPFP=1.4736 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 797,724B, BPFP=1.5141 +⌛️ [2/4] FRONTEND: Frontend time: 0.550s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.073s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12190965 459.56948494 + layer.39.0 378.75431244 2438.28595724 + ------------------------------------------------------------------------------------- + TOTAL 189.43811104 1448.92772109 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1574112 +BPFP 1.4939 bits/point +EBPFP 1.4939 equivalent bits/point +MSE 1448.927721 +---------------------- -------------------------------------------------------- +Time: 1.674s Load: 0.051s, Pack+Encode: 0.550s, Decode+Unpack: 1.073s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1448.9277 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966193-ILSVRC2012_val_00000074.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02966193-ILSVRC2012_val_00000074.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966687-ILSVRC2012_val_00001041.zst (29/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966687-ILSVRC2012_val_00001041.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 673,308B, BPFP=1.2780 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 684,492B, BPFP=1.2992 +⌛️ [2/4] FRONTEND: Frontend time: 0.532s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.035s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12487827 233.62026239 + layer.39.0 254.07423773 1892.39893100 + ------------------------------------------------------------------------------------- + TOTAL 127.09955800 1063.00959670 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1357800 +BPFP 1.2886 bits/point +EBPFP 1.2886 equivalent bits/point +MSE 1063.009597 +---------------------- -------------------------------------------------------- +Time: 1.618s Load: 0.051s, Pack+Encode: 0.532s, Decode+Unpack: 1.035s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1063.0096 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02966687-ILSVRC2012_val_00001041.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02966687-ILSVRC2012_val_00001041.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02971356-ILSVRC2012_val_00000019.zst (30/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02971356-ILSVRC2012_val_00000019.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 603,772B, BPFP=1.1460 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 522,228B, BPFP=0.9912 +⌛️ [2/4] FRONTEND: Frontend time: 0.572s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.045s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09754465 37.83218310 + layer.39.0 24.51746044 1187.02210884 + ------------------------------------------------------------------------------------- + TOTAL 12.30750255 612.42714597 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1126000 +BPFP 1.0686 bits/point +EBPFP 1.0686 equivalent bits/point +MSE 612.427146 +---------------------- -------------------------------------------------------- +Time: 1.667s Load: 0.050s, Pack+Encode: 0.572s, Decode+Unpack: 1.045s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 612.4271 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02971356-ILSVRC2012_val_00000019.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02971356-ILSVRC2012_val_00000019.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02978881-ILSVRC2012_val_00000353.zst (31/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02978881-ILSVRC2012_val_00000353.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 695,276B, BPFP=1.3197 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 710,096B, BPFP=1.3478 +⌛️ [2/4] FRONTEND: Frontend time: 0.564s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.084s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09975241 38.24419947 + layer.39.0 226.62124939 1688.93561710 + ------------------------------------------------------------------------------------- + TOTAL 113.36050090 863.58990828 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1405372 +BPFP 1.3338 bits/point +EBPFP 1.3338 equivalent bits/point +MSE 863.589908 +---------------------- -------------------------------------------------------- +Time: 1.718s Load: 0.070s, Pack+Encode: 0.564s, Decode+Unpack: 1.084s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 863.5899 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02978881-ILSVRC2012_val_00000353.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02978881-ILSVRC2012_val_00000353.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02980441-ILSVRC2012_val_00000122.zst (32/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02980441-ILSVRC2012_val_00000122.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 659,524B, BPFP=1.2518 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 590,240B, BPFP=1.1203 +⌛️ [2/4] FRONTEND: Frontend time: 0.525s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.040s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10186533 73.24424502 + layer.39.0 8.25151846 1281.18658892 + ------------------------------------------------------------------------------------- + TOTAL 4.17669190 677.21541697 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1249764 +BPFP 1.1861 bits/point +EBPFP 1.1861 equivalent bits/point +MSE 677.215417 +---------------------- -------------------------------------------------------- +Time: 1.616s Load: 0.050s, Pack+Encode: 0.525s, Decode+Unpack: 1.040s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 677.2154 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02980441-ILSVRC2012_val_00000122.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02980441-ILSVRC2012_val_00000122.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02988304-ILSVRC2012_val_00003491.zst (33/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02988304-ILSVRC2012_val_00003491.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.052s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 626,436B, BPFP=1.1890 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 591,952B, BPFP=1.1236 +⌛️ [2/4] FRONTEND: Frontend time: 0.511s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.038s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10176498 85.96111972 + layer.39.0 516.16180758 2167.63994169 + ------------------------------------------------------------------------------------- + TOTAL 258.13178628 1126.80053070 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1218388 +BPFP 1.1563 bits/point +EBPFP 1.1563 equivalent bits/point +MSE 1126.800531 +---------------------- -------------------------------------------------------- +Time: 1.601s Load: 0.052s, Pack+Encode: 0.511s, Decode+Unpack: 1.038s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1126.8005 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02988304-ILSVRC2012_val_00003491.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02988304-ILSVRC2012_val_00003491.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992211-ILSVRC2012_val_00000108.zst (34/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992211-ILSVRC2012_val_00000108.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 715,592B, BPFP=1.3583 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 662,560B, BPFP=1.2576 +⌛️ [2/4] FRONTEND: Frontend time: 0.548s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.065s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10107529 37.49789693 + layer.39.0 89.13089923 2268.32410107 + ------------------------------------------------------------------------------------- + TOTAL 44.61598726 1152.91099900 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1378152 +BPFP 1.3079 bits/point +EBPFP 1.3079 equivalent bits/point +MSE 1152.910999 +---------------------- -------------------------------------------------------- +Time: 1.664s Load: 0.051s, Pack+Encode: 0.548s, Decode+Unpack: 1.065s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1152.9110 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992211-ILSVRC2012_val_00000108.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02992211-ILSVRC2012_val_00000108.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992529-ILSVRC2012_val_00000089.zst (35/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992529-ILSVRC2012_val_00000089.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 671,996B, BPFP=1.2755 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 717,076B, BPFP=1.3611 +⌛️ [2/4] FRONTEND: Frontend time: 0.512s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.051s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11197385 125.19265519 + layer.39.0 964.25631681 2548.53158406 + ------------------------------------------------------------------------------------- + TOTAL 482.18414533 1336.86211962 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1389072 +BPFP 1.3183 bits/point +EBPFP 1.3183 equivalent bits/point +MSE 1336.862120 +---------------------- -------------------------------------------------------- +Time: 1.633s Load: 0.070s, Pack+Encode: 0.512s, Decode+Unpack: 1.051s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1336.8621 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02992529-ILSVRC2012_val_00000089.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02992529-ILSVRC2012_val_00000089.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02999410-ILSVRC2012_val_00000376.zst (36/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02999410-ILSVRC2012_val_00000376.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 701,012B, BPFP=1.3306 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 667,768B, BPFP=1.2675 +⌛️ [2/4] FRONTEND: Frontend time: 0.534s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.030s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10398186 88.86636753 + layer.39.0 145.78410471 1440.44861516 + ------------------------------------------------------------------------------------- + TOTAL 72.94404329 764.65749134 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1368780 +BPFP 1.2990 bits/point +EBPFP 1.2990 equivalent bits/point +MSE 764.657491 +---------------------- -------------------------------------------------------- +Time: 1.614s Load: 0.050s, Pack+Encode: 0.534s, Decode+Unpack: 1.030s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 764.6575 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n02999410-ILSVRC2012_val_00000376.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n02999410-ILSVRC2012_val_00000376.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000134-ILSVRC2012_val_00001094.zst (37/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000134-ILSVRC2012_val_00001094.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 667,068B, BPFP=1.2661 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 732,572B, BPFP=1.3905 +⌛️ [2/4] FRONTEND: Frontend time: 0.549s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.097s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09696872 36.87134809 + layer.39.0 22.81329530 1464.84754616 + ------------------------------------------------------------------------------------- + TOTAL 11.45513201 750.85944713 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1399640 +BPFP 1.3283 bits/point +EBPFP 1.3283 equivalent bits/point +MSE 750.859447 +---------------------- -------------------------------------------------------- +Time: 1.717s Load: 0.070s, Pack+Encode: 0.549s, Decode+Unpack: 1.097s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 750.8594 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000134-ILSVRC2012_val_00001094.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03000134-ILSVRC2012_val_00001094.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000247-ILSVRC2012_val_00002280.zst (38/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000247-ILSVRC2012_val_00002280.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 857,520B, BPFP=1.6276 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 571,708B, BPFP=1.0851 +⌛️ [2/4] FRONTEND: Frontend time: 0.574s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.067s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.29135144 1868.93986880 + layer.39.0 428.26293732 1945.47436832 + ------------------------------------------------------------------------------------- + TOTAL 214.27714438 1907.20711856 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1429228 +BPFP 1.3564 bits/point +EBPFP 1.3564 equivalent bits/point +MSE 1907.207119 +---------------------- -------------------------------------------------------- +Time: 1.691s Load: 0.050s, Pack+Encode: 0.574s, Decode+Unpack: 1.067s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1907.2071 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000247-ILSVRC2012_val_00002280.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03000247-ILSVRC2012_val_00002280.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000684-ILSVRC2012_val_00000537.zst (39/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000684-ILSVRC2012_val_00000537.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 776,808B, BPFP=1.4744 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 703,192B, BPFP=1.3347 +⌛️ [2/4] FRONTEND: Frontend time: 0.533s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.043s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13150742 543.57847425 + layer.39.0 55.24585459 1535.01797862 + ------------------------------------------------------------------------------------- + TOTAL 27.68868101 1039.29822643 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1480000 +BPFP 1.4046 bits/point +EBPFP 1.4046 equivalent bits/point +MSE 1039.298226 +---------------------- -------------------------------------------------------- +Time: 1.645s Load: 0.069s, Pack+Encode: 0.533s, Decode+Unpack: 1.043s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1039.2982 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03000684-ILSVRC2012_val_00000537.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03000684-ILSVRC2012_val_00000537.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03014705-ILSVRC2012_val_00001168.zst (40/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03014705-ILSVRC2012_val_00001168.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.049s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 624,964B, BPFP=1.1862 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 636,280B, BPFP=1.2077 +⌛️ [2/4] FRONTEND: Frontend time: 0.545s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.049s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09787338 61.16632501 + layer.39.0 322.89622813 1822.55490768 + ------------------------------------------------------------------------------------- + TOTAL 161.49705076 941.86061634 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1261244 +BPFP 1.1970 bits/point +EBPFP 1.1970 equivalent bits/point +MSE 941.860616 +---------------------- -------------------------------------------------------- +Time: 1.644s Load: 0.049s, Pack+Encode: 0.545s, Decode+Unpack: 1.049s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 941.8606 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03014705-ILSVRC2012_val_00001168.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03014705-ILSVRC2012_val_00001168.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03017168-ILSVRC2012_val_00001601.zst (41/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03017168-ILSVRC2012_val_00001601.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 655,984B, BPFP=1.2451 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 694,916B, BPFP=1.3190 +⌛️ [2/4] FRONTEND: Frontend time: 0.600s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.064s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10213913 88.63734512 + layer.39.0 475.40952988 2414.25048591 + ------------------------------------------------------------------------------------- + TOTAL 237.75583451 1251.44391551 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1350900 +BPFP 1.2821 bits/point +EBPFP 1.2821 equivalent bits/point +MSE 1251.443916 +---------------------- -------------------------------------------------------- +Time: 1.715s Load: 0.051s, Pack+Encode: 0.600s, Decode+Unpack: 1.064s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1251.4439 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03017168-ILSVRC2012_val_00001601.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03017168-ILSVRC2012_val_00001601.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03018349-ILSVRC2012_val_00000346.zst (42/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03018349-ILSVRC2012_val_00000346.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 695,024B, BPFP=1.3192 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 777,384B, BPFP=1.4755 +⌛️ [2/4] FRONTEND: Frontend time: 0.598s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.076s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09959339 65.05836978 + layer.39.0 56.59841169 1596.88739067 + ------------------------------------------------------------------------------------- + TOTAL 28.34900254 830.97288022 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1472408 +BPFP 1.3974 bits/point +EBPFP 1.3974 equivalent bits/point +MSE 830.972880 +---------------------- -------------------------------------------------------- +Time: 1.744s Load: 0.070s, Pack+Encode: 0.598s, Decode+Unpack: 1.076s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 830.9729 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03018349-ILSVRC2012_val_00000346.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03018349-ILSVRC2012_val_00000346.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03026506-ILSVRC2012_val_00001908.zst (43/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03026506-ILSVRC2012_val_00001908.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 716,596B, BPFP=1.3602 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 750,060B, BPFP=1.4237 +⌛️ [2/4] FRONTEND: Frontend time: 0.595s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.078s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10977067 110.57304574 + layer.39.0 668.54063411 2545.38386783 + ------------------------------------------------------------------------------------- + TOTAL 334.32520239 1327.97845678 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1466656 +BPFP 1.3919 bits/point +EBPFP 1.3919 equivalent bits/point +MSE 1327.978457 +---------------------- -------------------------------------------------------- +Time: 1.743s Load: 0.070s, Pack+Encode: 0.595s, Decode+Unpack: 1.078s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1327.9785 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03026506-ILSVRC2012_val_00001908.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03026506-ILSVRC2012_val_00001908.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03028079-ILSVRC2012_val_00003351.zst (44/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03028079-ILSVRC2012_val_00003351.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 663,888B, BPFP=1.2601 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 654,744B, BPFP=1.2428 +⌛️ [2/4] FRONTEND: Frontend time: 0.527s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.070s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10934904 150.41742590 + layer.39.0 15.31112010 1344.64006317 + ------------------------------------------------------------------------------------- + TOTAL 7.71023457 747.52874453 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1318632 +BPFP 1.2514 bits/point +EBPFP 1.2514 equivalent bits/point +MSE 747.528745 +---------------------- -------------------------------------------------------- +Time: 1.647s Load: 0.051s, Pack+Encode: 0.527s, Decode+Unpack: 1.070s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 747.5287 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03028079-ILSVRC2012_val_00003351.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03028079-ILSVRC2012_val_00003351.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03032252-ILSVRC2012_val_00000086.zst (45/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03032252-ILSVRC2012_val_00000086.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 747,296B, BPFP=1.4184 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 692,092B, BPFP=1.3136 +⌛️ [2/4] FRONTEND: Frontend time: 0.519s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.035s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.13507480 360.03155369 + layer.39.0 103.55165816 1391.02538873 + ------------------------------------------------------------------------------------- + TOTAL 51.84336648 875.52847121 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1439388 +BPFP 1.3660 bits/point +EBPFP 1.3660 equivalent bits/point +MSE 875.528471 +---------------------- -------------------------------------------------------- +Time: 1.605s Load: 0.051s, Pack+Encode: 0.519s, Decode+Unpack: 1.035s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 875.5285 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03032252-ILSVRC2012_val_00000086.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03032252-ILSVRC2012_val_00000086.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03041632-ILSVRC2012_val_00000564.zst (46/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03041632-ILSVRC2012_val_00000564.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.058s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 576,160B, BPFP=1.0936 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 603,628B, BPFP=1.1457 +⌛️ [2/4] FRONTEND: Frontend time: 0.592s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.038s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10123130 174.09804725 + layer.39.0 371.34277818 2286.32264334 + ------------------------------------------------------------------------------------- + TOTAL 185.72200474 1230.21034530 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1179788 +BPFP 1.1197 bits/point +EBPFP 1.1197 equivalent bits/point +MSE 1230.210345 +---------------------- -------------------------------------------------------- +Time: 1.688s Load: 0.058s, Pack+Encode: 0.592s, Decode+Unpack: 1.038s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1230.2103 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03041632-ILSVRC2012_val_00000564.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03041632-ILSVRC2012_val_00000564.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03042490-ILSVRC2012_val_00001426.zst (47/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03042490-ILSVRC2012_val_00001426.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 731,924B, BPFP=1.3893 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 639,132B, BPFP=1.2131 +⌛️ [2/4] FRONTEND: Frontend time: 0.588s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.069s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10706725 125.13043610 + layer.39.0 141.71039845 2022.89346453 + ------------------------------------------------------------------------------------- + TOTAL 70.90873285 1074.01195032 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1371056 +BPFP 1.3012 bits/point +EBPFP 1.3012 equivalent bits/point +MSE 1074.011950 +---------------------- -------------------------------------------------------- +Time: 1.728s Load: 0.070s, Pack+Encode: 0.588s, Decode+Unpack: 1.069s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1074.0120 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03042490-ILSVRC2012_val_00001426.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03042490-ILSVRC2012_val_00001426.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03047690-ILSVRC2012_val_00001500.zst (48/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03047690-ILSVRC2012_val_00001500.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 640,752B, BPFP=1.2162 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 713,580B, BPFP=1.3544 +⌛️ [2/4] FRONTEND: Frontend time: 0.609s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.053s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09570478 24.94929278 + layer.39.0 226.76483540 2023.31608358 + ------------------------------------------------------------------------------------- + TOTAL 113.43027009 1024.13268818 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1354332 +BPFP 1.2853 bits/point +EBPFP 1.2853 equivalent bits/point +MSE 1024.132688 +---------------------- -------------------------------------------------------- +Time: 1.732s Load: 0.070s, Pack+Encode: 0.609s, Decode+Unpack: 1.053s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1024.1327 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03047690-ILSVRC2012_val_00001500.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03047690-ILSVRC2012_val_00001500.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03062245-ILSVRC2012_val_00000344.zst (49/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03062245-ILSVRC2012_val_00000344.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 620,420B, BPFP=1.1776 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 598,156B, BPFP=1.1353 +⌛️ [2/4] FRONTEND: Frontend time: 0.537s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.062s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09619164 13.80092266 + layer.39.0 46.71096787 1448.50279397 + ------------------------------------------------------------------------------------- + TOTAL 23.40357976 731.15185832 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1218576 +BPFP 1.1565 bits/point +EBPFP 1.1565 equivalent bits/point +MSE 731.151858 +---------------------- -------------------------------------------------------- +Time: 1.669s Load: 0.071s, Pack+Encode: 0.537s, Decode+Unpack: 1.062s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 731.1519 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03062245-ILSVRC2012_val_00000344.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03062245-ILSVRC2012_val_00000344.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063599-ILSVRC2012_val_00000164.zst (50/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063599-ILSVRC2012_val_00000164.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 684,308B, BPFP=1.2989 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 765,396B, BPFP=1.4528 +⌛️ [2/4] FRONTEND: Frontend time: 0.557s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.045s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10111790 73.07635599 + layer.39.0 9.80528160 1298.97376093 + ------------------------------------------------------------------------------------- + TOTAL 4.95319975 686.02505846 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1449704 +BPFP 1.3758 bits/point +EBPFP 1.3758 equivalent bits/point +MSE 686.025058 +---------------------- -------------------------------------------------------- +Time: 1.654s Load: 0.051s, Pack+Encode: 0.557s, Decode+Unpack: 1.045s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 686.0251 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063599-ILSVRC2012_val_00000164.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03063599-ILSVRC2012_val_00000164.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063689-ILSVRC2012_val_00001940.zst (51/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063689-ILSVRC2012_val_00001940.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 649,352B, BPFP=1.2325 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 686,044B, BPFP=1.3022 +⌛️ [2/4] FRONTEND: Frontend time: 0.529s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.042s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09645106 36.95404747 + layer.39.0 18.48014797 1841.82470845 + ------------------------------------------------------------------------------------- + TOTAL 9.28829952 939.38937796 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1335396 +BPFP 1.2673 bits/point +EBPFP 1.2673 equivalent bits/point +MSE 939.389378 +---------------------- -------------------------------------------------------- +Time: 1.622s Load: 0.051s, Pack+Encode: 0.529s, Decode+Unpack: 1.042s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 939.3894 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03063689-ILSVRC2012_val_00001940.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03063689-ILSVRC2012_val_00001940.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03065424-ILSVRC2012_val_00000915.zst (52/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03065424-ILSVRC2012_val_00000915.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 754,608B, BPFP=1.4323 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 730,200B, BPFP=1.3860 +⌛️ [2/4] FRONTEND: Frontend time: 0.539s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.066s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12384982 257.93330904 + layer.39.0 2154.15986395 3189.19436346 + ------------------------------------------------------------------------------------- + TOTAL 1077.14185688 1723.56383625 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1484808 +BPFP 1.4091 bits/point +EBPFP 1.4091 equivalent bits/point +MSE 1723.563836 +---------------------- -------------------------------------------------------- +Time: 1.655s Load: 0.051s, Pack+Encode: 0.539s, Decode+Unpack: 1.066s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1723.5638 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03065424-ILSVRC2012_val_00000915.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03065424-ILSVRC2012_val_00000915.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03075370-ILSVRC2012_val_00004971.zst (53/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03075370-ILSVRC2012_val_00004971.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.057s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 657,888B, BPFP=1.2487 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 607,528B, BPFP=1.1531 +⌛️ [2/4] FRONTEND: Frontend time: 0.596s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.073s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10672879 88.18291424 + layer.39.0 301.29020894 1575.64030612 + ------------------------------------------------------------------------------------- + TOTAL 150.69846886 831.91161018 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1265416 +BPFP 1.2009 bits/point +EBPFP 1.2009 equivalent bits/point +MSE 831.911610 +---------------------- -------------------------------------------------------- +Time: 1.726s Load: 0.057s, Pack+Encode: 0.596s, Decode+Unpack: 1.073s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 831.9116 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03075370-ILSVRC2012_val_00004971.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03075370-ILSVRC2012_val_00004971.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03089624-ILSVRC2012_val_00001190.zst (54/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03089624-ILSVRC2012_val_00001190.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 704,436B, BPFP=1.3371 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 723,412B, BPFP=1.3731 +⌛️ [2/4] FRONTEND: Frontend time: 0.537s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.084s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10385029 66.78336826 + layer.39.0 606.38896987 2337.16520894 + ------------------------------------------------------------------------------------- + TOTAL 303.24641008 1201.97428860 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1427848 +BPFP 1.3551 bits/point +EBPFP 1.3551 equivalent bits/point +MSE 1201.974289 +---------------------- -------------------------------------------------------- +Time: 1.671s Load: 0.051s, Pack+Encode: 0.537s, Decode+Unpack: 1.084s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1201.9743 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03089624-ILSVRC2012_val_00001190.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03089624-ILSVRC2012_val_00001190.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03095699-ILSVRC2012_val_00000403.zst (55/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03095699-ILSVRC2012_val_00000403.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 738,548B, BPFP=1.4018 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 687,408B, BPFP=1.3048 +⌛️ [2/4] FRONTEND: Frontend time: 0.622s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.055s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.12139760 474.45690598 + layer.39.0 62.59250486 1750.54227405 + ------------------------------------------------------------------------------------- + TOTAL 31.35695123 1112.49959001 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1425956 +BPFP 1.3533 bits/point +EBPFP 1.3533 equivalent bits/point +MSE 1112.499590 +---------------------- -------------------------------------------------------- +Time: 1.729s Load: 0.051s, Pack+Encode: 0.622s, Decode+Unpack: 1.055s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1112.4996 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03095699-ILSVRC2012_val_00000403.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03095699-ILSVRC2012_val_00000403.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03100240-ILSVRC2012_val_00001201.zst (56/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03100240-ILSVRC2012_val_00001201.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 687,140B, BPFP=1.3042 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 537,944B, BPFP=1.0211 +⌛️ [2/4] FRONTEND: Frontend time: 0.524s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.059s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10258218 75.62840895 + layer.39.0 42.98202138 1411.16107872 + ------------------------------------------------------------------------------------- + TOTAL 21.54230178 743.39474384 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1225084 +BPFP 1.1627 bits/point +EBPFP 1.1627 equivalent bits/point +MSE 743.394744 +---------------------- -------------------------------------------------------- +Time: 1.634s Load: 0.051s, Pack+Encode: 0.524s, Decode+Unpack: 1.059s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 743.3947 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03100240-ILSVRC2012_val_00001201.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03100240-ILSVRC2012_val_00001201.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03109150-ILSVRC2012_val_00000678.zst (57/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03109150-ILSVRC2012_val_00000678.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.072s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 702,484B, BPFP=1.3334 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 705,444B, BPFP=1.3390 +⌛️ [2/4] FRONTEND: Frontend time: 0.582s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.063s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09720685 28.12892333 + layer.39.0 496.21158285 2181.00850340 + ------------------------------------------------------------------------------------- + TOTAL 248.15439485 1104.56871337 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1407928 +BPFP 1.3362 bits/point +EBPFP 1.3362 equivalent bits/point +MSE 1104.568713 +---------------------- -------------------------------------------------------- +Time: 1.717s Load: 0.072s, Pack+Encode: 0.582s, Decode+Unpack: 1.063s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1104.5687 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03109150-ILSVRC2012_val_00000678.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03109150-ILSVRC2012_val_00000678.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03110669-ILSVRC2012_val_00002171.zst (58/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03110669-ILSVRC2012_val_00002171.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 773,680B, BPFP=1.4685 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 770,756B, BPFP=1.4630 +⌛️ [2/4] FRONTEND: Frontend time: 0.526s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.033s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.15128201 432.96519679 + layer.39.0 15.00769387 1442.56948494 + ------------------------------------------------------------------------------------- + TOTAL 7.57948794 937.76734086 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1544436 +BPFP 1.4657 bits/point +EBPFP 1.4657 equivalent bits/point +MSE 937.767341 +---------------------- -------------------------------------------------------- +Time: 1.627s Load: 0.069s, Pack+Encode: 0.526s, Decode+Unpack: 1.033s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 937.7673 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03110669-ILSVRC2012_val_00002171.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03110669-ILSVRC2012_val_00002171.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124043-ILSVRC2012_val_00000766.zst (59/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124043-ILSVRC2012_val_00000766.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 610,248B, BPFP=1.1583 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 658,468B, BPFP=1.2498 +⌛️ [2/4] FRONTEND: Frontend time: 0.550s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.056s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11473456 171.78094631 + layer.39.0 54.83309418 1976.46550049 + ------------------------------------------------------------------------------------- + TOTAL 27.47391437 1074.12322340 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1268716 +BPFP 1.2041 bits/point +EBPFP 1.2041 equivalent bits/point +MSE 1074.123223 +---------------------- -------------------------------------------------------- +Time: 1.655s Load: 0.050s, Pack+Encode: 0.550s, Decode+Unpack: 1.056s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1074.1232 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124043-ILSVRC2012_val_00000766.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03124043-ILSVRC2012_val_00000766.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124170-ILSVRC2012_val_00001875.zst (60/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124170-ILSVRC2012_val_00001875.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 675,600B, BPFP=1.2823 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 544,172B, BPFP=1.0329 +⌛️ [2/4] FRONTEND: Frontend time: 0.550s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.070s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11393612 111.45481050 + layer.39.0 9.06747107 1383.06316812 + ------------------------------------------------------------------------------------- + TOTAL 4.59070360 747.25898931 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1219772 +BPFP 1.1576 bits/point +EBPFP 1.1576 equivalent bits/point +MSE 747.258989 +---------------------- -------------------------------------------------------- +Time: 1.689s Load: 0.070s, Pack+Encode: 0.550s, Decode+Unpack: 1.070s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 747.2590 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03124170-ILSVRC2012_val_00001875.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03124170-ILSVRC2012_val_00001875.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03126707-ILSVRC2012_val_00000020.zst (61/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03126707-ILSVRC2012_val_00000020.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 677,472B, BPFP=1.2859 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 558,012B, BPFP=1.0592 +⌛️ [2/4] FRONTEND: Frontend time: 0.515s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.034s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.15273996 370.17945214 + layer.39.0 1033.15269679 2034.36746842 + ------------------------------------------------------------------------------------- + TOTAL 516.65271838 1202.27346028 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1235484 +BPFP 1.1725 bits/point +EBPFP 1.1725 equivalent bits/point +MSE 1202.273460 +---------------------- -------------------------------------------------------- +Time: 1.599s Load: 0.051s, Pack+Encode: 0.515s, Decode+Unpack: 1.034s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1202.2735 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03126707-ILSVRC2012_val_00000020.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03126707-ILSVRC2012_val_00000020.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03127747-ILSVRC2012_val_00001689.zst (62/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03127747-ILSVRC2012_val_00001689.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 587,760B, BPFP=1.1156 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 620,776B, BPFP=1.1783 +⌛️ [2/4] FRONTEND: Frontend time: 0.586s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.069s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10152024 61.18634597 + layer.39.0 322.92343902 1996.01396987 + ------------------------------------------------------------------------------------- + TOTAL 161.51247963 1028.60015792 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1208536 +BPFP 1.1469 bits/point +EBPFP 1.1469 equivalent bits/point +MSE 1028.600158 +---------------------- -------------------------------------------------------- +Time: 1.706s Load: 0.050s, Pack+Encode: 0.586s, Decode+Unpack: 1.069s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1028.6002 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03127747-ILSVRC2012_val_00001689.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03127747-ILSVRC2012_val_00001689.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03131574-ILSVRC2012_val_00003036.zst (63/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03131574-ILSVRC2012_val_00003036.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 648,280B, BPFP=1.2305 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 674,860B, BPFP=1.2809 +⌛️ [2/4] FRONTEND: Frontend time: 0.584s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.074s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09568423 14.05784970 + layer.39.0 163.24681122 2191.50388727 + ------------------------------------------------------------------------------------- + TOTAL 81.67124773 1102.78086849 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1323140 +BPFP 1.2557 bits/point +EBPFP 1.2557 equivalent bits/point +MSE 1102.780868 +---------------------- -------------------------------------------------------- +Time: 1.728s Load: 0.070s, Pack+Encode: 0.584s, Decode+Unpack: 1.074s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1102.7809 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03131574-ILSVRC2012_val_00003036.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03131574-ILSVRC2012_val_00003036.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03133878-ILSVRC2012_val_00000534.zst (64/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03133878-ILSVRC2012_val_00000534.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 749,948B, BPFP=1.4235 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 713,432B, BPFP=1.3542 +⌛️ [2/4] FRONTEND: Frontend time: 0.617s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.062s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11186348 186.79591837 + layer.39.0 28.46096218 1888.14042760 + ------------------------------------------------------------------------------------- + TOTAL 14.28641283 1037.46817298 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1463380 +BPFP 1.3888 bits/point +EBPFP 1.3888 equivalent bits/point +MSE 1037.468173 +---------------------- -------------------------------------------------------- +Time: 1.749s Load: 0.071s, Pack+Encode: 0.617s, Decode+Unpack: 1.062s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1037.4682 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03133878-ILSVRC2012_val_00000534.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03133878-ILSVRC2012_val_00000534.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03134739-ILSVRC2012_val_00000249.zst (65/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03134739-ILSVRC2012_val_00000249.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 671,940B, BPFP=1.2754 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 751,536B, BPFP=1.4265 +⌛️ [2/4] FRONTEND: Frontend time: 0.546s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.038s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09967384 73.46146896 + layer.39.0 372.24465500 2012.87597182 + ------------------------------------------------------------------------------------- + TOTAL 186.17216442 1043.16872039 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1423476 +BPFP 1.3509 bits/point +EBPFP 1.3509 equivalent bits/point +MSE 1043.168720 +---------------------- -------------------------------------------------------- +Time: 1.635s Load: 0.051s, Pack+Encode: 0.546s, Decode+Unpack: 1.038s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1043.1687 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03134739-ILSVRC2012_val_00000249.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03134739-ILSVRC2012_val_00000249.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03141823-ILSVRC2012_val_00001337.zst (66/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03141823-ILSVRC2012_val_00001337.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 723,556B, BPFP=1.3734 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 768,632B, BPFP=1.4589 +⌛️ [2/4] FRONTEND: Frontend time: 0.569s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.062s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10422104 100.74730473 + layer.39.0 29.45558301 1565.01955782 + ------------------------------------------------------------------------------------- + TOTAL 14.77990203 832.88343127 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1492188 +BPFP 1.4161 bits/point +EBPFP 1.4161 equivalent bits/point +MSE 832.883431 +---------------------- -------------------------------------------------------- +Time: 1.681s Load: 0.051s, Pack+Encode: 0.569s, Decode+Unpack: 1.062s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 832.8834 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03141823-ILSVRC2012_val_00001337.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03141823-ILSVRC2012_val_00001337.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03160309-ILSVRC2012_val_00000330.zst (67/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03160309-ILSVRC2012_val_00000330.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 651,432B, BPFP=1.2365 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 476,668B, BPFP=0.9048 +⌛️ [2/4] FRONTEND: Frontend time: 0.587s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.064s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09980877 63.51002946 + layer.39.0 30.04123011 1237.48809524 + ------------------------------------------------------------------------------------- + TOTAL 15.07051944 650.49906235 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1128100 +BPFP 1.0706 bits/point +EBPFP 1.0706 equivalent bits/point +MSE 650.499062 +---------------------- -------------------------------------------------------- +Time: 1.722s Load: 0.070s, Pack+Encode: 0.587s, Decode+Unpack: 1.064s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 650.4991 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03160309-ILSVRC2012_val_00000330.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03160309-ILSVRC2012_val_00000330.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03187595-ILSVRC2012_val_00000137.zst (68/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03187595-ILSVRC2012_val_00000137.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 677,584B, BPFP=1.2861 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 725,248B, BPFP=1.3766 +⌛️ [2/4] FRONTEND: Frontend time: 0.592s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.084s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10716813 148.80686650 + layer.39.0 12.39187394 1467.89480078 + ------------------------------------------------------------------------------------- + TOTAL 6.24952103 808.35083364 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1402832 +BPFP 1.3313 bits/point +EBPFP 1.3313 equivalent bits/point +MSE 808.350834 +---------------------- -------------------------------------------------------- +Time: 1.746s Load: 0.070s, Pack+Encode: 0.592s, Decode+Unpack: 1.084s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 808.3508 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03187595-ILSVRC2012_val_00000137.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03187595-ILSVRC2012_val_00000137.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03188531-ILSVRC2012_val_00000493.zst (69/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03188531-ILSVRC2012_val_00000493.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 604,652B, BPFP=1.1477 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 662,228B, BPFP=1.2570 +⌛️ [2/4] FRONTEND: Frontend time: 0.590s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.069s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09509044 12.60181305 + layer.39.0 10.77256154 1366.93039359 + ------------------------------------------------------------------------------------- + TOTAL 5.43382599 689.76610332 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1266880 +BPFP 1.2023 bits/point +EBPFP 1.2023 equivalent bits/point +MSE 689.766103 +---------------------- -------------------------------------------------------- +Time: 1.729s Load: 0.070s, Pack+Encode: 0.590s, Decode+Unpack: 1.069s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 689.7661 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03188531-ILSVRC2012_val_00000493.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03188531-ILSVRC2012_val_00000493.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03196217-ILSVRC2012_val_00003643.zst (70/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03196217-ILSVRC2012_val_00003643.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 621,996B, BPFP=1.1806 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 664,232B, BPFP=1.2608 +⌛️ [2/4] FRONTEND: Frontend time: 0.601s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.043s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09478207 12.60817826 + layer.39.0 65.57403274 2007.81802721 + ------------------------------------------------------------------------------------- + TOTAL 32.83440740 1010.21310274 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1286228 +BPFP 1.2207 bits/point +EBPFP 1.2207 equivalent bits/point +MSE 1010.213103 +---------------------- -------------------------------------------------------- +Time: 1.714s Load: 0.070s, Pack+Encode: 0.601s, Decode+Unpack: 1.043s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1010.2131 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03196217-ILSVRC2012_val_00003643.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03196217-ILSVRC2012_val_00003643.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03201208-ILSVRC2012_val_00000241.zst (71/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03201208-ILSVRC2012_val_00000241.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 715,228B, BPFP=1.3576 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 626,520B, BPFP=1.1892 +⌛️ [2/4] FRONTEND: Frontend time: 0.526s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.039s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10331685 65.29380770 + layer.39.0 136.59314261 1175.94533528 + ------------------------------------------------------------------------------------- + TOTAL 68.34822973 620.61957149 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1341748 +BPFP 1.2734 bits/point +EBPFP 1.2734 equivalent bits/point +MSE 620.619571 +---------------------- -------------------------------------------------------- +Time: 1.615s Load: 0.050s, Pack+Encode: 0.526s, Decode+Unpack: 1.039s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 620.6196 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03201208-ILSVRC2012_val_00000241.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03201208-ILSVRC2012_val_00000241.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03207743-ILSVRC2012_val_00000256.zst (72/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03207743-ILSVRC2012_val_00000256.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 751,412B, BPFP=1.4262 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 603,384B, BPFP=1.1453 +⌛️ [2/4] FRONTEND: Frontend time: 0.523s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.045s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09674843 326.33630952 + layer.39.0 189.63590258 1900.88107386 + ------------------------------------------------------------------------------------- + TOTAL 94.86632550 1113.60869169 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1354796 +BPFP 1.2858 bits/point +EBPFP 1.2858 equivalent bits/point +MSE 1113.608692 +---------------------- -------------------------------------------------------- +Time: 1.639s Load: 0.070s, Pack+Encode: 0.523s, Decode+Unpack: 1.045s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1113.6087 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03207743-ILSVRC2012_val_00000256.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03207743-ILSVRC2012_val_00000256.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03216828-ILSVRC2012_val_00001729.zst (73/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03216828-ILSVRC2012_val_00001729.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 692,848B, BPFP=1.3151 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 618,304B, BPFP=1.1736 +⌛️ [2/4] FRONTEND: Frontend time: 0.595s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.074s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10800209 250.61610180 + layer.39.0 31.30713223 1244.26554908 + ------------------------------------------------------------------------------------- + TOTAL 15.70756716 747.44082544 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1311152 +BPFP 1.2443 bits/point +EBPFP 1.2443 equivalent bits/point +MSE 747.440825 +---------------------- -------------------------------------------------------- +Time: 1.738s Load: 0.070s, Pack+Encode: 0.595s, Decode+Unpack: 1.074s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 747.4408 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03216828-ILSVRC2012_val_00001729.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03216828-ILSVRC2012_val_00001729.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03218198-ILSVRC2012_val_00002266.zst (74/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03218198-ILSVRC2012_val_00002266.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 756,596B, BPFP=1.4361 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 742,860B, BPFP=1.4100 +⌛️ [2/4] FRONTEND: Frontend time: 0.549s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.075s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11617067 345.22852891 + layer.39.0 195.83184524 1750.22862002 + ------------------------------------------------------------------------------------- + TOTAL 97.97400795 1047.72857447 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1499456 +BPFP 1.4230 bits/point +EBPFP 1.4230 equivalent bits/point +MSE 1047.728574 +---------------------- -------------------------------------------------------- +Time: 1.675s Load: 0.051s, Pack+Encode: 0.549s, Decode+Unpack: 1.075s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1047.7286 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03218198-ILSVRC2012_val_00002266.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03218198-ILSVRC2012_val_00002266.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03220513-ILSVRC2012_val_00001868.zst (75/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03220513-ILSVRC2012_val_00001868.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 858,228B, BPFP=1.6290 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 699,440B, BPFP=1.3276 +⌛️ [2/4] FRONTEND: Frontend time: 0.560s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.050s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.20032125 991.54689018 + layer.39.0 377.00176142 2412.22108844 + ------------------------------------------------------------------------------------- + TOTAL 188.60104134 1701.88398931 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1557668 +BPFP 1.4783 bits/point +EBPFP 1.4783 equivalent bits/point +MSE 1701.883989 +---------------------- -------------------------------------------------------- +Time: 1.679s Load: 0.069s, Pack+Encode: 0.560s, Decode+Unpack: 1.050s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1701.8840 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03220513-ILSVRC2012_val_00001868.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03220513-ILSVRC2012_val_00001868.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03223299-ILSVRC2012_val_00001893.zst (76/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03223299-ILSVRC2012_val_00001893.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.068s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 607,716B, BPFP=1.1535 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 579,004B, BPFP=1.0990 +⌛️ [2/4] FRONTEND: Frontend time: 0.554s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.061s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10735053 74.61077958 + layer.39.0 354.51621720 1432.04543246 + ------------------------------------------------------------------------------------- + TOTAL 177.31178386 753.32810602 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1186720 +BPFP 1.1262 bits/point +EBPFP 1.1262 equivalent bits/point +MSE 753.328106 +---------------------- -------------------------------------------------------- +Time: 1.682s Load: 0.068s, Pack+Encode: 0.554s, Decode+Unpack: 1.061s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 753.3281 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03223299-ILSVRC2012_val_00001893.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03223299-ILSVRC2012_val_00001893.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03240683-ILSVRC2012_val_00000504.zst (77/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03240683-ILSVRC2012_val_00000504.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 632,168B, BPFP=1.1999 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 654,740B, BPFP=1.2427 +⌛️ [2/4] FRONTEND: Frontend time: 0.556s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.087s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10065408 133.80193149 + layer.39.0 443.53838678 1961.91205053 + ------------------------------------------------------------------------------------- + TOTAL 221.81952043 1047.85699101 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1286908 +BPFP 1.2213 bits/point +EBPFP 1.2213 equivalent bits/point +MSE 1047.856991 +---------------------- -------------------------------------------------------- +Time: 1.715s Load: 0.071s, Pack+Encode: 0.556s, Decode+Unpack: 1.087s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1047.8570 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03240683-ILSVRC2012_val_00000504.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03240683-ILSVRC2012_val_00000504.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03250847-ILSVRC2012_val_00000542.zst (78/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03250847-ILSVRC2012_val_00000542.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 717,108B, BPFP=1.3611 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 740,592B, BPFP=1.4057 +⌛️ [2/4] FRONTEND: Frontend time: 0.526s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.046s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10136319 50.26281204 + layer.39.0 140.24735787 2349.53644315 + ------------------------------------------------------------------------------------- + TOTAL 70.17436053 1199.89962760 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1457700 +BPFP 1.3834 bits/point +EBPFP 1.3834 equivalent bits/point +MSE 1199.899628 +---------------------- -------------------------------------------------------- +Time: 1.642s Load: 0.070s, Pack+Encode: 0.526s, Decode+Unpack: 1.046s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1199.8996 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03250847-ILSVRC2012_val_00000542.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03250847-ILSVRC2012_val_00000542.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03255030-ILSVRC2012_val_00001045.zst (79/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03255030-ILSVRC2012_val_00001045.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 661,084B, BPFP=1.2548 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 627,396B, BPFP=1.1908 +⌛️ [2/4] FRONTEND: Frontend time: 0.608s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.054s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10050351 61.54246386 + layer.39.0 12.06722622 1264.56304665 + ------------------------------------------------------------------------------------- + TOTAL 6.08386487 663.05275525 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1288480 +BPFP 1.2228 bits/point +EBPFP 1.2228 equivalent bits/point +MSE 663.052755 +---------------------- -------------------------------------------------------- +Time: 1.731s Load: 0.069s, Pack+Encode: 0.608s, Decode+Unpack: 1.054s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 663.0528 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03255030-ILSVRC2012_val_00001045.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03255030-ILSVRC2012_val_00001045.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03271574-ILSVRC2012_val_00000942.zst (80/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03271574-ILSVRC2012_val_00000942.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 654,044B, BPFP=1.2414 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 630,628B, BPFP=1.1970 +⌛️ [2/4] FRONTEND: Frontend time: 0.525s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.050s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10164264 63.18028729 + layer.39.0 660.63544704 2400.57385811 + ------------------------------------------------------------------------------------- + TOTAL 330.36854484 1231.87707270 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1284672 +BPFP 1.2192 bits/point +EBPFP 1.2192 equivalent bits/point +MSE 1231.877073 +---------------------- -------------------------------------------------------- +Time: 1.625s Load: 0.051s, Pack+Encode: 0.525s, Decode+Unpack: 1.050s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1231.8771 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03271574-ILSVRC2012_val_00000942.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03271574-ILSVRC2012_val_00000942.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272010-ILSVRC2012_val_00000374.zst (81/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272010-ILSVRC2012_val_00000374.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 658,476B, BPFP=1.2498 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 718,948B, BPFP=1.3646 +⌛️ [2/4] FRONTEND: Frontend time: 0.531s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.028s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10420663 111.46972182 + layer.39.0 9.63653369 1235.98931001 + ------------------------------------------------------------------------------------- + TOTAL 4.87037016 673.72951591 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1377424 +BPFP 1.3072 bits/point +EBPFP 1.3072 equivalent bits/point +MSE 673.729516 +---------------------- -------------------------------------------------------- +Time: 1.610s Load: 0.051s, Pack+Encode: 0.531s, Decode+Unpack: 1.028s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 673.7295 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272010-ILSVRC2012_val_00000374.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03272010-ILSVRC2012_val_00000374.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272562-ILSVRC2012_val_00001699.zst (82/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272562-ILSVRC2012_val_00001699.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.068s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 717,352B, BPFP=1.3616 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 576,592B, BPFP=1.0944 +⌛️ [2/4] FRONTEND: Frontend time: 0.517s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.042s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11399285 236.59537476 + layer.39.0 12.79457642 1590.02818270 + ------------------------------------------------------------------------------------- + TOTAL 6.45428464 913.31177873 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1293944 +BPFP 1.2280 bits/point +EBPFP 1.2280 equivalent bits/point +MSE 913.311779 +---------------------- -------------------------------------------------------- +Time: 1.627s Load: 0.068s, Pack+Encode: 0.517s, Decode+Unpack: 1.042s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 913.3118 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03272562-ILSVRC2012_val_00001699.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03272562-ILSVRC2012_val_00001699.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03290653-ILSVRC2012_val_00000199.zst (83/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03290653-ILSVRC2012_val_00000199.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 621,316B, BPFP=1.1793 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 670,548B, BPFP=1.2728 +⌛️ [2/4] FRONTEND: Frontend time: 0.524s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.073s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09581849 36.87479880 + layer.39.0 9.30266794 1191.60532070 + ------------------------------------------------------------------------------------- + TOTAL 4.69924322 614.24005975 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1291864 +BPFP 1.2260 bits/point +EBPFP 1.2260 equivalent bits/point +MSE 614.240060 +---------------------- -------------------------------------------------------- +Time: 1.667s Load: 0.070s, Pack+Encode: 0.524s, Decode+Unpack: 1.073s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 614.2401 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03290653-ILSVRC2012_val_00000199.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03290653-ILSVRC2012_val_00000199.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03291819-ILSVRC2012_val_00000419.zst (84/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03291819-ILSVRC2012_val_00000419.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 609,764B, BPFP=1.1574 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 565,452B, BPFP=1.0733 +⌛️ [2/4] FRONTEND: Frontend time: 0.595s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.046s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10621172 73.62982872 + layer.39.0 31.36357166 1260.33053936 + ------------------------------------------------------------------------------------- + TOTAL 15.73489169 666.98018404 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1175216 +BPFP 1.1153 bits/point +EBPFP 1.1153 equivalent bits/point +MSE 666.980184 +---------------------- -------------------------------------------------------- +Time: 1.693s Load: 0.051s, Pack+Encode: 0.595s, Decode+Unpack: 1.046s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 666.9802 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03291819-ILSVRC2012_val_00000419.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03291819-ILSVRC2012_val_00000419.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03314780-ILSVRC2012_val_00000624.zst (85/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03314780-ILSVRC2012_val_00000624.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 680,080B, BPFP=1.2908 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 746,404B, BPFP=1.4167 +⌛️ [2/4] FRONTEND: Frontend time: 0.596s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.089s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10172509 50.77206329 + layer.39.0 35.60390853 1910.43561710 + ------------------------------------------------------------------------------------- + TOTAL 17.85281681 980.60384020 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1426484 +BPFP 1.3538 bits/point +EBPFP 1.3538 equivalent bits/point +MSE 980.603840 +---------------------- -------------------------------------------------------- +Time: 1.755s Load: 0.070s, Pack+Encode: 0.596s, Decode+Unpack: 1.089s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 980.6038 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03314780-ILSVRC2012_val_00000624.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03314780-ILSVRC2012_val_00000624.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03325584-ILSVRC2012_val_00001256.zst (86/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03325584-ILSVRC2012_val_00001256.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 744,904B, BPFP=1.4139 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 742,756B, BPFP=1.4098 +⌛️ [2/4] FRONTEND: Frontend time: 0.588s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.070s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.11348933 162.91584670 + layer.39.0 26.85401292 1557.03401361 + ------------------------------------------------------------------------------------- + TOTAL 13.48375113 859.97493015 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1487660 +BPFP 1.4118 bits/point +EBPFP 1.4118 equivalent bits/point +MSE 859.974930 +---------------------- -------------------------------------------------------- +Time: 1.728s Load: 0.070s, Pack+Encode: 0.588s, Decode+Unpack: 1.070s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 859.9749 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03325584-ILSVRC2012_val_00001256.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03325584-ILSVRC2012_val_00001256.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03337140-ILSVRC2012_val_00000132.zst (87/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03337140-ILSVRC2012_val_00000132.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 600,816B, BPFP=1.1404 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 615,900B, BPFP=1.1690 +⌛️ [2/4] FRONTEND: Frontend time: 0.561s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.062s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09852950 12.93792897 + layer.39.0 10.39905343 1227.52210884 + ------------------------------------------------------------------------------------- + TOTAL 5.24879146 620.23001890 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1216716 +BPFP 1.1547 bits/point +EBPFP 1.1547 equivalent bits/point +MSE 620.230019 +---------------------- -------------------------------------------------------- +Time: 1.673s Load: 0.050s, Pack+Encode: 0.561s, Decode+Unpack: 1.062s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 620.2300 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03337140-ILSVRC2012_val_00000132.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03337140-ILSVRC2012_val_00000132.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03344393-ILSVRC2012_val_00000288.zst (88/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03344393-ILSVRC2012_val_00000288.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 630,180B, BPFP=1.1961 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 623,356B, BPFP=1.1832 +⌛️ [2/4] FRONTEND: Frontend time: 0.590s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.067s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09830858 73.45055879 + layer.39.0 109.00505649 1780.67638484 + ------------------------------------------------------------------------------------- + TOTAL 54.55168253 927.06347182 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1253536 +BPFP 1.1897 bits/point +EBPFP 1.1897 equivalent bits/point +MSE 927.063472 +---------------------- -------------------------------------------------------- +Time: 1.727s Load: 0.070s, Pack+Encode: 0.590s, Decode+Unpack: 1.067s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 927.0635 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03344393-ILSVRC2012_val_00000288.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03344393-ILSVRC2012_val_00000288.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03345487-ILSVRC2012_val_00000764.zst (89/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03345487-ILSVRC2012_val_00000764.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 691,248B, BPFP=1.3120 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 666,744B, BPFP=1.2655 +⌛️ [2/4] FRONTEND: Frontend time: 0.537s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.046s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10639974 76.87604015 + layer.39.0 14.55993569 1688.58236152 + ------------------------------------------------------------------------------------- + TOTAL 7.33316771 882.72920083 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1357992 +BPFP 1.2888 bits/point +EBPFP 1.2888 equivalent bits/point +MSE 882.729201 +---------------------- -------------------------------------------------------- +Time: 1.634s Load: 0.050s, Pack+Encode: 0.537s, Decode+Unpack: 1.046s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 882.7292 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03345487-ILSVRC2012_val_00000764.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03345487-ILSVRC2012_val_00000764.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03347037-ILSVRC2012_val_00000743.zst (90/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03347037-ILSVRC2012_val_00000743.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.069s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 758,236B, BPFP=1.4392 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 702,744B, BPFP=1.3339 +⌛️ [2/4] FRONTEND: Frontend time: 0.582s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.046s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.14351733 447.65867954 + layer.39.0 355.98426871 1841.14188533 + ------------------------------------------------------------------------------------- + TOTAL 178.06389302 1144.40028243 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1460980 +BPFP 1.3865 bits/point +EBPFP 1.3865 equivalent bits/point +MSE 1144.400282 +---------------------- -------------------------------------------------------- +Time: 1.696s Load: 0.069s, Pack+Encode: 0.582s, Decode+Unpack: 1.046s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1144.4003 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03347037-ILSVRC2012_val_00000743.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03347037-ILSVRC2012_val_00000743.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03355925-ILSVRC2012_val_00000445.zst (91/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03355925-ILSVRC2012_val_00000445.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 608,664B, BPFP=1.1553 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 537,252B, BPFP=1.0197 +⌛️ [2/4] FRONTEND: Frontend time: 0.558s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.058s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09979894 86.70799168 + layer.39.0 9.06502540 1428.33175413 + ------------------------------------------------------------------------------------- + TOTAL 4.58241217 757.51987290 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1145916 +BPFP 1.0875 bits/point +EBPFP 1.0875 equivalent bits/point +MSE 757.519873 +---------------------- -------------------------------------------------------- +Time: 1.686s Load: 0.071s, Pack+Encode: 0.558s, Decode+Unpack: 1.058s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 757.5199 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03355925-ILSVRC2012_val_00000445.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03355925-ILSVRC2012_val_00000445.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03376595-ILSVRC2012_val_00001616.zst (92/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03376595-ILSVRC2012_val_00001616.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 6.816s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 721,160B, BPFP=1.3688 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 744,928B, BPFP=1.4139 +⌛️ [2/4] FRONTEND: Frontend time: 0.528s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.060s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09988844 77.06294035 + layer.39.0 1408.20760447 2899.57677357 + ------------------------------------------------------------------------------------- + TOTAL 704.15374646 1488.31985696 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1466088 +BPFP 1.3914 bits/point +EBPFP 1.3914 equivalent bits/point +MSE 1488.319857 +---------------------- -------------------------------------------------------- +Time: 8.404s Load: 6.816s, Pack+Encode: 0.528s, Decode+Unpack: 1.060s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1488.3199 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03376595-ILSVRC2012_val_00001616.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03376595-ILSVRC2012_val_00001616.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03379051-ILSVRC2012_val_00002562.zst (93/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03379051-ILSVRC2012_val_00002562.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 723,148B, BPFP=1.3726 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 721,108B, BPFP=1.3687 +⌛️ [2/4] FRONTEND: Frontend time: 0.528s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.039s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10889592 185.23785228 + layer.39.0 102.95462828 2241.57677357 + ------------------------------------------------------------------------------------- + TOTAL 51.53176210 1213.40731293 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1444256 +BPFP 1.3707 bits/point +EBPFP 1.3707 equivalent bits/point +MSE 1213.407313 +---------------------- -------------------------------------------------------- +Time: 1.618s Load: 0.050s, Pack+Encode: 0.528s, Decode+Unpack: 1.039s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1213.4073 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03379051-ILSVRC2012_val_00002562.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03379051-ILSVRC2012_val_00002562.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388043-ILSVRC2012_val_00001018.zst (94/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388043-ILSVRC2012_val_00001018.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.071s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 671,500B, BPFP=1.2746 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 609,656B, BPFP=1.1572 +⌛️ [2/4] FRONTEND: Frontend time: 0.571s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.075s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09747427 63.41838633 + layer.39.0 21.12933142 1315.50340136 + ------------------------------------------------------------------------------------- + TOTAL 10.61340285 689.46089384 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1281156 +BPFP 1.2159 bits/point +EBPFP 1.2159 equivalent bits/point +MSE 689.460894 +---------------------- -------------------------------------------------------- +Time: 1.716s Load: 0.071s, Pack+Encode: 0.571s, Decode+Unpack: 1.075s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 689.4609 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388043-ILSVRC2012_val_00001018.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03388043-ILSVRC2012_val_00001018.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388183-ILSVRC2012_val_00002799.zst (95/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388183-ILSVRC2012_val_00002799.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 733,852B, BPFP=1.3929 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 709,700B, BPFP=1.3471 +⌛️ [2/4] FRONTEND: Frontend time: 0.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.074s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10066175 65.54595633 + layer.39.0 786.68810739 2523.33430515 + ------------------------------------------------------------------------------------- + TOTAL 393.39438457 1294.44013074 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1443552 +BPFP 1.3700 bits/point +EBPFP 1.3700 equivalent bits/point +MSE 1294.440131 +---------------------- -------------------------------------------------------- +Time: 1.725s Load: 0.070s, Pack+Encode: 0.581s, Decode+Unpack: 1.074s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1294.4401 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388183-ILSVRC2012_val_00002799.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03388183-ILSVRC2012_val_00002799.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388549-ILSVRC2012_val_00002945.zst (96/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388549-ILSVRC2012_val_00002945.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.050s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 683,888B, BPFP=1.2981 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 645,360B, BPFP=1.2249 +⌛️ [2/4] FRONTEND: Frontend time: 0.524s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.048s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09849939 38.72301309 + layer.39.0 10.79426799 1384.89212828 + ------------------------------------------------------------------------------------- + TOTAL 5.44638369 711.80757068 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1329248 +BPFP 1.2615 bits/point +EBPFP 1.2615 equivalent bits/point +MSE 711.807571 +---------------------- -------------------------------------------------------- +Time: 1.622s Load: 0.050s, Pack+Encode: 0.524s, Decode+Unpack: 1.048s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 711.8076 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03388549-ILSVRC2012_val_00002945.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03388549-ILSVRC2012_val_00002945.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03393912-ILSVRC2012_val_00000047.zst (97/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03393912-ILSVRC2012_val_00000047.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 676,220B, BPFP=1.2835 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 620,060B, BPFP=1.1769 +⌛️ [2/4] FRONTEND: Frontend time: 0.543s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.041s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.09729456 111.49504221 + layer.39.0 38.26720800 1904.53061224 + ------------------------------------------------------------------------------------- + TOTAL 19.18225128 1008.01282723 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1296280 +BPFP 1.2302 bits/point +EBPFP 1.2302 equivalent bits/point +MSE 1008.012827 +---------------------- -------------------------------------------------------- +Time: 1.654s Load: 0.070s, Pack+Encode: 0.543s, Decode+Unpack: 1.041s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1008.0128 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03393912-ILSVRC2012_val_00000047.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03393912-ILSVRC2012_val_00000047.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03394916-ILSVRC2012_val_00000957.zst (98/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03394916-ILSVRC2012_val_00000957.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 657,984B, BPFP=1.2489 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 669,652B, BPFP=1.2711 +⌛️ [2/4] FRONTEND: Frontend time: 0.581s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.047s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10421823 39.15237792 + layer.39.0 9.72561820 1299.70396016 + ------------------------------------------------------------------------------------- + TOTAL 4.91491822 669.42816904 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1327636 +BPFP 1.2600 bits/point +EBPFP 1.2600 equivalent bits/point +MSE 669.428169 +---------------------- -------------------------------------------------------- +Time: 1.698s Load: 0.070s, Pack+Encode: 0.581s, Decode+Unpack: 1.047s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 669.4282 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03394916-ILSVRC2012_val_00000957.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03394916-ILSVRC2012_val_00000957.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03404251-ILSVRC2012_val_00000641.zst (99/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03404251-ILSVRC2012_val_00000641.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.070s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 674,292B, BPFP=1.2799 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 722,312B, BPFP=1.3710 +⌛️ [2/4] FRONTEND: Frontend time: 0.543s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.043s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10764784 63.38368562 + layer.39.0 585.45553936 2204.11418853 + ------------------------------------------------------------------------------------- + TOTAL 292.78159360 1133.74893707 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1396604 +BPFP 1.3254 bits/point +EBPFP 1.3254 equivalent bits/point +MSE 1133.748937 +---------------------- -------------------------------------------------------- +Time: 1.656s Load: 0.070s, Pack+Encode: 0.543s, Decode+Unpack: 1.043s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1133.7489 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03404251-ILSVRC2012_val_00000641.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03404251-ILSVRC2012_val_00000641.zst + + 💪 Processing: ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03417042-ILSVRC2012_val_00001144.zst (100/100) + +[1/4] FRONTEND: Loading features from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03417042-ILSVRC2012_val_00001144.zst... + +Original data structure: +root: [dict] with 2 keys + key['backbone.blocks.39']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.9']: [list] with 1 items + item[0]: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + +Parsed features: +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ +⌛️ [1/4] FRONTEND: Load time: 0.051s + +------------------------------------------------------------ +DINOv3 Features Summary +------------------------------------------------------------ +Number of layers: 2 +Layer names: ['layer.9', 'layer.39'] +Data type: torch.float32 + layer.9.0: torch.Size([1029, 4096]), torch.float32 + layer.39.0: torch.Size([1029, 4096]), torch.float32 +------------------------------------------------------------ + +[2/4] FRONTEND: Pack + Encode (strategy: individual)... + IndividualPacker: + layer.9.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.9.0 + layer.39.0: torch.Size([1029, 4096]) -> torch.Size([1, 1, 1029, 4096]) + From layer.39.0 + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + layer.9.0: 676,780B, BPFP=1.2846 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + layer.39.0: 638,280B, BPFP=1.2115 +⌛️ [2/4] FRONTEND: Frontend time: 0.527s (Pack+Encode) + +[3/4] BACKEND: Decode + Unpack... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) +⌛️ [3/4] BACKEND: Backend time: 1.028s + +[4/4] METRICS: Computing MSE Breakdown... + Using per-key quantization points (layer.9: torch.Size([256])) for layer.9.0 + Using per-key quantization points (layer.39: torch.Size([256])) for layer.39.0 + IndividualUnPacker: + layer.9.0: torch.Size([1, 1029, 4096]) + layer.39.0: torch.Size([1, 1029, 4096]) + Per-key MSE Breakdown (Quant=Truncation+Quantization, Codec=Compression): + Key Quant-MSE Total-MSE + ------------------------------------------------------------------------------------- + layer.9.0 0.10091509 123.61286747 + layer.39.0 202.93364310 2078.99732750 + ------------------------------------------------------------------------------------- + TOTAL 101.51727910 1101.30509749 + (elements=8,429,568) +---------------------- -------------------------------------------------------- +SAMPLE-WISE STATISTICS +---------------------- -------------------------------------------------------- +Handler dinov3-total +Strategy individual +Architecture hyperprior-featurecoding +---------------------- -------------------------------------------------------- +Total Elements 8429568 +Total Bytes 1315060 +BPFP 1.2480 bits/point +EBPFP 1.2480 equivalent bits/point +MSE 1101.305097 +---------------------- -------------------------------------------------------- +Time: 1.606s Load: 0.051s, Pack+Encode: 0.527s, Decode+Unpack: 1.028s +---------------------- -------------------------------------------------------- +Restored Feature Format: [dict] with 6 keys + key['backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['encoder.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.9']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu + key['segmentation_model.0.backbone.blocks.39']: [Tensor] shape=torch.Size([1, 1029, 4096]), dtype=torch.float32, device=cpu +💾 Converting with 1101.3051 MSE: + from ../datasets/FeatureCoding-DINOv3/imagenet1k-val/n03417042-ILSVRC2012_val_00001144.zst + to output-fixed/dinov3-total/lambda0.02/hyperprior-featurecoding-8bit-layerwise/cls_in1kval/n03417042-ILSVRC2012_val_00001144.zst +------------------------ ---------------------------- +TOTAL PROCESSING SUMMARY +------------------------ ---------------------------- +Total files 100 +Avg BPFP 1.2689 bits/point +Avg EBPFP 1.2689 equivalent bits/point +Avg MSE 963.229714 +Avg Time 1.749s +------------------------ ----------------------------